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2404.12091

Wu Ran

Wu Ran, Peirong Ma, Zhiquan He, Hao Ren, Hong Lu

Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains

21 pages, 14 figures

International Conference on Learning Representations 2024

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to suboptimal results. To overcome this limitation, we focus on addressing various rainy images by delving into meaningful representations that encapsulate both the rain and background components. Leveraging these representations as instructive guidance, we put forth a Context-based Instance-level Modulation (CoI-M) mechanism adept at efficiently modulating CNN- or Transformer-based models. Furthermore, we devise a rain-/detail-aware contrastive learning strategy to help extract joint rain-/detail-aware representations. By integrating CoI-M with the rain-/detail-aware Contrastive learning, we develop CoIC, an innovative and potent algorithm tailored for training models on mixed datasets. Moreover, CoIC offers insight into modeling relationships of datasets, quantitatively assessing the impact of rain and details on restoration, and unveiling distinct behaviors of models given diverse inputs. Extensive experiments validate the efficacy of CoIC in boosting the deraining ability of CNN and Transformer models. CoIC also enhances the deraining prowess remarkably when real-world dataset is included.

2024-04-19

2404.12143

Hilde Weerts

Hilde Weerts, Rapha\"ele Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy

The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action

2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24)

10.1145/3630106.3659025

cs.AI cs.CY

http://creativecommons.org/licenses/by/4.0/

Various metrics and interventions have been developed to identify and mitigate unfair outputs of machine learning systems. While individuals and organizations have an obligation to avoid discrimination, the use of fairness-aware machine learning interventions has also been described as amounting to 'algorithmic positive action' under European Union (EU) non-discrimination law. As the Court of Justice of the European Union has been strict when it comes to assessing the lawfulness of positive action, this would impose a significant legal burden on those wishing to implement fair-ml interventions. In this paper, we propose that algorithmic fairness interventions often should be interpreted as a means to prevent discrimination, rather than a measure of positive action. Specifically, we suggest that this category mistake can often be attributed to neutrality fallacies: faulty assumptions regarding the neutrality of fairness-aware algorithmic decision-making. Our findings raise the question of whether a negative obligation to refrain from discrimination is sufficient in the context of algorithmic decision-making. Consequently, we suggest moving away from a duty to 'not do harm' towards a positive obligation to actively 'do no harm' as a more adequate framework for algorithmic decision-making and fair ml-interventions.

2024-04-19

2404.12240

Lukas Rottkamp

Lukas Rottkamp, Matthias Schubert

A Time-Inhomogeneous Markov Model for Resource Availability under Sparse Observations

11 pages, long version of a paper published at 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2018)

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 460-463) 2018

10.1145/3274895.3274945

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Accurate spatio-temporal information about the current situation is crucial for smart city applications such as modern routing algorithms. Often, this information describes the state of stationary resources, e.g. the availability of parking bays, charging stations or the amount of people waiting for a vehicle to pick them up near a given location. To exploit this kind of information, predicting future states of the monitored resources is often mandatory because a resource might change its state within the time until it is needed. To train an accurate predictive model, it is often not possible to obtain a continuous time series on the state of the resource. For example, the information might be collected from traveling agents visiting the resource with an irregular frequency. Thus, it is necessary to develop methods which work on sparse observations for training and prediction. In this paper, we propose time-inhomogeneous discrete Markov models to allow accurate prediction even when the frequency of observation is very rare. Our new model is able to blend recent observations with historic data and also provide useful probabilistic estimates for future states. Since resources availability in a city is typically time-dependent, our Markov model is time-inhomogeneous and cyclic within a predefined time interval. To train our model, we propose a modified Baum-Welch algorithm. Evaluations on real-world datasets of parking bay availability show that our new method indeed yields good results compared to methods being trained on complete data and non-cyclic variants.

2024-04-19

2404.12292

Niklas Penzel

Niklas Penzel, Gideon Stein, Joachim Denzler

Reducing Bias in Pre-trained Models by Tuning while Penalizing Change

12 pages, 12 figures, presented at VISAPP 2024

Proceedings of the 19th International Joint Conference on Computer Vision (2024), Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, SciTePress, pages 90-101

10.5220/0012345800003660

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

Deep models trained on large amounts of data often incorporate implicit biases present during training time. If later such a bias is discovered during inference or deployment, it is often necessary to acquire new data and retrain the model. This behavior is especially problematic in critical areas such as autonomous driving or medical decision-making. In these scenarios, new data is often expensive and hard to come by. In this work, we present a method based on change penalization that takes a pre-trained model and adapts the weights to mitigate a previously detected bias. We achieve this by tuning a zero-initialized copy of a frozen pre-trained network. Our method needs very few, in extreme cases only a single, examples that contradict the bias to increase performance. Additionally, we propose an early stopping criterion to modify baselines and reduce overfitting. We evaluate our approach on a well-known bias in skin lesion classification and three other datasets from the domain shift literature. We find that our approach works especially well with very few images. Simple fine-tuning combined with our early stopping also leads to performance benefits for a larger number of tuning samples.

2024-04-19

2404.12295

Niklas Penzel

Tristan Piater, Niklas Penzel, Gideon Stein, Joachim Denzler

When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out

10 pages, 2 figures, 5 tables, presented at VISAPP 2024

Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP (2024), ISBN 978-989-758-679-8, ISSN 2184-4321, SciTePress, pages 149-158

10.5220/0012382600003660

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

A substantial body of research has focused on developing systems that assist medical professionals during labor-intensive early screening processes, many based on convolutional deep-learning architectures. Recently, multiple studies explored the application of so-called self-attention mechanisms in the vision domain. These studies often report empirical improvements over fully convolutional approaches on various datasets and tasks. To evaluate this trend for medical imaging, we extend two widely adopted convolutional architectures with different self-attention variants on two different medical datasets. With this, we aim to specifically evaluate the possible advantages of additional self-attention. We compare our models with similarly sized convolutional and attention-based baselines and evaluate performance gains statistically. Additionally, we investigate how including such layers changes the features learned by these models during the training. Following a hyperparameter search, and contrary to our expectations, we observe no significant improvement in balanced accuracy over fully convolutional models. We also find that important features, such as dermoscopic structures in skin lesion images, are still not learned by employing self-attention. Finally, analyzing local explanations, we confirm biased feature usage. We conclude that merely incorporating attention is insufficient to surpass the performance of existing fully convolutional methods.

2024-04-19

2404.12341

Yinzhu Jin

Yinzhu Jin, Matthew B. Dwyer, P. Thomas Fletcher

Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold

Accepted and pulished in International Symposium on Biomedical Imaging (ISBI) 2024: https://ieeexplore.ieee.org/document/10635874

in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 2024, pp. 1-5

10.1109/ISBI56570.2024.10635874

cs.LG cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

This paper introduces a new technique to measure the feature dependency of neural network models. The motivation is to better understand a model by querying whether it is using information from human-understandable features, e.g., anatomical shape, volume, or image texture. Our method is based on the principle that if a model is dependent on a feature, then removal of that feature should significantly harm its performance. A targeted feature is "removed" by collapsing the dimension in the data distribution that corresponds to that feature. We perform this by moving data points along the feature dimension to a baseline feature value while staying on the data manifold, as estimated by a deep generative model. Then we observe how the model's performance changes on the modified test data set, with the target feature dimension removed. We test our method on deep neural network models trained on synthetic image data with known ground truth, an Alzheimer's disease prediction task using MRI and hippocampus segmentations from the OASIS-3 dataset, and a cell nuclei classification task using the Lizard dataset.

2024-10-10

2404.12361

Trevor Chan

Trevor J. Chan, Chamith S. Rajapakse

Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

10.1109/ISBI56570.2024.10635304.

cs.AI physics.med-ph

http://creativecommons.org/licenses/by/4.0/

Deep learning methods for accelerated MRI achieve state-of-the-art results but largely ignore additional speedups possible with noncartesian sampling trajectories. To address this gap, we created a generative diffusion model-based reconstruction algorithm for multi-coil highly undersampled spiral MRI. This model uses conditioning during training as well as frequency-based guidance to ensure consistency between images and measurements. Evaluated on retrospective data, we show high quality (structural similarity > 0.87) in reconstructed images with ultrafast scan times (0.02 seconds for a 2D image). We use this algorithm to identify a set of optimal variable-density spiral trajectories and show large improvements in image quality compared to conventional reconstruction using the non-uniform fast Fourier transform. By combining efficient spiral sampling trajectories, multicoil imaging, and deep learning reconstruction, these methods could enable the extremely high acceleration factors needed for real-time 3D imaging.

2024-10-02

2404.12415

Loganathan Girija Divyanth

Shubhadip Dasgupta, Satwik Pate, Divya Rathore, L.G. Divyanth, Ayan Das, Anshuman Nayak, Subhadip Dey, Asim Biswas, David C. Weindorf, Bin Li, Sergio Henrique Godinho Silva, Bruno Teixeira Ribeiro, Sanjay Srivastava, Somsubhra Chakraborty

Prediction of soil fertility parameters using USB-microscope imagery and portable X-ray fluorescence spectrometry

Published in 'Soil Advances'

Soil Advances, Volume 2, 2024, 100016

10.1016/j.soilad.2024.100016

eess.IV cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment, with a focus on key indicators such as available boron (B), organic carbon (OC), available manganese (Mn), available sulfur (S), and the sulfur availability index (SAI). A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed. The research integrated color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results showed that combining image features (IFs) with AVs significantly improved prediction accuracy for available B (R2 = 0.80) and OC (R2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further enhanced predictions for available Mn and SAI, with R2 values of 0.72 and 0.70, respectively. The study highlights the potential of integrating these technologies to offer rapid, cost-effective soil testing methods, paving the way for more advanced predictive models and a deeper understanding of soil fertility. Future work should explore the application of deep learning models on a larger dataset, incorporating soils from a wider range of agro-climatic zones under field conditions.

2024-09-06

2404.12489

Christopher Bryant

Kelvin Wey Han Chan, Christopher Bryant, Li Nguyen, Andrew Caines, Zheng Yuan

Grammatical Error Correction for Code-Switched Sentences by Learners of English

Proceedings of the 2024 Joint International Conference on Computational Linguistics

cs.CL

http://creativecommons.org/licenses/by/4.0/

Code-switching (CSW) is a common phenomenon among multilingual speakers where multiple languages are used in a single discourse or utterance. Mixed language utterances may still contain grammatical errors however, yet most existing Grammar Error Correction (GEC) systems have been trained on monolingual data and not developed with CSW in mind. In this work, we conduct the first exploration into the use of GEC systems on CSW text. Through this exploration, we propose a novel method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora. We then investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints, and identify how they affect the performance of GEC systems on CSW text. Our best model achieves an average increase of 1.57 $F_{0.5}$ across 3 CSW test sets (English-Chinese, English-Korean and English-Japanese) without affecting the model's performance on a monolingual dataset. We furthermore discovered that models trained on one CSW language generalise relatively well to other typologically similar CSW languages.

2024-08-13

2404.12631

Solvi Arnold

Solvi Arnold, Reiji Suzuki, Takaya Arita, Kimitoshi Yamazaki

Breaching the Bottleneck: Evolutionary Transition from Reward-Driven Learning to Reward-Agnostic Domain-Adapted Learning in Neuromodulated Neural Nets

Camera ready version. 9 pages, 5 figures

ALIFE 2024: Proceedings of the 2024 Artificial Life Conference

10.1162/isal_a_00725

cs.NE cs.AI

http://creativecommons.org/licenses/by/4.0/

Advanced biological intelligence learns efficiently from an information-rich stream of stimulus information, even when feedback on behaviour quality is sparse or absent. Such learning exploits implicit assumptions about task domains. We refer to such learning as Domain-Adapted Learning (DAL). In contrast, AI learning algorithms rely on explicit externally provided measures of behaviour quality to acquire fit behaviour. This imposes an information bottleneck that precludes learning from diverse non-reward stimulus information, limiting learning efficiency. We consider the question of how biological evolution circumvents this bottleneck to produce DAL. We propose that species first evolve the ability to learn from reward signals, providing inefficient (bottlenecked) but broad adaptivity. From there, integration of non-reward information into the learning process can proceed via gradual accumulation of biases induced by such information on specific task domains. This scenario provides a biologically plausible pathway towards bottleneck-free, domain-adapted learning. Focusing on the second phase of this scenario, we set up a population of NNs with reward-driven learning modelled as Reinforcement Learning (A2C), and allow evolution to improve learning efficiency by integrating non-reward information into the learning process using a neuromodulatory update mechanism. On a navigation task in continuous 2D space, evolved DAL agents show a 300-fold increase in learning speed compared to pure RL agents. Evolution is found to eliminate reliance on reward information altogether, allowing DAL agents to learn from non-reward information exclusively, using local neuromodulation-based connection weight updates only. Code available at github.com/aislab/dal.

2024-08-05

2404.12691

William Brannon

Shayne Longpre, Robert Mahari, Naana Obeng-Marnu, William Brannon, Tobin South, Katy Gero, Sandy Pentland, Jad Kabbara

Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?

ICML 2024 camera-ready version (Spotlight paper). 9 pages, 2 tables

Proceedings of ICML 2024, in PMLR 235:32711-32725. URL: https://proceedings.mlr.press/v235/longpre24b.html

cs.AI cs.CY

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in tracing authenticity, verifying consent, preserving privacy, addressing representation and bias, respecting copyright, and overall developing ethical and trustworthy foundation models. In response, regulation is emphasizing the need for training data transparency to understand foundation models' limitations. Based on a large-scale analysis of the foundation model training data landscape and existing solutions, we identify the missing infrastructure to facilitate responsible foundation model development practices. We examine the current shortcomings of common tools for tracing data authenticity, consent, and documentation, and outline how policymakers, developers, and data creators can facilitate responsible foundation model development by adopting universal data provenance standards.

