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2402.11571

Eric Nichols

Zining Wang and Paul Reisert and Eric Nichols and Randy Gomez

Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru

Accepted as Late Breaking Report (LBR) at the 19th Annual ACM/IEEE International Conference on Human Robot Interaction (HRI '24)

Companion of HRI '24, March 11-14, 2024, Boulder, CO, USA

10.1145/3610978.3640562

cs.RO cs.AI cs.CL

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

Social robots aim to establish long-term bonds with humans through engaging conversation. However, traditional conversational approaches, reliant on scripted interactions, often fall short in maintaining engaging conversations. This paper addresses this limitation by integrating large language models (LLMs) into social robots to achieve more dynamic and expressive conversations. We introduce a fully-automated conversation system that leverages LLMs to generate robot responses with expressive behaviors, congruent with the robot's personality. We incorporate robot behavior with two modalities: 1) a text-to-speech (TTS) engine capable of various delivery styles, and 2) a library of physical actions for the robot. We develop a custom, state-of-the-art emotion recognition model to dynamically select the robot's tone of voice and utilize emojis from LLM output as cues for generating robot actions. A demo of our system is available here. To illuminate design and implementation issues, we conduct a pilot study where volunteers chat with a social robot using our proposed system, and we analyze their feedback, conducting a rigorous error analysis of chat transcripts. Feedback was overwhelmingly positive, with participants commenting on the robot's empathy, helpfulness, naturalness, and entertainment. Most negative feedback was due to automatic speech recognition (ASR) errors which had limited impact on conversations. However, we observed a small class of errors, such as the LLM repeating itself or hallucinating fictitious information and human responses, that have the potential to derail conversations, raising important issues for LLM application.

2024-02-20

2402.11670

Lars Nieradzik

Lars Nieradzik, Henrike Stephani, J\"ordis Sieburg-Rockel, Stephanie Helmling, Andrea Olbrich, Janis Keuper

Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestry

Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP, 2024, pp. 483-492

cs.CV cs.LG

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

In this study, we explore the explainability of neural networks in agriculture and forestry, specifically in fertilizer treatment classification and wood identification. The opaque nature of these models, often considered 'black boxes', is addressed through an extensive evaluation of state-of-the-art Attribution Maps (AMs), also known as class activation maps (CAMs) or saliency maps. Our comprehensive qualitative and quantitative analysis of these AMs uncovers critical practical limitations. Findings reveal that AMs frequently fail to consistently highlight crucial features and often misalign with the features considered important by domain experts. These discrepancies raise substantial questions about the utility of AMs in understanding the decision-making process of neural networks. Our study provides critical insights into the trustworthiness and practicality of AMs within the agriculture and forestry sectors, thus facilitating a better understanding of neural networks in these application areas.

2024-02-20

2402.11680

Till Beemelmanns

Till Beemelmanns, Yuchen Tao, Bastian Lampe, Lennart Reiher, Raphael van Kempen, Timo Woopen, and Lutz Eckstein

3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods

Code: https://github.com/ika-rwth-aachen/Point-Cloud-Compression

2022 IEEE Intelligent Vehicles Symposium (IV)

10.1109/IV51971.2022.9827270

cs.CV cs.AI eess.IV

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

Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume. Transforming the raw point cloud data into a dense 2D matrix structure is a promising way for applying compression algorithms. We propose a new lossless and calibrated 3D-to-2D transformation which allows compression algorithms to efficiently exploit spatial correlations within the 2D representation. To compress the structured representation, we use common image compression methods and also a self-supervised deep compression approach using a recurrent neural network. We also rearrange the LiDAR's intensity measurements to a dense 2D representation and propose a new metric to evaluate the compression performance of the intensity. Compared to approaches that are based on generic octree point cloud compression or based on raw point cloud data compression, our approach achieves the best quantitative and visual performance. Source code and dataset are available at https://github.com/ika-rwth-aachen/Point-Cloud-Compression.