2024-09-04

2404.12718

Hisashi Shimodaira

Hisashi Shimodaira

Improving Prediction Accuracy of Semantic Segmentation Methods Using Convolutional Autoencoder Based Pre-processing Layers

The changes from the previous version: References [14] and [17] are added in page 2372. Summary of results and discussion (6) are added in page 2383. The new version has been reviewed by AAIML Journal. Reviewer1: The manuscript presents a solid contribution and is well written. The reviewer2: The work is novel and the results are promissing

Advances in Artificial Intelligence and Machine Learning; Research 4 (2) 2369-2386; Published 29-06-2024

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, we propose a method to improve prediction accuracy of semantic segmentation methods as follows: (1) construct a neural network that has pre-processing layers based on a convolutional autoencoder ahead of a semantic segmentation network, and (2) train the entire network initialized by the weights of the pre-trained autoencoder. We applied this method to the fully convolutional network (FCN) and experimentally compared its prediction accuracy on the cityscapes dataset. The Mean IoU of the proposed target model with the He normal initialization is 18.7% higher than that of FCN with the He normal initialization. In addition, those of the modified models of the target model are significantly higher than that of FCN with the He normal initialization. The accuracy and loss curves during the training showed that these are resulting from the improvement of the generalization ability. All of these results provide strong evidence that the proposed method is significantly effective in improving the prediction accuracy of FCN. The proposed method has the following features: it is comparatively simple, whereas the effect on improving the generalization ability and prediction accuracy of FCN is significant; the increase in the number of parameters by using it is very small, and that in the computation time is substantially large. In principle, the proposed method can be applied to other semantic segmentation methods. For semantic segmentation, at present, there is no effective way to improve the prediction accuracy of existing methods. None have published a method which is the same as or similar to our method and none have used such a method in practice. Therefore, we believe that our method is useful in practice and worthy of being widely known and used.

2024-07-10

2404.12810

Marharyta Domnich

Marharyta Domnich, and Raul Vicente

Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence

This work has been accepted to be presented to The 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024), July 17-19, 2024 - Valletta, Malta

In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2155. Springer, Cham

10.1007/978-3-031-63800-8_4

cs.LG cs.AI

http://creativecommons.org/licenses/by/4.0/

A pressing issue in the adoption of AI models is the increasing demand for more human-centric explanations of their predictions. To advance towards more human-centric explanations, understanding how humans produce and select explanations has been beneficial. In this work, inspired by insights of human cognition we propose and test the incorporation of two novel biases to enhance the search for effective counterfactual explanations. Central to our methodology is the application of diffusion distance, which emphasizes data connectivity and actionability in the search for feasible counterfactual explanations. In particular, diffusion distance effectively weights more those points that are more interconnected by numerous short-length paths. This approach brings closely connected points nearer to each other, identifying a feasible path between them. We also introduce a directional coherence term that allows the expression of a preference for the alignment between the joint and marginal directional changes in feature space to reach a counterfactual. This term enables the generation of counterfactual explanations that align with a set of marginal predictions based on expectations of how the outcome of the model varies by changing one feature at a time. We evaluate our method, named Coherent Directional Counterfactual Explainer (CoDiCE), and the impact of the two novel biases against existing methods such as DiCE, FACE, Prototypes, and Growing Spheres. Through a series of ablation experiments on both synthetic and real datasets with continuous and mixed-type features, we demonstrate the effectiveness of our method.

2024-07-26

2404.12832

Marharyta Domnich

Dmytro Shvetsov, Joonas Ariva, Marharyta Domnich, Raul Vicente, and Dmytro Fishman

COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images

This work has been accepted to be presented to The 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024), July 17-19, 2024 - Valletta, Malta

In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2155. Springer, Cham

10.1007/978-3-031-63800-8_3

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (CT) and X-ray scans. However, the performance of deep learning models, particularly in segmentation tasks, is often limited by the need for extensive annotated datasets. To address this challenge, the capabilities of weakly supervised semantic segmentation are explored through the lens of Explainable AI and the generation of counterfactual explanations. The scope of this research is development of a novel counterfactual inpainting approach (COIN) that flips the predicted classification label from abnormal to normal by using a generative model. For instance, if the classifier deems an input medical image X as abnormal, indicating the presence of a pathology, the generative model aims to inpaint the abnormal region, thus reversing the classifier's original prediction label. The approach enables us to produce precise segmentations for pathologies without depending on pre-existing segmentation masks. Crucially, image-level labels are utilized, which are substantially easier to acquire than creating detailed segmentation masks. The effectiveness of the method is demonstrated by segmenting synthetic targets and actual kidney tumors from CT images acquired from Tartu University Hospital in Estonia. The findings indicate that COIN greatly surpasses established attribution methods, such as RISE, ScoreCAM, and LayerCAM, as well as an alternative counterfactual explanation method introduced by Singla et al. This evidence suggests that COIN is a promising approach for semantic segmentation of tumors in CT images, and presents a step forward in making deep learning applications more accessible and effective in healthcare, where annotated data is scarce.

2024-07-26

2404.12845

Aleksei Dorkin

Aleksei Dorkin and Kairit Sirts

TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages

11 pages, 3 figures

Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pp. 120-130, March 2024

cs.CL

http://creativecommons.org/licenses/by/4.0/

We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages for morphological annotation, POS-tagging, lemmatization, character- and word-level gap-filling. We developed a simple, uniform, and computationally lightweight approach based on the adapters framework using parameter-efficient fine-tuning. We applied the same adapter-based approach uniformly to all tasks and 16 languages by fine-tuning stacked language- and task-specific adapters. Our submission obtained an overall second place out of three submissions, with the first place in word-level gap-filling. Our results show the feasibility of adapting language models pre-trained on modern languages to historical and ancient languages via adapter training.

2024-04-22

2404.12886

Zeyu Ling

Zeyu Ling, Bo Han, Yongkang Wongkan, Han Lin, Mohan Kankanhalli, Weidong Geng

MCM: Multi-condition Motion Synthesis Framework

International Joint Conference on Artificial Intelligence 2024

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Conditional human motion synthesis (HMS) aims to generate human motion sequences that conform to specific conditions. Text and audio represent the two predominant modalities employed as HMS control conditions. While existing research has primarily focused on single conditions, the multi-condition human motion synthesis remains underexplored. In this study, we propose a multi-condition HMS framework, termed MCM, based on a dual-branch structure composed of a main branch and a control branch. This framework effectively extends the applicability of the diffusion model, which is initially predicated solely on textual conditions, to auditory conditions. This extension encompasses both music-to-dance and co-speech HMS while preserving the intrinsic quality of motion and the capabilities for semantic association inherent in the original model. Furthermore, we propose the implementation of a Transformer-based diffusion model, designated as MWNet, as the main branch. This model adeptly apprehends the spatial intricacies and inter-joint correlations inherent in motion sequences, facilitated by the integration of multi-wise self-attention modules. Extensive experiments show that our method achieves competitive results in single-condition and multi-condition HMS tasks.

2024-04-22

2404.13024

Ahan Shabanov

Ahan Shabanov, Shrisudhan Govindarajan, Cody Reading, Lily Goli, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi

BANF: Band-limited Neural Fields for Levels of Detail Reconstruction

Project Page: https://theialab.github.io/banf

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 20571-20580

cs.CV eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Largely due to their implicit nature, neural fields lack a direct mechanism for filtering, as Fourier analysis from discrete signal processing is not directly applicable to these representations. Effective filtering of neural fields is critical to enable level-of-detail processing in downstream applications, and support operations that involve sampling the field on regular grids (e.g. marching cubes). Existing methods that attempt to decompose neural fields in the frequency domain either resort to heuristics or require extensive modifications to the neural field architecture. We show that via a simple modification, one can obtain neural fields that are low-pass filtered, and in turn show how this can be exploited to obtain a frequency decomposition of the entire signal. We demonstrate the validity of our technique by investigating level-of-detail reconstruction, and showing how coarser representations can be computed effectively.

2024-07-22

2404.13071

Edward Chang

Edward Y. Chang

Modeling Emotions and Ethics with Large Language Models

8 pages, 4 figures, 3 tables

IEEE MIPR 2024

cs.CL cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper explores the integration of human-like emotions and ethical considerations into Large Language Models (LLMs). We first model eight fundamental human emotions, presented as opposing pairs, and employ collaborative LLMs to reinterpret and express these emotions across a spectrum of intensity. Our focus extends to embedding a latent ethical dimension within LLMs, guided by a novel self-supervised learning algorithm with human feedback (SSHF). This approach enables LLMs to perform self-evaluations and adjustments concerning ethical guidelines, enhancing their capability to generate content that is not only emotionally resonant but also ethically aligned. The methodologies and case studies presented herein illustrate the potential of LLMs to transcend mere text and image generation, venturing into the realms of empathetic interaction and principled decision-making, thereby setting a new precedent in the development of emotionally aware and ethically conscious AI systems.

2024-06-26

2404.13077

James Snyder Jr

Yilin Gao, Sai Kumar Arava, Yancheng Li and James W. Snyder Jr

Improving the Capabilities of Large Language Model Based Marketing Analytics Copilots With Semantic Search And Fine-Tuning

16 pages, 5 figures, presented at the 2nd International Conference on NLP & AI (NLPAI 2024)

International Journal on Cybernetics & Informatics (IJCI), vol. 13, no. 2, pp. 15-31, Apr. 2024

10.5121/ijci.2024.130202

cs.CL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Artificial intelligence (AI) is widely deployed to solve problems related to marketing attribution and budget optimization. However, AI models can be quite complex, and it can be difficult to understand model workings and insights without extensive implementation teams. In principle, recently developed large language models (LLMs), like GPT-4, can be deployed to provide marketing insights, reducing the time and effort required to make critical decisions. In practice, there are substantial challenges that need to be overcome to reliably use such models. We focus on domain-specific question-answering, SQL generation needed for data retrieval, and tabular analysis and show how a combination of semantic search, prompt engineering, and fine-tuning can be applied to dramatically improve the ability of LLMs to execute these tasks accurately. We compare both proprietary models, like GPT-4, and open-source models, like Llama-2-70b, as well as various embedding methods. These models are tested on sample use cases specific to marketing mix modeling and attribution.

2024-04-23

2404.13099

Mohit Gupta

Avinash Anand, Mohit Gupta, Kritarth Prasad, Navya Singla, Sanjana Sanjeev, Jatin Kumar, Adarsh Raj Shivam, Rajiv Ratn Shah

Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks

10 pages, 3 figures, NeurIPS 2023 Workshop on Generative AI for Education (GAIED)

NeurIPS 2023 Workshop on Generative AI for Education (GAIED)

cs.CL cs.AI

http://creativecommons.org/licenses/by/4.0/

The rapid progress in the field of natural language processing (NLP) systems and the expansion of large language models (LLMs) have opened up numerous opportunities in the field of education and instructional methods. These advancements offer the potential for tailored learning experiences and immediate feedback, all delivered through accessible and cost-effective services. One notable application area for this technological advancement is in the realm of solving mathematical problems. Mathematical problem-solving not only requires the ability to decipher complex problem statements but also the skill to perform precise arithmetic calculations at each step of the problem-solving process. However, the evaluation of the arithmetic capabilities of large language models remains an area that has received relatively little attention. In response, we introduce an extensive mathematics dataset called "MathQuest" sourced from the 11th and 12th standard Mathematics NCERT textbooks. This dataset encompasses mathematical challenges of varying complexity and covers a wide range of mathematical concepts. Utilizing this dataset, we conduct fine-tuning experiments with three prominent LLMs: LLaMA-2, WizardMath, and MAmmoTH. These fine-tuned models serve as benchmarks for evaluating their performance on our dataset. Our experiments reveal that among the three models, MAmmoTH-13B emerges as the most proficient, achieving the highest level of competence in solving the presented mathematical problems. Consequently, MAmmoTH-13B establishes itself as a robust and dependable benchmark for addressing NCERT mathematics problems.

2024-04-23

2404.13108

Marek Wodzinski

Marek Wodzinski, Niccol\`o Marini, Manfredo Atzori, Henning M\"uller

RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge

Computer Methods and Programs in Biomedicine, Vol. 250, 2024

10.1016/j.cmpb.2024.108187

eess.IV cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology. We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The method scored 1st place in the ACROBAT 2023 challenge. We evaluated using three open datasets: (i) ANHIR, (ii) ACROBAT, and (iii) HyReCo, and performed several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset. The method does not require any fine-tuning to a new datasets and can be used out-of-the-box for other types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level. The proposed method is a significant contribution to the WSI registration, thus advancing the field of digital pathology.

2024-05-22

2404.13353

Pengzhi Li

Pengzhi Li, Baijuan Li

Generating Daylight-driven Architectural Design via Diffusion Models

Project page: https://zrealli.github.io/DDADesign/

CVPR 2024 Workshop

cs.CV

http://creativecommons.org/licenses/by/4.0/

In recent years, the rapid development of large-scale models has made new possibilities for interdisciplinary fields such as architecture. In this paper, we present a novel daylight-driven AI-aided architectural design method. Firstly, we formulate a method for generating massing models, producing architectural massing models using random parameters quickly. Subsequently, we integrate a daylight-driven facade design strategy, accurately determining window layouts and applying them to the massing models. Finally, we seamlessly combine a large-scale language model with a text-to-image model, enhancing the efficiency of generating visual architectural design renderings. Experimental results demonstrate that our approach supports architects' creative inspirations and pioneers novel avenues for architectural design development. Project page: https://zrealli.github.io/DDADesign/.

2024-04-23

2404.13421

Michael Duchesne

Michael Duchesne, Kaiwen Zhang, Chamseddine Talhi

MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning

Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC '24, 1587-1595, April 2024. ACM

10.1145/3605098.3636000

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Federated Learning (FL) has emerged as a prominent privacy-preserving technique for enabling use cases like confidential clinical machine learning. FL operates by aggregating models trained by remote devices which owns the data. Thus, FL enables the training of powerful global models using crowd-sourced data from a large number of learners, without compromising their privacy. However, the aggregating server is a single point of failure when generating the global model. Moreover, the performance of the model suffers when the data is not independent and identically distributed (non-IID data) on all remote devices. This leads to vastly different models being aggregated, which can reduce the performance by as much as 50% in certain scenarios. In this paper, we seek to address the aforementioned issues while retaining the benefits of FL. We propose MultiConfederated Learning: a decentralized FL framework which is designed to handle non-IID data. Unlike traditional FL, MultiConfederated Learning will maintain multiple models in parallel (instead of a single global model) to help with convergence when the data is non-IID. With the help of transfer learning, learners can converge to fewer models. In order to increase adaptability, learners are allowed to choose which updates to aggregate from their peers.

2024-04-23

2404.13439

Sefika Efeoglu

Sefika Efeoglu and Adrian Paschke

Fine-Grained Named Entities for Corona News

Published at SWAT4HCLS 2023: The 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences

CEUR-WS 2023

cs.CL

http://creativecommons.org/licenses/by/4.0/

Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured texts is time-consuming without representing them in a structured format; therefore, representing them in a structured format is crucial. An information extraction pipeline with essential tasks -- named entity tagging and relation extraction -- to accomplish this goal might be applied to these texts. This study proposes a data annotation pipeline to generate training data from corona news articles, including generic and domain-specific entities. Named entity recognition models are trained on this annotated corpus and then evaluated on test sentences manually annotated by domain experts evaluating the performance of a trained model. The code base and demonstration are available at https://github.com/sefeoglu/coronanews-ner.git.

2024-04-25

2404.13454

Michael Bidollahkhani

Michael Bidollahkhani, Julian M. Kunkel

Revolutionizing System Reliability: The Role of AI in Predictive Maintenance Strategies

Accepted, published and presented for the IARIA CLOUDCOMP2024 Conference of Venice, Italy

In Proceedings of the IARIA CloudComputing 2024 Conference (pp. 1-9). Venice, Italy. ISSN: 2308-4294. ISBN: 978-1-68558-156-5

cs.AI cs.PF cs.SY eess.SY

http://creativecommons.org/licenses/by-nc-sa/4.0/

The landscape of maintenance in distributed systems is rapidly evolving with the integration of Artificial Intelligence (AI). Also, as the complexity of computing continuum systems intensifies, the role of AI in predictive maintenance (Pd.M.) becomes increasingly pivotal. This paper presents a comprehensive survey of the current state of Pd.M. in the computing continuum, with a focus on the combination of scalable AI technologies. Recognizing the limitations of traditional maintenance practices in the face of increasingly complex and heterogenous computing continuum systems, the study explores how AI, especially machine learning and neural networks, is being used to enhance Pd.M. strategies. The survey encompasses a thorough review of existing literature, highlighting key advancements, methodologies, and case studies in the field. It critically examines the role of AI in improving prediction accuracy for system failures and in optimizing maintenance schedules, thereby contributing to reduced downtime and enhanced system longevity. By synthesizing findings from the latest advancements in the field, the article provides insights into the effectiveness and challenges of implementing AI-driven predictive maintenance. It underscores the evolution of maintenance practices in response to technological advancements and the growing complexity of computing continuum systems. The conclusions drawn from this survey are instrumental for practitioners and researchers in understanding the current landscape and future directions of Pd.M. in distributed systems. It emphasizes the need for continued research and development in this area, pointing towards a trend of more intelligent, efficient, and cost-effective maintenance solutions in the era of AI.

2024-04-23

2404.13515

Yuxuan Zhu

Yuxuan Zhu, Jiachen Liu, Mosharaf Chowdhury, Fan Lai

FedTrans: Efficient Federated Learning via Multi-Model Transformation

MLSys (2024)

cs.LG cs.AI cs.DC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices. Yet, training and customizing models for FL clients is notoriously challenging due to the heterogeneity of client data, device capabilities, and the massive scale of clients, making individualized model exploration prohibitively expensive. State-of-the-art FL solutions personalize a globally trained model or concurrently train multiple models, but they often incur suboptimal model accuracy and huge training costs. In this paper, we introduce FedTrans, a multi-model FL training framework that automatically produces and trains high-accuracy, hardware-compatible models for individual clients at scale. FedTrans begins with a basic global model, identifies accuracy bottlenecks in model architectures during training, and then employs model transformation to derive new models for heterogeneous clients on the fly. It judiciously assigns models to individual clients while performing soft aggregation on multi-model updates to minimize total training costs. Our evaluations using realistic settings show that FedTrans improves individual client model accuracy by 14% - 72% while slashing training costs by 1.6X - 20X over state-of-the-art solutions.

2024-04-29

2404.13565

Panfeng Li

Panfeng Li, Qikai Yang, Xieming Geng, Wenjing Zhou, Zhicheng Ding, Yi Nian

Exploring Diverse Methods in Visual Question Answering

Accepted by 2024 5th International Conference on Electronic Communication and Artificial Intelligence

Proceedings of the 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI), 2024, pp. 681-685

10.1109/ICECAI62591.2024.10674838

cs.CV cs.AI cs.CL cs.LG

http://creativecommons.org/licenses/by/4.0/

This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. Leveraging a balanced VQA dataset, we investigate three distinct strategies. Firstly, GAN-based approaches aim to generate answer embeddings conditioned on image and question inputs, showing potential but struggling with more complex tasks. Secondly, autoencoder-based techniques focus on learning optimal embeddings for questions and images, achieving comparable results with GAN due to better ability on complex questions. Lastly, attention mechanisms, incorporating Multimodal Compact Bilinear pooling (MCB), address language priors and attention modeling, albeit with a complexity-performance trade-off. This study underscores the challenges and opportunities in VQA and suggests avenues for future research, including alternative GAN formulations and attentional mechanisms.

2024-09-26

2404.13634

Md Fahim Sikder

Resmi Ramachandranpillai, Md Fahim Sikder, David Bergstr\"om, Fredrik Heintz

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks

Journal of Artificial Intelligence Research, vol. 79, Apr. 2024, 1313-41

10.1613/jair.1.15317

cs.LG cs.AI

http://creativecommons.org/licenses/by/4.0/

Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the existing literature primarily focuses on the quality of synthetic health data, neglecting the crucial aspect of fairness in downstream predictions. Consequently, models trained on synthetic EHR have faced criticism for producing biased outcomes in target tasks. These biases can arise from either spurious correlations between features or the failure of models to accurately represent sub-groups. To address these concerns, we present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based synthetic data generator specifically designed for the healthcare domain. In order to tackle spurious correlations (i), we propose an information-constrained Data Generation Process that enables the generator to learn a fair deterministic transformation based on a well-defined notion of algorithmic fairness. To overcome the challenge of capturing exact sub-group representations (ii), we incentivize the generator to preserve sub-group densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. We conduct extensive experiments using the MIMIC-III database. Our results demonstrate that Bt-GAN achieves SOTA accuracy while significantly improving fairness and minimizing bias amplification. We also perform an in-depth explainability analysis to provide additional evidence supporting the validity of our study. In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain. By incorporating fairness considerations and leveraging advanced techniques such as GANs, we pave the way for more reliable and unbiased predictions in healthcare applications.

2024-04-29

2404.13667

Felix Schmitt-Koopmann

Felix M. Schmitt-Koopmann, Elaine M. Huang, Hans-Peter Hutter, Thilo Stadelmann, Alireza Darvishy

MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition

12 pages, 6 figures

IEEE Access 12 (2024) 76963-76974

10.1109/ACCESS.2024.3404834

cs.CV cs.AI

http://creativecommons.org/licenses/by/4.0/

Printed mathematical expression recognition (MER) models are usually trained and tested using LaTeX-generated mathematical expressions (MEs) as input and the LaTeX source code as ground truth. As the same ME can be generated by various different LaTeX source codes, this leads to unwanted variations in the ground truth data that bias test performance results and hinder efficient learning. In addition, the use of only one font to generate the MEs heavily limits the generalization of the reported results to realistic scenarios. We propose a data-centric approach to overcome this problem, and present convincing experimental results: Our main contribution is an enhanced LaTeX normalization to map any LaTeX ME to a canonical form. Based on this process, we developed an improved version of the benchmark dataset im2latex-100k, featuring 30 fonts instead of one. Second, we introduce the real-world dataset realFormula, with MEs extracted from papers. Third, we developed a MER model, MathNet, based on a convolutional vision transformer, with superior results on all four test sets (im2latex-100k, im2latexv2, realFormula, and InftyMDB-1), outperforming the previous state of the art by up to 88.3%.

2024-06-10

2404.13756

Anthony Bilic

Anthony Bilic, Chen Chen

BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark

IEEE International Conference on Healthcare Informatics (IEEE ICHI 2024)

10.1109/ICHI61247.2024.00107

eess.IV cs.CV

http://creativecommons.org/licenses/by/4.0/

Binary breast cancer tumor segmentation with Magnetic Resonance Imaging (MRI) data is typically trained and evaluated on private medical data, which makes comparing deep learning approaches difficult. We propose a benchmark (BC-MRI-SEG) for binary breast cancer tumor segmentation based on publicly available MRI datasets. The benchmark consists of four datasets in total, where two datasets are used for supervised training and evaluation, and two are used for zero-shot evaluation. Additionally we compare state-of-the-art (SOTA) approaches on our benchmark and provide an exhaustive list of available public breast cancer MRI datasets. The source code has been made available at https://irulenot.github.io/BC_MRI_SEG_Benchmark.

2024-08-27

2404.13812

Qikai Yang

Qikai Yang, Panfeng Li, Xinhe Xu, Zhicheng Ding, Wenjing Zhou, Yi Nian

A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation

Accepted by 2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)

Proceedings of the 2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), 2024, pp. 214-218

10.1109/MLISE62164.2024.10674203

cs.SI cs.AI cs.IR cs.LG

http://creativecommons.org/licenses/by-nc-sa/4.0/

In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size and potential bias present in real-world datasets. This study presents and explores a generative augmentation framework of social network advertising data. Our framework explores three generative models for data augmentation - Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Gaussian Mixture Models (GMMs) - to enrich data availability and diversity in the context of social network advertising analytics effectiveness. By performing synthetic extensions of the feature space, we find that through data augmentation, the performance of various classifiers has been quantitatively improved. Furthermore, we compare the relative performance gains brought by each data augmentation technique, providing insights for practitioners to select appropriate techniques to enhance model performance. This paper contributes to the literature by showing that synthetic data augmentation alleviates the limitations imposed by small or imbalanced datasets in the field of social network advertising. At the same time, this article also provides a comparative perspective on the practicality of different data augmentation methods, thereby guiding practitioners to choose appropriate techniques to enhance model performance.

2024-09-23

2404.13865

Mohit Gupta

Avinash Anand, Kritarth Prasad, Ujjwal Goel, Mohit Gupta, Naman Lal, Astha Verma, Rajiv Ratn Shah

Context-Enhanced Language Models for Generating Multi-Paper Citations

14 pages, 7 figures, 11th International Conference, BDA 2023, Delhi, India

Big Data and Artificial Intelligence 2023, Delhi, India, December 7, 80 94

10.1007/978-3-031-49601-1_6

cs.CL

http://creativecommons.org/licenses/by/4.0/

Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.

2024-04-23

2404.13880

Panfeng Li

Zhicheng Ding, Panfeng Li, Qikai Yang, Siyang Li, Qingtian Gong

Regional Style and Color Transfer

Accepted by 2024 5th International Conference on Computer Vision, Image and Deep Learning

Proceedings of the 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2024, pp. 593-597

10.1109/CVIDL62147.2024.10604182

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-sa/4.0/

This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figures. To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image. Subsequently, style transfer is applied exclusively to the background region. The isolated foreground objects are then carefully reintegrated into the style-transferred background. To enhance the visual coherence between foreground and background, a color transfer step is employed on the foreground elements prior to their rein-corporation. Finally, we utilize feathering techniques to achieve a seamless amalgamation of foreground and background, resulting in a visually unified and aesthetically pleasing final composition. Extensive evaluations demonstrate that our proposed approach yields significantly more natural stylistic transformations compared to conventional methods.

2024-09-17

2404.13996

Fabrice Mayran De Chamisso

Fabrice Mayran de Chamisso, Lo\"ic Cotten, Valentine Dhers, Thomas Lompech, Florian Seywert and Arnaud Susset

Challenges in automatic and selective plant-clearing

Proceedings of the IEEE ICRA 2024 Workshop on Field Robotics

cs.CV

http://creativecommons.org/licenses/by/4.0/

With the advent of multispectral imagery and AI, there have been numerous works on automatic plant segmentation for purposes such as counting, picking, health monitoring, localized pesticide delivery, etc. In this paper, we tackle the related problem of automatic and selective plant-clearing in a sustainable forestry context, where an autonomous machine has to detect and avoid specific plants while clearing any weeds which may compete with the species being cultivated. Such an autonomous system requires a high level of robustness to weather conditions, plant variability, terrain and weeds while remaining cheap and easy to maintain. We notably discuss the lack of robustness of spectral imagery, investigate the impact of the reference database's size and discuss issues specific to AI systems operating in uncontrolled environments.

2024-04-24

2404.14024

Alexandre Bittar

Alexandre Bittar, Philip N. Garner

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks

Frontiers in Neuroscience, Vol. 18 (2024)

10.3389/fnins.2024.1449181

cs.CL q-bio.NC

http://creativecommons.org/licenses/by-sa/4.0/

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronisation phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.

2024-09-26

2404.14044

Jiahao Ma

Jiahao Ma, Miaomiao Liu, David Ahmedt-Aristizaba, Chuong Nguyen

HashPoint: Accelerated Point Searching and Sampling for Neural Rendering

CVPR2024 Highlight

The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024

cs.CV

http://creativecommons.org/licenses/by-sa/4.0/

In this paper, we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm, two typical approaches are employed: rasterization and ray tracing. The rasterization-based methods enable real-time rendering at the cost of increased memory and lower fidelity. In contrast, the ray-tracing-based methods yield superior quality but demand longer rendering time. We solve this problem by our HashPoint method combining these two strategies, leveraging rasterization for efficient point searching and sampling, and ray marching for rendering. Our method optimizes point searching by rasterizing points within the camera's view, organizing them in a hash table, and facilitating rapid searches. Notably, we accelerate the rendering process by adaptive sampling on the primary surface encountered by the ray. Our approach yields substantial speed-up for a range of state-of-the-art ray-tracing-based methods, maintaining equivalent or superior accuracy across synthetic and real test datasets. The code will be available at https://jiahao-ma.github.io/hashpoint/.