2024-02-20

2402.11895

Sugat Chaturvedi

Rochana Chaturvedi, Sugat Chaturvedi and Elena Zheleva

Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization

In Proceedings of the ACM Web Conference 2024 (WWW '24), May 13-17, 2024, Singapore, Singapore. ACM, New York, NY, USA, 12 pages

10.1145/3589334.3645675

cs.SI cs.CL physics.soc-ph

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

While exposure to diverse viewpoints may reduce polarization, it can also have a backfire effect and exacerbate polarization when the discussion is adversarial. Here, we examine the question whether intergroup interactions around important events affect polarization between majority and minority groups in social networks. We compile data on the religious identity of nearly 700,000 Indian Twitter users engaging in COVID-19-related discourse during 2020. We introduce a new measure for an individual's group conformity based on contextualized embeddings of tweet text, which helps us assess polarization between religious groups. We then use a meta-learning framework to examine heterogeneous treatment effects of intergroup interactions on an individual's group conformity in the light of communal, political, and socio-economic events. We find that for political and social events, intergroup interactions reduce polarization. This decline is weaker for individuals at the extreme who already exhibit high conformity to their group. In contrast, during communal events, intergroup interactions can increase group conformity. Finally, we decompose the differential effects across religious groups in terms of emotions and topics of discussion. The results show that the dynamics of religious polarization are sensitive to the context and have important implications for understanding the role of intergroup interactions.

2024-08-21

2402.11929

Chong Zeng

Chong Zeng and Yue Dong and Pieter Peers and Youkang Kong and Hongzhi Wu and Xin Tong

DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation

Accepted to SIGGRAPH 2024. Project page: https://dilightnet.github.io/

ACM SIGGRAPH 2024 Conference Proceedings

10.1145/3641519.3657396

cs.CV cs.GR

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

This paper presents a novel method for exerting fine-grained lighting control during text-driven diffusion-based image generation. While existing diffusion models already have the ability to generate images under any lighting condition, without additional guidance these models tend to correlate image content and lighting. Moreover, text prompts lack the necessary expressional power to describe detailed lighting setups. To provide the content creator with fine-grained control over the lighting during image generation, we augment the text-prompt with detailed lighting information in the form of radiance hints, i.e., visualizations of the scene geometry with a homogeneous canonical material under the target lighting. However, the scene geometry needed to produce the radiance hints is unknown. Our key observation is that we only need to guide the diffusion process, hence exact radiance hints are not necessary; we only need to point the diffusion model in the right direction. Based on this observation, we introduce a three stage method for controlling the lighting during image generation. In the first stage, we leverage a standard pretrained diffusion model to generate a provisional image under uncontrolled lighting. Next, in the second stage, we resynthesize and refine the foreground object in the generated image by passing the target lighting to a refined diffusion model, named DiLightNet, using radiance hints computed on a coarse shape of the foreground object inferred from the provisional image. To retain the texture details, we multiply the radiance hints with a neural encoding of the provisional synthesized image before passing it to DiLightNet. Finally, in the third stage, we resynthesize the background to be consistent with the lighting on the foreground object. We demonstrate and validate our lighting controlled diffusion model on a variety of text prompts and lighting conditions.

2024-05-29

2402.12041

Ciaran Eising

Daniel Jakab, Brian Michael Deegan, Sushil Sharma, Eoin Martino Grua, Jonathan Horgan, Enda Ward, Pepijn Van De Ven, Anthony Scanlan, Ciar\'an Eising

Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges

23 pages, 19 figures, 2 tables

IEEE Transactions on Intelligent Transportation Systems, 2024

10.1109/TITS.2024.3368136

cs.CV eess.IV

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

In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using camera systems on vehicles, there is a particular need for a wide field of view to capture the entire vehicle's surroundings, in areas such as low-speed maneuvering, automated parking, and cocoon sensing. However, one crucial challenge in surround-view cameras is the strong optical aberrations of the fisheye camera, which is an area that has received little attention in the literature. Additionally, a comprehensive dataset is needed for testing safety-critical scenarios in vehicle automation. The industry has turned to simulation as a cost-effective strategy for creating synthetic datasets with surround-view camera imagery. We examine different simulation methods (such as model-driven and data-driven simulations) and discuss the simulators' ability (or lack thereof) to model real-world optical performance. Overall, this paper highlights the optical aberrations in automotive fisheye datasets, and the limitations of optical reality in simulated fisheye datasets, with a focus on computer vision in surround-view optical systems.

2024-03-12

2402.12074

Yongquan He

Yongquan He and Peng Zhang and Luchen Liu and Qi Liang and Wenyuan Zhang and Chuang Zhang

HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph

7 pages, 3 figures

IJCAI (2021) 1915-1921

10.24963/IJCAI.2021/264

cs.AI

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

In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer future events based on historical information. However, these methods do not comprehensively consider the latent patterns behind temporal changes, to pass historical information selectively, update representations appropriately and predict events accurately. In this paper, we propose the Historical Information Passing (HIP) network to predict future events. HIP network passes information from temporal, structural and repetitive perspectives, which are used to model the temporal evolution of events, the interactions of events at the same time step, and the known events respectively. In particular, our method considers the updating of relation representations and adopts three scoring functions corresponding to the above dimensions. Experimental results on five benchmark datasets show the superiority of HIP network, and the significant improvements on Hits@1 prove that our method can more accurately predict what is going to happen.