2024-05-14

2404.14050

Hilde Weerts

Hilde Weerts, Aislinn Kelly-Lyth, Reuben Binns, Jeremias Adams-Prassl

Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms

2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24)

10.1145/3630106.3659010

cs.AI cs.CY

http://creativecommons.org/licenses/by/4.0/

Emerging scholarship suggests that the EU legal concept of direct discrimination - where a person is given different treatment on grounds of a protected characteristic - may apply to various algorithmic decision-making contexts. This has important implications: unlike indirect discrimination, there is generally no 'objective justification' stage in the direct discrimination framework, which means that the deployment of directly discriminatory algorithms will usually be unlawful per se. In this paper, we focus on the most likely candidate for direct discrimination in the algorithmic context, termed inherent direct discrimination, where a proxy is inextricably linked to a protected characteristic. We draw on computer science literature to suggest that, in the algorithmic context, 'treatment on the grounds of' needs to be understood in terms of two steps: proxy capacity and proxy use. Only where both elements can be made out can direct discrimination be said to be `on grounds of' a protected characteristic. We analyse the legal conditions of our proposed proxy capacity and proxy use tests. Based on this analysis, we discuss technical approaches and metrics that could be developed or applied to identify inherent direct discrimination in algorithmic decision-making.

2024-04-23

2404.14057

Shir Lissak

Shir Lissak, Yaakov Ophir, Refael Tikochinski, Anat Brunstein Klomek, Itay Sisso, Eyal Fruchter, Roi Reichart

Bored to Death: Artificial Intelligence Research Reveals the Role of Boredom in Suicide Behavior

www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1328122

cs.CL

http://creativecommons.org/publicdomain/zero/1.0/

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Method: The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results: An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusions: Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.

2024-04-29

2404.14183

Yuxia Wang

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Chenxi Whitehouse, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov

SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

23 pages, 12 tables

Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

cs.CL

http://creativecommons.org/licenses/by/4.0/

We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LLMs.

2024-04-23

2404.14232

Anwesha Das

Anwesha Das, Zekun Wu, Iza \v{S}krjanec, and Anna Maria Feit (Saarland University, Germany)

Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction

18 pages, 9 Figures, Conference: ACM Symposium on Eye Tracking Research & Applications (ETRA); Journal: Proc. ACM Hum.-Comput. Interact., Vol. 8, No. ETRA, Article 236. Publication date: May 2024

Proc. ACM Hum.-Comput. Interact., Vol. 8, No. ETRA, Article 236. Publication date: May 2024

10.1145/3655610

cs.HC cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Visual highlighting can guide user attention in complex interfaces. However, its effectiveness under limited attentional capacities is underexplored. This paper examines the joint impact of visual highlighting (permanent and dynamic) and dual-task-induced cognitive load on gaze behaviour. Our analysis, using eye-movement data from 27 participants viewing 150 unique webpages reveals that while participants' ability to attend to UI elements decreases with increasing cognitive load, dynamic adaptations (i.e., highlighting) remain attention-grabbing. The presence of these factors significantly alters what people attend to and thus what is salient. Accordingly, we show that state-of-the-art saliency models increase their performance when accounting for different cognitive loads. Our empirical insights, along with our openly available dataset, enhance our understanding of attentional processes in UIs under varying cognitive (and perceptual) loads and open the door for new models that can predict user attention while multitasking.

2024-05-03

2404.14357

Ted Edward Holmberg

Ted Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi

A Stochastic Geo-spatiotemporal Bipartite Network to Optimize GCOOS Sensor Placement Strategies

7 pages, 6 figures, 2022 IEEE International Conference on Big Data (Big Data)

2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 3668-3674

10.1109/BigData55660.2022.10020928

cs.MA cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes. The coverage and coverage robustness scores evaluate the effectiveness of the observer node placements. This measure is beneficial for stochastic data as it may be coupled with Monte Carlo simulations to identify optimal placements for new observer nodes. In this paper, we construct a Geo-SpatioTemporal Bipartite Network (GSTBN) within the stochastic and dynamical environment of the Gulf of Mexico. This GSTBN consists of GCOOS sensor nodes and HYCOM Region of Interest (RoI) event nodes. The goal is to identify optimal placements to expand GCOOS to improve the forecasting outcomes by the HYCOM ocean prediction model.

2024-10-01

2404.14388

Ted Edward Holmberg

Ted Edward Holmberg, Mahdi Abdelguerfi, Elias Ioup

STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies

10 pages, 17 figures, 2023 IEEE International Conference on Big Data (BigData)

2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 2920-2929

10.1109/BigData59044.2023.10386774

cs.LG cs.CV cs.MA

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Spatiotemporal networks' observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet), linking observational nodes (e.g., surveillance cameras) to events within defined geographical regions, enabling efficient monitoring. Using data from Real-Time Crime Camera (RTCC) systems and Calls for Service (CFS) in New Orleans, where RTCC combats rising crime amidst reduced police presence, we address the network's initial observational imbalances. Aiming for uniform observational efficacy, we propose the Proximal Recurrence approach. It outperformed traditional clustering methods like k-means and DBSCAN by offering holistic event frequency and spatial consideration, enhancing observational coverage.

2024-10-01

2404.14450

Sefika Efeoglu

Sefika Efeoglu

GraphMatcher: A Graph Representation Learning Approach for Ontology Matching

The 17th International Workshop on Ontology Matching, The 21st International Semantic Web Conference (ISWC) 2022, 23 October 2022, Hangzhou, China

The 17th International Workshop on Ontology Matching, The 21st International Semantic Web Conference (ISWC) 2022, 23 October 2022, Hangzhou, China

cs.AI

http://creativecommons.org/licenses/by/4.0/

Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain ontologies, semantically similar entities in these ontologies must be found and aligned before merging them. GraphMatcher, developed in this study, is an ontology matching system using a graph attention approach to compute higher-level representation of a class together with its surrounding terms. The GraphMatcher has obtained remarkable results in in the Ontology Alignment Evaluation Initiative (OAEI) 2022 conference track. Its codes are available at ~\url{https://github.com/sefeoglu/gat_ontology_matching}.

2024-04-24

2404.14575

Richard Stromer

Richard Stromer (1), Oskar Triebe (1), Chad Zanocco (1), Ram Rajagopal (1) ((1) Stanford University)

Designing forecasting software for forecast users: Empowering non-experts to create and understand their own forecasts

10 pages

AMCIS 2023 Proceedings 1

cs.HC cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Forecasts inform decision-making in nearly every domain. Forecasts are often produced by experts with rare or hard to acquire skills. In practice, forecasts are often used by domain experts and managers with little forecasting expertise. Our study focuses on how to design forecasting software that empowers non-expert users. We study how users can make use of state-of-the-art forecasting methods, embed their domain knowledge, and how they build understanding and trust towards generated forecasts. To do so, we co-designed a forecasting software prototype using feedback from users and then analyzed their interactions with our prototype. Our results identified three main considerations for non-expert users: (1) a safe stepwise approach facilitating causal understanding and trust; (2) a white box model supporting human-reasoning-friendly components; (3) the inclusion of domain knowledge. This paper contributes insights into how non-expert users interact with forecasting software and by recommending ways to design more accessible forecasting software.

2024-04-24

2404.14606

Armando Zhu

Armando Zhu, Keqin Li, Tong Wu, Peng Zhao, Bo Hong

Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification

Journal of Computer Technology and Applied Mathematics, vol. 1, no. 1, Apr. 2024, pp. 46-53,

10.5281/zenodo.11083875

cs.CV cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

With wearing masks becoming a new cultural norm, facial expression recognition (FER) while taking masks into account has become a significant challenge. In this paper, we propose a unified multi-branch vision transformer for facial expression recognition and mask wearing classification tasks. Our approach extracts shared features for both tasks using a dual-branch architecture that obtains multi-scale feature representations. Furthermore, we propose a cross-task fusion phase that processes tokens for each task with separate branches, while exchanging information using a cross attention module. Our proposed framework reduces the overall complexity compared with using separate networks for both tasks by the simple yet effective cross-task fusion phase. Extensive experiments demonstrate that our proposed model performs better than or on par with different state-of-the-art methods on both facial expression recognition and facial mask wearing classification task.

2024-05-01

2404.14736

Katie Seaborn

Katie Seaborn, Jacqueline Urakami, Peter Pennefather, Norihisa P. Miyake

Qualitative Approaches to Voice UX

ACM Computing Surveys (2024)

10.1145/3658666

cs.HC cs.AI cs.CL cs.CY cs.SD eess.AS

http://creativecommons.org/licenses/by-nc-nd/4.0/

Voice is a natural mode of expression offered by modern computer-based systems. Qualitative perspectives on voice-based user experiences (voice UX) offer rich descriptions of complex interactions that numbers alone cannot fully represent. We conducted a systematic review of the literature on qualitative approaches to voice UX, capturing the nature of this body of work in a systematic map and offering a qualitative synthesis of findings. We highlight the benefits of qualitative methods for voice UX research, identify opportunities for increasing rigour in methods and outcomes, and distill patterns of experience across a diversity of devices and modes of qualitative praxis.

2024-04-24

2404.14771

Hong Huang

Hong Huang, Yuyi Wang, Luyao Li, Jun Lin

Music Style Transfer With Diffusion Model

8 pages, 6 figures, ICMC 2023

International Computer Music Conference (ICMC 2023) pp. 40-47, October 2023

cs.SD cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Previous studies on music style transfer have mainly focused on one-to-one style conversion, which is relatively limited. When considering the conversion between multiple styles, previous methods required designing multiple modes to disentangle the complex style of the music, resulting in large computational costs and slow audio generation. The existing music style transfer methods generate spectrograms with artifacts, leading to significant noise in the generated audio. To address these issues, this study proposes a music style transfer framework based on diffusion models (DM) and uses spectrogram-based methods to achieve multi-to-multi music style transfer. The GuideDiff method is used to restore spectrograms to high-fidelity audio, accelerating audio generation speed and reducing noise in the generated audio. Experimental results show that our model has good performance in multi-mode music style transfer compared to the baseline and can generate high-quality audio in real-time on consumer-grade GPUs.

2024-04-24

2404.14946

Sen Liu

Sen Liu, Yiwei Guo, Xie Chen and Kai Yu

StoryTTS: A Highly Expressive Text-to-Speech Dataset with Rich Textual Expressiveness Annotations

Accepted by ICASSP 2024

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 11521-11525

cs.SD cs.CL eess.AS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

While acoustic expressiveness has long been studied in expressive text-to-speech (ETTS), the inherent expressiveness in text lacks sufficient attention, especially for ETTS of artistic works. In this paper, we introduce StoryTTS, a highly ETTS dataset that contains rich expressiveness both in acoustic and textual perspective, from the recording of a Mandarin storytelling show. A systematic and comprehensive labeling framework is proposed for textual expressiveness. We analyze and define speech-related textual expressiveness in StoryTTS to include five distinct dimensions through linguistics, rhetoric, etc. Then we employ large language models and prompt them with a few manual annotation examples for batch annotation. The resulting corpus contains 61 hours of consecutive and highly prosodic speech equipped with accurate text transcriptions and rich textual expressiveness annotations. Therefore, StoryTTS can aid future ETTS research to fully mine the abundant intrinsic textual and acoustic features. Experiments are conducted to validate that TTS models can generate speech with improved expressiveness when integrating with the annotated textual labels in StoryTTS.

2024-04-24

2404.15003

Aleksei Dorkin

Aleksei Dorkin and Kairit Sirts

Comparison of Current Approaches to Lemmatization: A Case Study in Estonian

6 pages, 2 figures

Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 280-285, May 2023

cs.CL

http://creativecommons.org/licenses/by/4.0/

This study evaluates three different lemmatization approaches to Estonian -- Generative character-level models, Pattern-based word-level classification models, and rule-based morphological analysis. According to our experiments, a significantly smaller Generative model consistently outperforms the Pattern-based classification model based on EstBERT. Additionally, we observe a relatively small overlap in errors made by all three models, indicating that an ensemble of different approaches could lead to improvements.

2024-04-24

2404.15010

Shuofeng Sun

Shuofeng Sun, Yongming Rao, Jiwen Lu, Haibin Yan

X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition

The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Numerous prior studies predominantly emphasize constructing relation vectors for individual neighborhood points and generating dynamic kernels for each vector and embedding these into high-dimensional spaces to capture implicit local structures. However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the local geometric structure of point clouds due to the absence of explicit structural information. Hence, we introduce X-3D, an explicit 3D structure modeling approach. X-3D functions by capturing the explicit local structural information within the input 3D space and employing it to produce dynamic kernels with shared weights for all neighborhood points within the current local region. This modeling approach introduces effective geometric prior and significantly diminishes the disparity between the local structure of the embedding space and the original input point cloud, thereby improving the extraction of local features. Experiments show that our method can be used on a variety of methods and achieves state-of-the-art performance on segmentation, classification, detection tasks with lower extra computational cost, such as \textbf{90.7\%} on ScanObjectNN for classification, \textbf{79.2\%} on S3DIS 6 fold and \textbf{74.3\%} on S3DIS Area 5 for segmentation, \textbf{76.3\%} on ScanNetV2 for segmentation and \textbf{64.5\%} mAP , \textbf{46.9\%} mAP on SUN RGB-D and \textbf{69.0\%} mAP , \textbf{51.1\%} mAP on ScanNetV2 . Our code is available at \href{https://github.com/sunshuofeng/X-3D}{https://github.com/sunshuofeng/X-3D}.

2024-04-24

2404.15129

Sara Dadjouy

Sara Dadjouy, Hedieh Sajedi

Gallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN

Published in 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR)

2024 10th International Conference on Artificial Intelligence and Robotics (QICAR) (pp. 227-231). IEEE

10.1109/QICAR61538.2024.10496645

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

Medical image analysis is a significant application of artificial intelligence for disease diagnosis. A crucial step in this process is the identification of regions of interest within the images. This task can be automated using object detection algorithms. YOLO and Faster R-CNN are renowned for such algorithms, each with its own strengths and weaknesses. This study aims to explore the advantages of both techniques to select more accurate bounding boxes for gallbladder detection from ultrasound images, thereby enhancing gallbladder cancer classification. A fusion method that leverages the benefits of both techniques is presented in this study. The proposed method demonstrated superior classification performance, with an accuracy of 92.62%, compared to the individual use of Faster R-CNN and YOLOv8, which yielded accuracies of 90.16% and 82.79%, respectively.

2024-04-24

2404.15196

Yurii Paniv

Yurii Paniv, Dmytro Chaplynskyi, Nikita Trynus, Volodymyr Kyrylov

Setting up the Data Printer with Improved English to Ukrainian Machine Translation

Published at Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP)@ LREC-COLING 2024 (pp. 41-50)

Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP)@ LREC-COLING 2024 (pp. 41-50)

cs.CL

http://creativecommons.org/licenses/by-sa/4.0/

To build large language models for Ukrainian we need to expand our corpora with large amounts of new algorithmic tasks expressed in natural language. Examples of task performance expressed in English are abundant, so with a high-quality translation system our community will be enabled to curate datasets faster. To aid this goal, we introduce a recipe to build a translation system using supervised finetuning of a large pretrained language model with a noisy parallel dataset of 3M pairs of Ukrainian and English sentences followed by a second phase of training using 17K examples selected by k-fold perplexity filtering on another dataset of higher quality. Our decoder-only model named Dragoman beats performance of previous state of the art encoder-decoder models on the FLORES devtest set.

2024-07-15

2404.15276

Xiangyu Xu

Xiangyu Xu, Lijuan Liu, Shuicheng Yan

SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation

Published at TPAMI 2024

https://www.computer.org/csdl/journal/tp/2024/05/10354384/1SP2qWh8Fq0

cs.CV cs.AI cs.GR cs.LG cs.MM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Existing Transformers for monocular 3D human shape and pose estimation typically have a quadratic computation and memory complexity with respect to the feature length, which hinders the exploitation of fine-grained information in high-resolution features that is beneficial for accurate reconstruction. In this work, we propose an SMPL-based Transformer framework (SMPLer) to address this issue. SMPLer incorporates two key ingredients: a decoupled attention operation and an SMPL-based target representation, which allow effective utilization of high-resolution features in the Transformer. In addition, based on these two designs, we also introduce several novel modules including a multi-scale attention and a joint-aware attention to further boost the reconstruction performance. Extensive experiments demonstrate the effectiveness of SMPLer against existing 3D human shape and pose estimation methods both quantitatively and qualitatively. Notably, the proposed algorithm achieves an MPJPE of 45.2 mm on the Human3.6M dataset, improving upon Mesh Graphormer by more than 10% with fewer than one-third of the parameters. Code and pretrained models are available at https://github.com/xuxy09/SMPLer.

2024-04-24

2404.15310

Ruikun Hou

Ruikun Hou, Tim F\"utterer, Babette B\"uhler, Efe Bozkir, Peter Gerjets, Ulrich Trautwein, Enkelejda Kasneci

Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT

Accepted as a full paper by the 25th International Conference on Artificial Intelligence in Education (AIED 2024)

Proceedings of the 25th International Conference on Artificial Intelligence in Education (AIED 2024)

10.1007/978-3-031-64302-6_5

cs.HC cs.AI cs.CY cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Classroom observation protocols standardize the assessment of teaching effectiveness and facilitate comprehension of classroom interactions. Whereas these protocols offer teachers specific feedback on their teaching practices, the manual coding by human raters is resource-intensive and often unreliable. This has sparked interest in developing AI-driven, cost-effective methods for automating such holistic coding. Our work explores a multimodal approach to automatically estimating encouragement and warmth in classrooms, a key component of the Global Teaching Insights (GTI) study's observation protocol. To this end, we employed facial and speech emotion recognition with sentiment analysis to extract interpretable features from video, audio, and transcript data. The prediction task involved both classification and regression methods. Additionally, in light of recent large language models' remarkable text annotation capabilities, we evaluated ChatGPT's zero-shot performance on this scoring task based on transcripts. We demonstrated our approach on the GTI dataset, comprising 367 16-minute video segments from 92 authentic lesson recordings. The inferences of GPT-4 and the best-trained model yielded correlations of r = .341 and r = .441 with human ratings, respectively. Combining estimates from both models through averaging, an ensemble approach achieved a correlation of r = .513, comparable to human inter-rater reliability. Our model explanation analysis indicated that text sentiment features were the primary contributors to the trained model's decisions. Moreover, GPT-4 could deliver logical and concrete reasoning as potential teacher guidelines. Our findings provide insights into using advanced, multimodal techniques for automated classroom observation, aiming to foster teacher training through frequent and valuable feedback.

2024-07-04

2404.15324

Jos\'e L. Risco-Mart\'in

Jos\'e L. Risco-Mart\'in, Ignacio-Iker Prado-Rujas, Javier Campoy, Mar\'ia S. P\'erez and Katzalin Olcoz

Advanced simulation-based predictive modelling for solar irradiance sensor farms

Journal of Simulation, pp. 1-18, 2024

10.1080/17477778.2024.2333775

eess.SP cs.AI cs.SY eess.SY

http://creativecommons.org/licenses/by/4.0/

As solar power continues to grow and replace traditional energy sources, the need for reliable forecasting models becomes increasingly important to ensure the stability and efficiency of the grid. However, the management of these models still needs to be improved, and new tools and technologies are required to handle the deployment and control of solar facilities. This work introduces a novel framework named Cloud-based Analysis and Integration for Data Efficiency (CAIDE), designed for real-time monitoring, management, and forecasting of solar irradiance sensor farms. CAIDE is designed to manage multiple sensor farms simultaneously while improving predictive models in real-time using well-grounded Modeling and Simulation (M&S) methodologies. The framework leverages Model Based Systems Engineering (MBSE) and an Internet of Things (IoT) infrastructure to support the deployment and analysis of solar plants in dynamic environments. The system can adapt and re-train the model when given incorrect results, ensuring that forecasts remain accurate and up-to-date. Furthermore, CAIDE can be executed in sequential, parallel, and distributed architectures, assuring scalability. The effectiveness of CAIDE is demonstrated in a complex scenario composed of several solar irradiance sensor farms connected to a centralized management system. Our results show that CAIDE is scalable and effective in managing and forecasting solar power production while improving the accuracy of predictive models in real time. The framework has important implications for the deployment of solar plants and the future of renewable energy sources.

2024-04-25

2404.15814

Hyunsu Kim

Hyunsu Kim, Jongmin Yoon, and Juho Lee

Fast Ensembling with Diffusion Schr\"odinger Bridge

ICLR 2024

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Deep Ensemble (DE) approach is a straightforward technique used to enhance the performance of deep neural networks by training them from different initial points, converging towards various local optima. However, a limitation of this methodology lies in its high computational overhead for inference, arising from the necessity to store numerous learned parameters and execute individual forward passes for each parameter during the inference stage. We propose a novel approach called Diffusion Bridge Network (DBN) to address this challenge. Based on the theory of the Schr\"odinger bridge, this method directly learns to simulate an Stochastic Differential Equation (SDE) that connects the output distribution of a single ensemble member to the output distribution of the ensembled model, allowing us to obtain ensemble prediction without having to invoke forward pass through all the ensemble models. By substituting the heavy ensembles with this lightweight neural network constructing DBN, we achieved inference with reduced computational cost while maintaining accuracy and uncertainty scores on benchmark datasets such as CIFAR-10, CIFAR-100, and TinyImageNet. Our implementation is available at https://github.com/kim-hyunsu/dbn.

2024-04-25

2404.16042

Romy M\"uller

Romy M\"uller

How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps

International Journal of Human-Computer Interaction (2024) 1-32

10.1080/10447318.2024.2381929

cs.HC cs.AI

http://creativecommons.org/licenses/by/4.0/

Saliency maps can explain how deep neural networks classify images. But are they actually useful for humans? The present systematic review of 68 user studies found that while saliency maps can enhance human performance, null effects or even costs are quite common. To investigate what modulates these effects, the empirical outcomes were organised along several factors related to the human tasks, AI performance, XAI methods, images to be classified, human participants and comparison conditions. In image-focused tasks, benefits were less common than in AI-focused tasks, but the effects depended on the specific cognitive requirements. Moreover, benefits were usually restricted to incorrect AI predictions in AI-focused tasks but to correct ones in image-focused tasks. XAI-related factors had surprisingly little impact. The evidence was limited for image- and human-related factors and the effects were highly dependent on the comparison conditions. These findings may support the design of future user studies.

2024-08-20

2404.16047

Nanna Inie

Nanna Inie, Stefania Druga, Peter Zukerman, Emily M. Bender

From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?

Accepted to FAccT 2024. arXiv admin note: text overlap with arXiv:2403.05957

FAccT 2024

10.1145/3630106.3659040

cs.HC cs.AI

http://creativecommons.org/licenses/by/4.0/

This paper investigates the influence of anthropomorphized descriptions of so-called "AI" (artificial intelligence) systems on people's self-assessment of trust in the system. Building on prior work, we define four categories of anthropomorphization (1. Properties of a cognizer, 2. Agency, 3. Biological metaphors, and 4. Properties of a communicator). We use a survey-based approach (n=954) to investigate whether participants are likely to trust one of two (fictitious) "AI" systems by randomly assigning people to see either an anthropomorphized or a de-anthropomorphized description of the systems. We find that participants are no more likely to trust anthropomorphized over de-anthropmorphized product descriptions overall. The type of product or system in combination with different anthropomorphic categories appears to exert greater influence on trust than anthropomorphizing language alone, and age is the only demographic factor that significantly correlates with people's preference for anthropomorphized or de-anthropomorphized descriptions. When elaborating on their choices, participants highlight factors such as lesser of two evils, lower or higher stakes contexts, and human favoritism as driving motivations when choosing between product A and B, irrespective of whether they saw an anthropomorphized or a de-anthropomorphized description of the product. Our results suggest that "anthropomorphism" in "AI" descriptions is an aggregate concept that may influence different groups differently, and provide nuance to the discussion of whether anthropomorphization leads to higher trust and over-reliance by the general public in systems sold as "AI".

2024-06-10

2404.16074

Md Shajalal

Md Shajalal, Alexander Boden, Gunnar Stevens, Delong Du, Dean-Robin Kern

Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments

This is the pre-print version of our accepted paper at the 2nd World Conference on eXplainable Artificial Intelligence (xAI2024), which will be held in Valletta, Malta in 17-19 July, 2024

Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2156. Springer, Cham

10.1007/978-3-031-63803-9_23

cs.HC cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models' decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.

2024-07-30

2404.16104

David Doukhan

Albert Rilliard, David Doukhan, R\'emi Uro, Simon Devauchelle

Evolution of Voices in French Audiovisual Media Across Genders and Age in a Diachronic Perspective

5 pages, 2 figures, keywords:, Gender, Diachrony, Vocal Tract Resonance, Vocal register, Broadcast speech

Radek Skarnitzl & Jan Vol\'in (Eds.), Proceedings of the 20th International Congress of Phonetic Sciences (ICPhS), Prague 2023, pp. 753-757. Guarant International. ISBN 978-80-908 114-2-3

eess.AS cs.CL cs.SD

http://creativecommons.org/licenses/by-nc-nd/4.0/

We present a diachronic acoustic analysis of the voice of 1023 speakers from French media archives. The speakers are spread across 32 categories based on four periods (years 1955/56, 1975/76, 1995/96, 2015/16), four age groups (20-35; 36-50; 51-65, >65), and two genders. The fundamental frequency ($F_0$) and the first four formants (F1-4) were estimated. Procedures used to ensure the quality of these estimations on heterogeneous data are described. From each speaker's $F_0$ distribution, the base-$F_0$ value was calculated to estimate the register. Average vocal tract length was estimated from formant frequencies. Base-$F_0$ and vocal tract length were fit by linear mixed models to evaluate how they may have changed across time periods and genders, corrected for age effects. Results show an effect of the period with a tendency to lower voices, independently of gender. A lowering of pitch is observed with age for female but not male speakers.

2024-04-26

2404.16218

Julian Stier

Simon Neumeyer, Julian Stier, Michael Granitzer

Efficient NAS with FaDE on Hierarchical Spaces

Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14642. Springer, Cham

10.1007/978-3-031-58553-1_13

cs.NE cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Neural architecture search (NAS) is a challenging problem. Hierarchical search spaces allow for cheap evaluations of neural network sub modules to serve as surrogate for architecture evaluations. Yet, sometimes the hierarchy is too restrictive or the surrogate fails to generalize. We present FaDE which uses differentiable architecture search to obtain relative performance predictions on finite regions of a hierarchical NAS space. The relative nature of these ranks calls for a memory-less, batch-wise outer search algorithm for which we use an evolutionary algorithm with pseudo-gradient descent. FaDE is especially suited on deep hierarchical, respectively multi-cell search spaces, which it can explore by linear instead of exponential cost and therefore eliminates the need for a proxy search space. Our experiments show that firstly, FaDE-ranks on finite regions of the search space correlate with corresponding architecture performances and secondly, the ranks can empower a pseudo-gradient evolutionary search on the complete neural architecture search space.

2024-04-26

2404.16409

Nicolas Audebert

Aimi Okabayashi (IRISA, OBELIX), Nicolas Audebert (CEDRIC - VERTIGO, CNAM, LaSTIG, IGN), Simon Donike (IPL), Charlotte Pelletier (OBELIX, IRISA)

Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series

EARTHVISION 2024 IEEE/CVF CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Jun 2024, Seattle, United States

cs.CV eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images. Super-resolution is often advanced as a way to get the best of both worlds. In this work, we investigate multi-image super-resolution of satellite image time series, i.e. how multiple images of the same area acquired at different dates can help reconstruct a higher resolution observation. In particular, we extend state-of-the-art deep single and multi-image super-resolution algorithms, such as SRDiff and HighRes-net, to deal with irregularly sampled Sentinel-2 time series. We introduce BreizhSR, a new dataset for 4x super-resolution of Sentinel-2 time series using very high-resolution SPOT-6 imagery of Brittany, a French region. We show that using multiple images significantly improves super-resolution performance, and that a well-designed temporal positional encoding allows us to perform super-resolution for different times of the series. In addition, we observe a trade-off between spectral fidelity and perceptual quality of the reconstructed HR images, questioning future directions for super-resolution of Earth Observation data.