2024-02-22

2402.12193

Yuxia Wang

Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Lizhi Lin, Zhenxuan Zhang, Jingru Zhao, Preslav Nakov, Timothy Baldwin

A Chinese Dataset for Evaluating the Safeguards in Large Language Models

14 pages

ACL2024-Findings

cs.CL

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

Many studies have demonstrated that large language models (LLMs) can produce harmful responses, exposing users to unexpected risks when LLMs are deployed. Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs. However, the focus has been almost exclusively on English, and little has been explored for other languages. Here we aim to bridge this gap. We first introduce a dataset for the safety evaluation of Chinese LLMs, and then extend it to two other scenarios that can be used to better identify false negative and false positive examples in terms of risky prompt rejections. We further present a set of fine-grained safety assessment criteria for each risk type, facilitating both manual annotation and automatic evaluation in terms of LLM response harmfulness. Our experiments on five LLMs show that region-specific risks are the prevalent type of risk, presenting the major issue with all Chinese LLMs we experimented with. Our data is available at https://github.com/Libr-AI/do-not-answer. Warning: this paper contains example data that may be offensive, harmful, or biased.

2024-08-06

2402.12202

Yu Yang

Chengyi Ju and Jiannong Cao and Yu Yang and Zhen-Qun Yang and Ho Man Lee

Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid Federated Approach

2023 IEEE International Conference on Data Mining Workshops (ICDMW)

10.1109/ICDMW60847.2023.00191

cs.IR cs.AI

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

In the era of modern education, addressing cross-school learner diversity is crucial, especially in personalized recommender systems for elective course selection. However, privacy concerns often limit cross-school data sharing, which hinders existing methods' ability to model sparse data and address heterogeneity effectively, ultimately leading to suboptimal recommendations. In response, we propose HFRec, a heterogeneity-aware hybrid federated recommender system designed for cross-school elective course recommendations. The proposed model constructs heterogeneous graphs for each school, incorporating various interactions and historical behaviors between students to integrate context and content information. We design an attention mechanism to capture heterogeneity-aware representations. Moreover, under a federated scheme, we train individual school-based models with adaptive learning settings to recommend tailored electives. Our HFRec model demonstrates its effectiveness in providing personalized elective recommendations while maintaining privacy, as it outperforms state-of-the-art models on both open-source and real-world datasets.

2024-02-20

2402.12320

Ganesh Sapkota

Ganesh Sapkota, Sanjay Madria

Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment

2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), St. Louis, MO, USA, 2023, pp. 1-11

10.1109/AIPR60534.2023.10440690

cs.CV cs.LG

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

In this paper, we have proposed a new strategy of using the landmark anchor node instead of a radio-based anchor node to obtain the virtual coordinates (landmarkID, DISTANCE) of moving troops or defense forces that will help in tracking and maneuvering the troops along a safe path within a GPS-denied battlefield environment. The proposed strategy implements landmark recognition using the Yolov5 model and landmark distance estimation using an efficient Stereo Matching Algorithm. We consider that a moving node carrying a low-power mobile device facilitated with a calibrated stereo vision camera that captures stereo images of a scene containing landmarks within the battlefield region whose locations are stored in an offline server residing within the device itself. We created a custom landmark image dataset called MSTLandmarkv1 with 34 landmark classes and another landmark stereo dataset of those 34 landmark instances called MSTLandmarkStereov1. We trained the YOLOv5 model with MSTLandmarkv1 dataset and achieved 0.95 mAP @ 0.5 IoU and 0.767 mAP @ [0.5: 0.95] IoU. We calculated the distance from a node to the landmark utilizing the bounding box coordinates and the depth map generated by the improved SGM algorithm using MSTLandmarkStereov1. The tuple of landmark IDs obtained from the detection result and the distances calculated by the SGM algorithm are stored as the virtual coordinates of a node. In future work, we will use these virtual coordinates to obtain the location of a node using an efficient trilateration algorithm and optimize the node position using the appropriate optimization method.