2024-04-26

2404.16442

Andreas Fischer

Zineddine Bettouche, Anas Safi, Andreas Fischer

Contextual Categorization Enhancement through LLMs Latent-Space

Fifteenth International Conference on Computational Logics, Algebras, Programming, Tools, and Benchmarking (COMPUTATION TOOLS 2024), ISSN: 2308-4170

cs.CL cs.AI

http://creativecommons.org/licenses/by-sa/4.0/

Managing the semantic quality of the categorization in large textual datasets, such as Wikipedia, presents significant challenges in terms of complexity and cost. In this paper, we propose leveraging transformer models to distill semantic information from texts in the Wikipedia dataset and its associated categories into a latent space. We then explore different approaches based on these encodings to assess and enhance the semantic identity of the categories. Our graphical approach is powered by Convex Hull, while we utilize Hierarchical Navigable Small Worlds (HNSWs) for the hierarchical approach. As a solution to the information loss caused by the dimensionality reduction, we modulate the following mathematical solution: an exponential decay function driven by the Euclidean distances between the high-dimensional encodings of the textual categories. This function represents a filter built around a contextual category and retrieves items with a certain Reconsideration Probability (RP). Retrieving high-RP items serves as a tool for database administrators to improve data groupings by providing recommendations and identifying outliers within a contextual framework.

2024-04-26

2404.16547

Giampiero Salvi

Giampiero Salvi

Developing Acoustic Models for Automatic Speech Recognition in Swedish

16 pages, 7 figures

European Student Journal of Language and Speech, 1999

eess.AS cs.AI cs.SD

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish. This is done employing hidden Markov models and using the SpeechDat database to train their parameters. Acoustic modeling has been worked out at a phonetic level, allowing general speech recognition applications, even though a simplified task (digits and natural number recognition) has been considered for model evaluation. Different kinds of phone models have been tested, including context independent models and two variations of context dependent models. Furthermore many experiments have been done with bigram language models to tune some of the system parameters. System performance over various speaker subsets with different sex, age and dialect has also been examined. Results are compared to previous similar studies showing a remarkable improvement.

2024-04-26

2404.16558

Leandro Di Bella

Leandro Di Bella, Yangxintong Lyu, Adrian Munteanu

DeepKalPose: An Enhanced Deep-Learning Kalman Filter for Temporally Consistent Monocular Vehicle Pose Estimation

4 pages, 3 Figures, published to IET Electronic Letters

Electronics Letters (ISSN: 00135194), jaar: 2024, volume: 60, nummer: 8, startpagina: ?

10.1049/ell2.13191

cs.CV cs.AI cs.RO

http://creativecommons.org/licenses/by-nc-nd/4.0/

This paper presents DeepKalPose, a novel approach for enhancing temporal consistency in monocular vehicle pose estimation applied on video through a deep-learning-based Kalman Filter. By integrating a Bi-directional Kalman filter strategy utilizing forward and backward time-series processing, combined with a learnable motion model to represent complex motion patterns, our method significantly improves pose accuracy and robustness across various conditions, particularly for occluded or distant vehicles. Experimental validation on the KITTI dataset confirms that DeepKalPose outperforms existing methods in both pose accuracy and temporal consistency.

2024-04-26

2404.16561

Haonan Wang

Ruiyang Wang, Haonan Wang, Junfeng Sun, Mingjia Zhao, Meng Liu

Research on geometric figure classification algorithm based on Deep Learning

6 pages,9 figures

Scientific Journal of Intelligent Systems Research,Volume 4 Issue 6, 2022

ISSN: 2664-9640

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In recent years, with the rapid development of computer information technology, the development of artificial intelligence has been accelerating. The traditional geometry recognition technology is relatively backward and the recognition rate is low. In the face of massive information database, the traditional algorithm model inevitably has the problems of low recognition accuracy and poor performance. Deep learning theory has gradually become a very important part of machine learning. The implementation of convolutional neural network (CNN) reduces the difficulty of graphics generation algorithm. In this paper, using the advantages of lenet-5 architecture sharing weights and feature extraction and classification, the proposed geometric pattern recognition algorithm model is faster in the training data set. By constructing the shared feature parameters of the algorithm model, the cross-entropy loss function is used in the recognition process to improve the generalization of the model and improve the average recognition accuracy of the test data set.

2024-04-26

2404.16954

Harit Vishwakarma

Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak

Taming False Positives in Out-of-Distribution Detection with Human Feedback

Appeared in the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024)

PMLR 238:1486-1494, 2024

cs.LG cs.AI stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Robustness to out-of-distribution (OOD) samples is crucial for safely deploying machine learning models in the open world. Recent works have focused on designing scoring functions to quantify OOD uncertainty. Setting appropriate thresholds for these scoring functions for OOD detection is challenging as OOD samples are often unavailable up front. Typically, thresholds are set to achieve a desired true positive rate (TPR), e.g., $95\%$ TPR. However, this can lead to very high false positive rates (FPR), ranging from 60 to 96\%, as observed in the Open-OOD benchmark. In safety-critical real-life applications, e.g., medical diagnosis, controlling the FPR is essential when dealing with various OOD samples dynamically. To address these challenges, we propose a mathematically grounded OOD detection framework that leverages expert feedback to \emph{safely} update the threshold on the fly. We provide theoretical results showing that it is guaranteed to meet the FPR constraint at all times while minimizing the use of human feedback. Another key feature of our framework is that it can work with any scoring function for OOD uncertainty quantification. Empirical evaluation of our system on synthetic and benchmark OOD datasets shows that our method can maintain FPR at most $5\%$ while maximizing TPR.

2024-04-29

2404.17027

Sudha Rao

Xiangyu Peng, Jessica Quaye, Sudha Rao, Weijia Xu, Portia Botchway, Chris Brockett, Nebojsa Jojic, Gabriel DesGarennes, Ken Lobb, Michael Xu, Jorge Leandro, Claire Jin, Bill Dolan

Player-Driven Emergence in LLM-Driven Game Narrative

Accepted at IEEE Conference on Games 2024

IEEE Conference on Games 2024

cs.CL cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We explore how interaction with large language models (LLMs) can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives. Our testbed is a text-adventure game in which players attempt to solve a mystery under a fixed narrative premise, but can freely interact with non-player characters generated by GPT-4, a large language model. We recruit 28 gamers to play the game and use GPT-4 to automatically convert the game logs into a node-graph representing the narrative in the player's gameplay. We find that through their interactions with the non-deterministic behavior of the LLM, players are able to discover interesting new emergent nodes that were not a part of the original narrative but have potential for being fun and engaging. Players that created the most emergent nodes tended to be those that often enjoy games that facilitate discovery, exploration and experimentation.

2024-06-05

2404.17126

Hai Siong Tan

Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth

Deep Evidential Learning for Radiotherapy Dose Prediction

28 pages

Computers in Biology and Medicine, Vol. 182, Nov 2024, 109172

10.1016/j.compbiomed.2024.109172

cs.LG cs.AI eess.IV physics.med-ph

http://creativecommons.org/licenses/by/4.0/

In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training. This was achieved only after reformulating the original loss function for a stable implementation. We found that (i)epistemic uncertainty was highly correlated with prediction errors, with various association indices comparable or stronger than those for Monte-Carlo Dropout and Deep Ensemble methods, (ii)the median error varied with uncertainty threshold much more linearly for epistemic uncertainty in Deep Evidential Learning relative to these other two conventional frameworks, indicative of a more uniformly calibrated sensitivity to model errors, (iii)relative to epistemic uncertainty, aleatoric uncertainty demonstrated a more significant shift in its distribution in response to Gaussian noise added to CT intensity, compatible with its interpretation as reflecting data noise. Collectively, our results suggest that Deep Evidential Learning is a promising approach that can endow deep-learning models in radiotherapy dose prediction with statistical robustness. Towards enhancing its clinical relevance, we demonstrate how we can use such a model to construct the predicted Dose-Volume-Histograms' confidence intervals.

2024-09-24

2404.17148

Xiongjun Guan

Xiongjun Guan, Yongjie Duan, Jianjiang Feng, Jie Zhou

Direct Regression of Distortion Field from a Single Fingerprint Image

2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates, 2022, pp. 1-8

10.1109/IJCB54206.2022.10007981

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses. Considering the limited number and variety of distorted fingerprints in the existing public dataset, we collected more distorted fingerprints with diverse finger poses and distortion patterns as a new database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.

2024-04-29

2404.17149

Xiongjun Guan

Xiongjun Guan, Jianjiang Feng, Jie Zhou

Pose-Specific 3D Fingerprint Unfolding

15th Chinese Conference on Biometric Recognition (CCBR), Shanghai, China, 2021, pp. 185-194

10.1007/978-3-030-86608-2_21

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In order to make 3D fingerprints compatible with traditional 2D flat fingerprints, a common practice is to unfold the 3D fingerprint into a 2D rolled fingerprint, which is then matched with the flat fingerprints by traditional 2D fingerprint recognition algorithms. The problem with this method is that there may be large elastic deformation between the unfolded rolled fingerprint and flat fingerprint, which affects the recognition rate. In this paper, we propose a pose-specific 3D fingerprint unfolding algorithm to unfold the 3D fingerprint using the same pose as the flat fingerprint. Our experiments show that the proposed unfolding algorithm improves the compatibility between 3D fingerprint and flat fingerprint and thus leads to higher genuine matching scores.

2024-04-29

2404.17194

Hailay Kidu Teklehaymanot

Hailay Teklehaymanot, Dren Fazlija, Niloy Ganguly, Gourab K. Patro, Wolfgang Nejdl

TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya

9 pages,3 figures, 7 tables,2 listings

LREC-COLING 2024

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources.This study initially explores the feasibility of using machine translation (MT) to convert an existing dataset into a Tigrinya dataset in SQuAD format. As a result, we present TIGQA, an expert annotated educational dataset consisting of 2.68K question-answer pairs covering 122 diverse topics such as climate, water, and traffic. These pairs are from 537 context paragraphs in publicly accessible Tigrinya and Biology books. Through comprehensive analyses, we demonstrate that the TIGQA dataset requires skills beyond simple word matching, requiring both single-sentence and multiple-sentence inference abilities. We conduct experiments using state-of-the art MRC methods, marking the first exploration of such models on TIGQA. Additionally, we estimate human performance on the dataset and juxtapose it with the results obtained from pretrained models.The notable disparities between human performance and best model performance underscore the potential for further enhancements to TIGQA through continued research. Our dataset is freely accessible via the provided link to encourage the research community to address the challenges in the Tigrinya MRC.

2024-04-29

2404.17273

Xuri Ge

Xuri Ge, Songpei Xu, Fuhai Chen, Jie Wang, Guoxin Wang, Shan An, Joemon M. Jose

3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting

Accepted Information Processing and Management (IP&M), 10 pages, 9 figures and 8 tables

Information Processing & Management, Volume 61, Issue 4, July 2024, 103716

10.1016/j.ipm.2024.103716

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, we propose a novel visual Semantic-Spatial Self-Highlighting Network (termed 3SHNet) for high-precision, high-efficiency and high-generalization image-sentence retrieval. 3SHNet highlights the salient identification of prominent objects and their spatial locations within the visual modality, thus allowing the integration of visual semantics-spatial interactions and maintaining independence between two modalities. This integration effectively combines object regions with the corresponding semantic and position layouts derived from segmentation to enhance the visual representation. And the modality-independence guarantees efficiency and generalization. Additionally, 3SHNet utilizes the structured contextual visual scene information from segmentation to conduct the local (region-based) or global (grid-based) guidance and achieve accurate hybrid-level retrieval. Extensive experiments conducted on MS-COCO and Flickr30K benchmarks substantiate the superior performances, inference efficiency and generalization of the proposed 3SHNet when juxtaposed with contemporary state-of-the-art methodologies. Specifically, on the larger MS-COCO 5K test set, we achieve 16.3%, 24.8%, and 18.3% improvements in terms of rSum score, respectively, compared with the state-of-the-art methods using different image representations, while maintaining optimal retrieval efficiency. Moreover, our performance on cross-dataset generalization improves by 18.6%. Data and code are available at https://github.com/XuriGe1995/3SHNet.

2024-04-29

2404.17357

Yushen Xu

Yushen Xu, Xiaosong Li, Yuchan Jie and Haishu Tan

Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion Model

Accepted by MICCAI 2024

International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024: 635-645

10.1007/978-3-031-72104-5_61

eess.IV cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal technique, can provide a more comprehensive view of the lesions, aiding physicians in evaluating the disease's shape, location, and biological activity. However, due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited, leading to sub-optimal fusion performance, and affecting the depth of image analysis by the physician. Thus, there is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information. Although current image processing methods can effectively address image fusion and super-resolution individually, solving both problems synchronously remains extremely challenging. In this paper, we propose TFS-Diff, a simultaneously realize tri-modal medical image fusion and super-resolution model. Specially, TFS-Diff is based on the diffusion model generation of a random iterative denoising process. We also develop a simple objective function and the proposed fusion super-resolution loss, effectively evaluates the uncertainty in the fusion and ensures the stability of the optimization process. And the channel attention module is proposed to effectively integrate key information from different modalities for clinical diagnosis, avoiding information loss caused by multiple image processing. Extensive experiments on public Harvard datasets show that TFS-Diff significantly surpass the existing state-of-the-art methods in both quantitative and visual evaluations. Code is available at https://github.com/XylonXu01/TFS-Diff.

2024-10-16

2404.17427

Moussa Kassem Sbeyti

Moussa Kassem Sbeyti, Michelle Karg, Christian Wirth, Nadja Klein, Sahin Albayrak

Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection

Accepted with an oral presentation at UAI 2024

The 40th Conference on Uncertainty in Artificial Intelligence, 2024, https://openreview.net/forum?id=HuibNFkaoi

cs.CV

http://creativecommons.org/licenses/by/4.0/

Object detectors in real-world applications often fail to detect objects due to varying factors such as weather conditions and noisy input. Therefore, a process that mitigates false detections is crucial for both safety and accuracy. While uncertainty-based thresholding shows promise, previous works demonstrate an imperfect correlation between uncertainty and detection errors. This hinders ideal thresholding, prompting us to further investigate the correlation and associated cost with different types of uncertainty. We therefore propose a cost-sensitive framework for object detection tailored to user-defined budgets on the two types of errors, missing and false detections. We derive minimum thresholding requirements to prevent performance degradation and define metrics to assess the applicability of uncertainty for failure recognition. Furthermore, we automate and optimize the thresholding process to maximize the failure recognition rate w.r.t. the specified budget. Evaluation on three autonomous driving datasets demonstrates that our approach significantly enhances safety, particularly in challenging scenarios. Leveraging localization aleatoric uncertainty and softmax-based entropy only, our method boosts the failure recognition rate by 36-60\% compared to conventional approaches. Code is available at https://mos-ks.github.io/publications.

2024-06-14

2404.17475

Van Bach Nguyen

Van Bach Nguyen, J\"org Schl\"otterer, Christin Seifert

CEval: A Benchmark for Evaluating Counterfactual Text Generation

INLG 2024

cs.CL cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Counterfactual text generation aims to minimally change a text, such that it is classified differently. Judging advancements in method development for counterfactual text generation is hindered by a non-uniform usage of data sets and metrics in related work. We propose CEval, a benchmark for comparing counterfactual text generation methods. CEval unifies counterfactual and text quality metrics, includes common counterfactual datasets with human annotations, standard baselines (MICE, GDBA, CREST) and the open-source language model LLAMA-2. Our experiments found no perfect method for generating counterfactual text. Methods that excel at counterfactual metrics often produce lower-quality text while LLMs with simple prompts generate high-quality text but struggle with counterfactual criteria. By making CEval available as an open-source Python library, we encourage the community to contribute more methods and maintain consistent evaluation in future work.

2024-08-14

2404.17488

Ingeborg Beckers

Danja Brandt and Martin Tschaikner, Teodor Chiaburu, Henning Schmidt, Ilona Schrimpf, Alexandra Stadel and Ingeborg E. Beckers, Frank Hau{\ss}er

Low Cost Machine Vision for Insect Classification

Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer

10.1007/978-3-031-47715-7_2

cs.CV cs.LG

http://creativecommons.org/licenses/by-sa/4.0/

Preserving the number and diversity of insects is one of our society's most important goals in the area of environmental sustainability. A prerequisite for this is a systematic and up-scaled monitoring in order to detect correlations and identify countermeasures. Therefore, automatized monitoring using live traps is important, but so far there is no system that provides image data of sufficient detailed information for entomological classification. In this work, we present an imaging method as part of a multisensor system developed as a low-cost, scalable, open-source system that is adaptable to classical trap types. The image quality meets the requirements needed for classification in the taxonomic tree. Therefore, illumination and resolution have been optimized and motion artefacts have been suppressed. The system is evaluated exemplarily on a dataset consisting of 16 insect species of the same as well as different genus, family and order. We demonstrate that standard CNN-architectures like ResNet50 (pretrained on iNaturalist data) or MobileNet perform very well for the prediction task after re-training. Smaller custom made CNNs also lead to promising results. Classification accuracy of $>96\%$ has been achieved. Moreover, it was proved that image cropping of insects is necessary for classification of species with high inter-class similarity.

2024-04-29

2404.17552

David Doukhan

R\'emi Uro, David Doukhan, Albert Rilliard, La\"etitia Larcher, Anissa-Claire Adgharouamane, Marie Tahon, Antoine Laurent

A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification

Keywords:, semi-automatic processing, corpus creation, diarization, speaker identification, gender-balanced, age-balanced, speaker corpus, diachrony

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 3271-3280, Marseille, 20-25 June 2022. European Language Resources Association (ELRA)

eess.AS cs.CL cs.DL cs.LG cs.SD

http://creativecommons.org/licenses/by-nc-nd/4.0/

This paper presents a semi-automatic approach to create a diachronic corpus of voices balanced for speaker's age, gender, and recording period, according to 32 categories (2 genders, 4 age ranges and 4 recording periods). Corpora were selected at French National Institute of Audiovisual (INA) to obtain at least 30 speakers per category (a total of 960 speakers; only 874 have be found yet). For each speaker, speech excerpts were extracted from audiovisual documents using an automatic pipeline consisting of speech detection, background music and overlapped speech removal and speaker diarization, used to present clean speaker segments to human annotators identifying target speakers. This pipeline proved highly effective, cutting down manual processing by a factor of ten. Evaluation of the quality of the automatic processing and of the final output is provided. It shows the automatic processing compare to up-to-date process, and that the output provides high quality speech for most of the selected excerpts. This method shows promise for creating large corpora of known target speakers.

2024-04-29

2404.17591

Peibo Li

Peibo Li, Maarten de Rijke, Hao Xue, Shuang Ao, Yang Song and Flora D. Salim

Large Language Models for Next Point-of-Interest Recommendation

In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Association for Computing Machinery, New York, NY, USA, 1463-1472

10.1145/3626772.3657840

cs.IR cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is often used for the next POI recommendation task, comes with challenges. One frequently disregarded challenge is how to effectively use the abundant contextual information present in LBSN data. Previous methods are limited by their numerical nature and fail to address this challenge. In this paper, we propose a framework that uses pretrained Large Language Models (LLMs) to tackle this challenge. Our framework allows us to preserve heterogeneous LBSN data in its original format, hence avoiding the loss of contextual information. Furthermore, our framework is capable of comprehending the inherent meaning of contextual information due to the inclusion of commonsense knowledge. In experiments, we test our framework on three real-world LBSN datasets. Our results show that the proposed framework outperforms the state-of-the-art models in all three datasets. Our analysis demonstrates the effectiveness of the proposed framework in using contextual information as well as alleviating the commonly encountered cold-start and short trajectory problems.

2024-08-02

2404.17593

Sefika Efeoglu

Sefika Efeoglu

A Continual Relation Extraction Approach for Knowledge Graph Completeness

Published at TPDL 2022

TPDL 2022: 26th International Conference on Theory and Practice of Digital Libraries, 20-23 September 2022, Padua, Italy

cs.DL cs.AI

http://creativecommons.org/licenses/by/4.0/

Representing unstructured data in a structured form is most significant for information system management to analyze and interpret it. To do this, the unstructured data might be converted into Knowledge Graphs, by leveraging an information extraction pipeline whose main tasks are named entity recognition and relation extraction. This thesis aims to develop a novel continual relation extraction method to identify relations (interconnections) between entities in a data stream coming from the real world. Domain-specific data of this thesis is corona news from German and Austrian newspapers.

2024-04-30

2404.17610

Xiongjun Guan

Xiongjun Guan, Yongjie Duan, Jianjiang Feng, Jie Zhou

Regression of Dense Distortion Field from a Single Fingerprint Image

arXiv admin note: text overlap with arXiv:2404.17148

IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4377-4390, 2023

10.1109/TIFS.2023.3296310

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses and distortion patterns. We conducted experiments on FVC2004 DB1\_A, expanded Tsinghua Distorted Fingerprint database (with additional distorted fingerprints in diverse finger poses and distortion patterns) and a latent fingerprint database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.

2024-04-30

2404.17617

Yuhang Zhang

Tao Liu, Yuhang Zhang, Zhu Feng, Zhiqin Yang, Chen Xu, Dapeng Man, Wu Yang

Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning

Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(19): 21359-21367

cs.CR cs.AI cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Backdoors on federated learning will be diluted by subsequent benign updates. This is reflected in the significant reduction of attack success rate as iterations increase, ultimately failing. We use a new metric to quantify the degree of this weakened backdoor effect, called attack persistence. Given that research to improve this performance has not been widely noted,we propose a Full Combination Backdoor Attack (FCBA) method. It aggregates more combined trigger information for a more complete backdoor pattern in the global model. Trained backdoored global model is more resilient to benign updates, leading to a higher attack success rate on the test set. We test on three datasets and evaluate with two models across various settings. FCBA's persistence outperforms SOTA federated learning backdoor attacks. On GTSRB, postattack 120 rounds, our attack success rate rose over 50% from baseline. The core code of our method is available at https://github.com/PhD-TaoLiu/FCBA.

2024-04-30

2404.17820

Yuchun Wang

Yuchun Wang and Cheng Gong and Jianwei Gong and Peng Jia

Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation

Journal of Field Robotics,2024,1-22

10.1002/rob.22345

cs.RO cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multi-layer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a CNN-LSTM network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios.

2024-04-30

2404.17861

Yuval Haitman

Yuval Haitman, Oded Bialer

BoostRad: Enhancing Object Detection by Boosting Radar Reflections

WACV2024

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2024, pp. 1627-1636

10.1109/WACV57701.2024.00166

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

Automotive radars have an important role in autonomous driving systems. The main challenge in automotive radar detection is the radar's wide point spread function (PSF) in the angular domain that causes blurriness and clutter in the radar image. Numerous studies suggest employing an 'end-to-end' learning strategy using a Deep Neural Network (DNN) to directly detect objects from radar images. This approach implicitly addresses the PSF's impact on objects of interest. In this paper, we propose an alternative approach, which we term "Boosting Radar Reflections" (BoostRad). In BoostRad, a first DNN is trained to narrow the PSF for all the reflection points in the scene. The output of the first DNN is a boosted reflection image with higher resolution and reduced clutter, resulting in a sharper and cleaner image. Subsequently, a second DNN is employed to detect objects within the boosted reflection image. We develop a novel method for training the boosting DNN that incorporates domain knowledge of radar's PSF characteristics. BoostRad's performance is evaluated using the RADDet and CARRADA datasets, revealing its superiority over reference methods.

2024-04-30

2404.17865

Hao Sun

Zitong Zhang, Yang Liu, Hao Sun

Vision-based Discovery of Nonlinear Dynamics for 3D Moving Target

17 pages

IJCAI 2024

cs.CV cs.AI nlin.CD

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Data-driven discovery of governing equations has kindled significant interests in many science and engineering areas. Existing studies primarily focus on uncovering equations that govern nonlinear dynamics based on direct measurement of the system states (e.g., trajectories). Limited efforts have been placed on distilling governing laws of dynamics directly from videos for moving targets in a 3D space. To this end, we propose a vision-based approach to automatically uncover governing equations of nonlinear dynamics for 3D moving targets via raw videos recorded by a set of cameras. The approach is composed of three key blocks: (1) a target tracking module that extracts plane pixel motions of the moving target in each video, (2) a Rodrigues' rotation formula-based coordinate transformation learning module that reconstructs the 3D coordinates with respect to a predefined reference point, and (3) a spline-enhanced library-based sparse regressor that uncovers the underlying governing law of dynamics. This framework is capable of effectively handling the challenges associated with measurement data, e.g., noise in the video, imprecise tracking of the target that causes data missing, etc. The efficacy of our method has been demonstrated through multiple sets of synthetic videos considering different nonlinear dynamics.

2024-04-30

2404.17877

Yubo Feng

Yubo Feng, Lishuang Li, Yi Xiang, Xueyang Qin

PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning

NLPCC 2023 Best Student Paper

Natural Language Processing and Chinese Computing (NLPCC 2023)

10.1007/978-3-031-44693-1_21

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The representation of events in text plays a significant role in various NLP tasks. Recent research demonstrates that contrastive learning has the ability to improve event comprehension capabilities of Pre-trained Language Models (PLMs) and enhance the performance of event representation learning. However, the efficacy of event representation learning based on contrastive learning and PLMs is limited by the short length of event texts. The length of event texts differs significantly from the text length used in the pre-training of PLMs. As a result, there is inconsistency in the distribution of text length between pre-training and event representation learning, which may undermine the learning process of event representation based on PLMs. In this study, we present PromptCL, a novel framework for event representation learning that effectively elicits the capabilities of PLMs to comprehensively capture the semantics of short event texts. PromptCL utilizes a Prompt template borrowed from prompt learning to expand the input text during Contrastive Learning. This helps in enhancing the event representation learning by providing a structured outline of the event components. Moreover, we propose Subject-Predicate-Object (SPO) word order and Event-oriented Masked Language Modeling (EventMLM) to train PLMs to understand the relationships between event components. Our experimental results demonstrate that PromptCL outperforms state-of-the-art baselines on event related tasks. Additionally, we conduct a thorough analysis and demonstrate that using a prompt results in improved generalization capabilities for event representations. Our code will be available at https://github.com/YuboFeng2023/PromptCL.

2024-04-30

2404.17892

Lindsey Kerbel

Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco

Shared learning of powertrain control policies for vehicle fleets

Elsevier Applied Energy Volume 365, 1 July 2024, 123217

10.1016/j.apenergy.2024.123217

eess.SY cs.AI cs.LG cs.SY

http://creativecommons.org/licenses/by-nc-nd/4.0/

Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-the-field learning of powertrain control policies that optimize fuel economy and other performance metrics. Indeed, they have shown great potential in this regard for individual vehicles on specific routes or drive cycles. However, for fleets of vehicles that must service a distribution of routes, DRL approaches struggle with learning stability issues that result in high variances and challenge their practical deployment. In this paper, we present a novel framework for shared learning among a fleet of vehicles through the use of a distilled group policy as the knowledge sharing mechanism for the policy learning computations at each vehicle. We detail the mathematical formulation that makes this possible. Several scenarios are considered to analyze the functionality, performance, and computational scalability of the framework with fleet size. Comparisons of the cumulative performance of fleets using our proposed shared learning approach with a baseline of individual learning agents and another state-of-the-art approach with a centralized learner show clear advantages to our approach. For example, we find a fleet average asymptotic improvement of 8.5 percent in fuel economy compared to the baseline while also improving on the metrics of acceleration error and shifting frequency for fleets serving a distribution of suburban routes. Furthermore, we include demonstrative results that show how the framework reduces variance within a fleet and also how it helps individual agents adapt better to new routes.

2024-04-30

2404.17900

Baihong Lin

Di Wu, Shicai Fan, Xue Zhou, Li Yu, Yuzhong Deng, Jianxiao Zou, Baihong Lin

Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling

International Joint Conference on Artificial Intelligence 2024

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect and locate anomalies. Recently, diffusion models have shown promising applications for anomaly detection due to their powerful generative ability. However, these models lack strict mathematical support for normal image reconstruction and unexpectedly suffer from low reconstruction quality. To address these issues, this paper proposes a novel and highly-interpretable method named Masked Diffusion Posterior Sampling (MDPS). In MDPS, the problem of normal image reconstruction is mathematically modeled as multiple diffusion posterior sampling for normal images based on the devised masked noisy observation model and the diffusion-based normal image prior under Bayesian framework. Using a metric designed from pixel-level and perceptual-level perspectives, MDPS can effectively compute the difference map between each normal posterior sample and the given test image. Anomaly scores are obtained by averaging all difference maps for multiple posterior samples. Exhaustive experiments on MVTec and BTAD datasets demonstrate that MDPS can achieve state-of-the-art performance in normal image reconstruction quality as well as anomaly detection and localization.

2024-04-30

2404.17922

Laksh Nanwani

Laksh Nanwani, Kumaraditya Gupta, Aditya Mathur, Swayam Agrawal, A.H. Abdul Hafez, K. Madhava Krishna

Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIM

Advanced Robotics - Taylor and Francis - 2024

10.1080/01691864.2024.2395926

cs.CV cs.RO

http://creativecommons.org/licenses/by-sa/4.0/

Humans excel at forming mental maps of their surroundings, equipping them to understand object relationships and navigate based on language queries. Our previous work SI Maps [1] showed that having instance-level information and the semantic understanding of an environment helps significantly improve performance for language-guided tasks. We extend this instance-level approach to 3D while increasing the pipeline's robustness and improving quantitative and qualitative results. Our method leverages foundational models for object recognition, image segmentation, and feature extraction. We propose a representation that results in a 3D point cloud map with instance-level embeddings, which bring in the semantic understanding that natural language commands can query. Quantitatively, the work improves upon the success rate of language-guided tasks. At the same time, we qualitatively observe the ability to identify instances more clearly and leverage the foundational models and language and image-aligned embeddings to identify objects that, otherwise, a closed-set approach wouldn't be able to identify.

2024-08-30

2404.18094

Wenbin Wang

Wenbin Wang, Yang Song, Sanjay Jha

USAT: A Universal Speaker-Adaptive Text-to-Speech Approach

15 pages, 13 figures. Copyright has been transferred to IEEE

IEEE/ACM Transactions on Audio, Speech and Language Processing, 2024

10.1109/TASLP.2024.3393714

cs.SD cs.AI cs.CL eess.AS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Conventional text-to-speech (TTS) research has predominantly focused on enhancing the quality of synthesized speech for speakers in the training dataset. The challenge of synthesizing lifelike speech for unseen, out-of-dataset speakers, especially those with limited reference data, remains a significant and unresolved problem. While zero-shot or few-shot speaker-adaptive TTS approaches have been explored, they have many limitations. Zero-shot approaches tend to suffer from insufficient generalization performance to reproduce the voice of speakers with heavy accents. While few-shot methods can reproduce highly varying accents, they bring a significant storage burden and the risk of overfitting and catastrophic forgetting. In addition, prior approaches only provide either zero-shot or few-shot adaptation, constraining their utility across varied real-world scenarios with different demands. Besides, most current evaluations of speaker-adaptive TTS are conducted only on datasets of native speakers, inadvertently neglecting a vast portion of non-native speakers with diverse accents. Our proposed framework unifies both zero-shot and few-shot speaker adaptation strategies, which we term as "instant" and "fine-grained" adaptations based on their merits. To alleviate the insufficient generalization performance observed in zero-shot speaker adaptation, we designed two innovative discriminators and introduced a memory mechanism for the speech decoder. To prevent catastrophic forgetting and reduce storage implications for few-shot speaker adaptation, we designed two adapters and a unique adaptation procedure.

2024-04-30

2404.18183

Shuochen Bi

Shuochen Bi, Wenqing Bao

Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management

6 pages, 1 figure, 2 tables

International Journal of Global Economics and Management ISSN: 3005-9690 (Print), ISSN: 3005-8090 (Online) | Volume 2, Number 3, Year 2024

10.62051/IJGEM.v2n3.08

q-fin.RM cs.AI

http://creativecommons.org/licenses/by/4.0/

With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime monitoring and early warning, helping banks intervene before risks occur and reduce losses.

2024-04-30

2404.18206

Cuiwei Liu

Cuiwei Liu, Youzhi Jiang, Chong Du, and Zhaokui Li

Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation

published in Signal Processing 2024

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Skeleton-based action recognition is vital for comprehending human-centric videos and has applications in diverse domains. One of the challenges of skeleton-based action recognition is dealing with low-quality data, such as skeletons that have missing or inaccurate joints. This paper addresses the issue of enhancing action recognition using low-quality skeletons through a general knowledge distillation framework. The proposed framework employs a teacher-student model setup, where a teacher model trained on high-quality skeletons guides the learning of a student model that handles low-quality skeletons. To bridge the gap between heterogeneous high-quality and lowquality skeletons, we present a novel part-based skeleton matching strategy, which exploits shared body parts to facilitate local action pattern learning. An action-specific part matrix is developed to emphasize critical parts for different actions, enabling the student model to distill discriminative part-level knowledge. A novel part-level multi-sample contrastive loss achieves knowledge transfer from multiple high-quality skeletons to low-quality ones, which enables the proposed knowledge distillation framework to include training low-quality skeletons that lack corresponding high-quality matches. Comprehensive experiments conducted on the NTU-RGB+D, Penn Action, and SYSU 3D HOI datasets demonstrate the effectiveness of the proposed knowledge distillation framework.

2024-04-30

2404.18401

Lingbo Huang

Lingbo Huang, Yushi Chen, and Xin He

Spectral-Spatial Mamba for Hyperspectral Image Classification

23 pages

Remote Sens. 2024, 16, 2449

10.3390/rs16132449

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recently, deep learning models have achieved excellent performance in hyperspectral image (HSI) classification. Among the many deep models, Transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, Transformer has the problem of quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of Transformers. Therefore, in this paper, we make a preliminary attempt to apply the Mamba to HSI classification, leading to the proposed spectral-spatial Mamba (SS-Mamba). Specifically, the proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. Firstly, the token generation module converts any given HSI cube to spatial and spectral tokens as sequences. And then these tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB block consists of two basic mamba blocks and a spectral-spatial feature enhancement module. The spatial and spectral tokens are processed separately by the two basic mamba blocks, respectively. Besides, the feature enhancement module modulates spatial and spectral tokens using HSI sample's center region information. In this way, the spectral and spatial tokens cooperate with each other and achieve information fusion within each block. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results compared with the state-of-the-art methods. The Mamba-based method opens a new window for HSI classification.

2024-08-02

2404.18443

Ran Xu

Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May D. Wang, Joyce C. Ho, Chao Zhang, Carl Yang

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers

Accepted to EMNLP 2024. The model and data are uploaded to \url{https://github.com/ritaranx/BMRetriever}

EMNLP 2024

cs.CL cs.AI cs.IR q-bio.QM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources. We present BMRetriever, a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedical corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. Experiments on 5 biomedical tasks across 11 datasets verify BMRetriever's efficacy on various biomedical applications. BMRetriever also exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger, and the 2B variant matching the performance of models with over 5B parameters. The training data and model checkpoints are released at \url{https://huggingface.co/BMRetriever} to ensure transparency, reproducibility, and application to new domains.

2024-10-07

2404.18504

Ingeborg Beckers

Martin Tschaikner and Danja Brandt, Henning Schmidt, Felix Bie{\ss}mann, Teodor Chiaburu, Ilona Schrimpf, Thomas Schrimpf, Alexandra Stadel and Frank Hau{\ss}er and Ingeborg Beckers

Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta)

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, SPIE 12727 (2023) 1272702

10.1117/12.2679927

cs.LG cs.CV eess.SP

http://creativecommons.org/licenses/by-sa/4.0/

Insect populations are declining globally, making systematic monitoring essential for conservation. Most classical methods involve death traps and counter insect conservation. This paper presents a multisensor approach that uses AI-based data fusion for insect classification. The system is designed as low-cost setup and consists of a camera module and an optical wing beat sensor as well as environmental sensors to measure temperature, irradiance or daytime as prior information. The system has been tested in the laboratory and in the field. First tests on a small very unbalanced data set with 7 species show promising results for species classification. The multisensor system will support biodiversity and agriculture studies.

2024-04-30

2404.18876

Cristiano Bacelar De Oliveira

Cristiano B. de Oliveira, Joao C. Neves, Rafael O. Ribeiro and David Menotti

A Multilevel Strategy to Improve People Tracking in a Real-World Scenario

Accepted for presentation at the International Conference on Computer Vision Theory and Applications (VISAPP) 2024

Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, 2024

10.5220/0012460000003660

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The Pal\'acio do Planalto, office of the President of Brazil, was invaded by protesters on January 8, 2023. Surveillance videos taken from inside the building were subsequently released by the Brazilian Supreme Court for public scrutiny. We used segments of such footage to create the UFPR-Planalto801 dataset for people tracking and re-identification in a real-world scenario. This dataset consists of more than 500,000 images. This paper presents a tracking approach targeting this dataset. The method proposed in this paper relies on the use of known state-of-the-art trackers combined in a multilevel hierarchy to correct the ID association over the trajectories. We evaluated our method using IDF1, MOTA, MOTP and HOTA metrics. The results show improvements for every tracker used in the experiments, with IDF1 score increasing by a margin up to 9.5%.

2024-04-30

2404.18935

Sourabh Gothe Mr

Sourabh Vasant Gothe, Vibhav Agarwal, Sourav Ghosh, Jayesh Rajkumar Vachhani, Pranay Kashyap, Barath Raj Kandur Raja

What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection

Accepted in WACV-2024. Supplementary at https://openaccess.thecvf.com/content/WACV2024/supplemental/Gothe_Whats_in_the_WACV_2024_supplemental.pdf

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2024, pp. 6926-6935

10.1109/WACV57701.2024.00679

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

Generic Event Boundary Detection (GEBD) task aims to recognize generic, taxonomy-free boundaries that segment a video into meaningful events. Current methods typically involve a neural model trained on a large volume of data, demanding substantial computational power and storage space. We explore two pivotal questions pertaining to GEBD: Can non-parametric algorithms outperform unsupervised neural methods? Does motion information alone suffice for high performance? This inquiry drives us to algorithmically harness motion cues for identifying generic event boundaries in videos. In this work, we propose FlowGEBD, a non-parametric, unsupervised technique for GEBD. Our approach entails two algorithms utilizing optical flow: (i) Pixel Tracking and (ii) Flow Normalization. By conducting thorough experimentation on the challenging Kinetics-GEBD and TAPOS datasets, our results establish FlowGEBD as the new state-of-the-art (SOTA) among unsupervised methods. FlowGEBD exceeds the neural models on the Kinetics-GEBD dataset by obtaining an F1@0.05 score of 0.713 with an absolute gain of 31.7% compared to the unsupervised baseline and achieves an average F1 score of 0.623 on the TAPOS validation dataset.

2024-05-14

2404.19043

Hyunho Lee

Hyunho Lee, Wenwen Li

Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery

46 pages, 11 figures, 5 tables

Remote Sensing of Environment, 309, 114213

10.1016/j.rse.2024.114213

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Flood inundation mapping is a critical task for responding to the increasing risk of flooding linked to global warming. Significant advancements of deep learning in recent years have triggered its extensive applications, including flood inundation mapping. To cope with the time-consuming and labor-intensive data labeling process in supervised learning, deep active learning strategies are one of the feasible approaches. However, there remains limited exploration into the interpretability of how deep active learning strategies operate, with a specific focus on flood inundation mapping in the field of remote sensing. In this study, we introduce a novel framework of Interpretable Deep Active Learning for Flood inundation Mapping (IDAL-FIM), specifically in terms of class ambiguity of multi-spectral satellite images. In the experiments, we utilize Sen1Floods11 dataset, and adopt U-Net with MC-dropout. In addition, we employ five acquisition functions, which are the random, K-means, BALD, entropy, and margin acquisition functions. Based on the experimental results, we demonstrate that two proposed class ambiguity indices are effective variables to interpret the deep active learning by establishing statistically significant correlation with the predictive uncertainty of the deep learning model at the tile level. Then, we illustrate the behaviors of deep active learning through visualizing two-dimensional density plots and providing interpretations regarding the operation of deep active learning, in flood inundation mapping.

2024-05-29

2404.19094

Matteo Merler

Matteo Merler, Katsiaryna Haitsiukevich, Nicola Dainese and Pekka Marttinen

In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery

18 pages, 11 figures

ACL Student Research Workshop 2024

10.18653/v1/2024.acl-srw.49

cs.CL cs.LG

http://creativecommons.org/licenses/by/4.0/

State of the art Symbolic Regression (SR) methods currently build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. In this work, we introduce the first comprehensive framework that utilizes LLMs for the task of SR. We propose In-Context Symbolic Regression (ICSR), an SR method which iteratively refines a functional form with an LLM and determines its coefficients with an external optimizer. ICSR leverages LLMs' strong mathematical prior both to propose an initial set of possible functions given the observations and to refine them based on their errors. Our findings reveal that LLMs are able to successfully find symbolic equations that fit the given data, matching or outperforming the overall performance of the best SR baselines on four popular benchmarks, while yielding simpler equations with better out of distribution generalization.

2024-09-27

2404.19126

Christopher Kymn

Christopher J. Kymn, Sonia Mazelet, Annabel Ng, Denis Kleyko, Bruno A. Olshausen

Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks

9 pages, 5 figures

2024 Neuro Inspired Computational Elements Conference (NICE)

10.1109/NICE61972.2024.10549719

cs.CV cs.NE

http://creativecommons.org/licenses/by-sa/4.0/

We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequently factorized to parse scene content. The sparse feature representation is learned from image statistics via convolutional sparse coding, while scene parsing is performed by a resonator network. The integration of sparse coding with the resonator network increases the capacity of distributed representations and reduces collisions in the combinatorial search space during factorization. We find that for this problem the resonator network is capable of fast and accurate vector factorization, and we develop a confidence-based metric that assists in tracking the convergence of the resonator network.

2024-07-01