2024-04-09

2402.12372

Mario S\"anger

Mario S\"anger, Samuele Garda, Xing David Wang, Leon Weber-Genzel, Pia Droop, Benedikt Fuchs, Alan Akbik, Ulf Leser

HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools

Bioinformatics, Volume 40, Number 10, 2024, btae564, Oxford University Press

10.1093/bioinformatics/btae564

cs.CL

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

With the exponential growth of the life science literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. Identifying named entities (e.g., diseases, drugs, or genes) in texts and their linkage to reference knowledge bases are crucial steps in BTM pipelines to enable information aggregation from different documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied in the wild, i.e., on application-dependent text collections different from those used for the tools' training, varying, e.g., in focus, genre, style, and text type. This raises the question of whether the reported performance of BTM tools can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for named entity extraction, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five for an in-depth analysis on three publicly available corpora encompassing four different entity types. Comparison between tools results in a mixed picture and shows that, in a cross-corpus setting, the performance is significantly lower than the one reported in an in-corpus setting. HunFlair2 showed the best performance on average, being closely followed by PubTator. Our results indicate that users of BTM tools should expect diminishing performances when applying them in the wild compared to original publications and show that further research is necessary to make BTM tools more robust.

2024-10-15

2402.12390

Jos\'e Alberto Ben\'itez-Andrades Ph.D.

Jos\'e Alberto Ben\'itez-Andrades, Alejandro Rodr\'iguez-Gonz\'alez, Carmen Benavides, Leticia S\'anchez-Valde\'on and Isa\'ias Garc\'ia

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies

Int. J. Environ. Res. Public Health 2018, 15(11), 2420;

10.3390/ijerph15112420

cs.SI cs.AI

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

Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called "ontology" that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.

2024-02-21

2402.12407

Shashwat Khandelwal

Shashwat Khandelwal, Ziaul Choudhury, Shashwat Shrivastava and Suresh Purini

Accelerating local laplacian filters on FPGAs

6 pages, 5 figures, 2 tables

10.1109/FPL50879.2020.00028

eess.IV cs.CV cs.GR eess.SP

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

Images when processed using various enhancement techniques often lead to edge degradation and other unwanted artifacts such as halos. These artifacts pose a major problem for photographic applications where they can denude the quality of an image. There is a plethora of edge-aware techniques proposed in the field of image processing. However, these require the application of complex optimization or post-processing methods. Local Laplacian Filtering is an edge-aware image processing technique that involves the construction of simple Gaussian and Laplacian pyramids. This technique can be successfully applied for detail smoothing, detail enhancement, tone mapping and inverse tone mapping of an image while keeping it artifact-free. The problem though with this approach is that it is computationally expensive. Hence, parallelization schemes using multi-core CPUs and GPUs have been proposed. As is well known, they are not power-efficient, and a well-designed hardware architecture on an FPGA can do better on the performance per watt metric. In this paper, we propose a hardware accelerator, which exploits fully the available parallelism in the Local Laplacian Filtering algorithm, while minimizing the utilization of on-chip FPGA resources. On Virtex-7 FPGA, we obtain a 7.5x speed-up to process a 1 MB image when compared to an optimized baseline CPU implementation. To the best of our knowledge, we are not aware of any other hardware accelerators proposed in the research literature for the Local Laplacian Filtering problem.

2024-02-21

2402.12522

Teng Wu

Teng Wu, Bruno Vallet, Marc Pierrot-Deseilligny, Ewelina Rupnik

An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset

International Journal of Applied Earth Observation and Geoinformation, 128(2024)

10.1016/j.jag.2024.103715

cs.CV

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

Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effectiveness in the special case of epipolar stereo disparity estimation in the computer vision community. DL-based methods depend heavily on the quality and quantity of training datasets. However, generating ground-truth disparity maps for real scenes remains a challenging task in the photogrammetry community. To address this challenge, we propose a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images to produce a large and diverse dataset for six aerial datasets across four different areas and two areas with different resolution images. We also introduce a LiDAR-to-image co-registration refinement to the framework that takes special precautions regarding occlusions and refrains from disparity interpolation to avoid precision loss. Evaluating 11 dense matching methods across datasets with diverse scene types, image resolutions, and geometric configurations, which are deeply investigated in dataset shift, GANet performs best with identical training and testing data, and PSMNet shows robustness across different datasets, and we proposed the best strategy for training with a limit dataset. We will also provide the dataset and training models; more information can be found at https://github.com/whuwuteng/Aerial_Stereo_Dataset.

2024-02-21

2402.12646

Sevil Zanjani Miyandoab

Ehsan Rokhsatyazdi, Shahryar Rahnamayan, Sevil Zanjani Miyandoab, Azam Asilian Bidgoli, H.R. Tizhoosh

Training Artificial Neural Networks by Coordinate Search Algorithm

7 pages, 9 figures

2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1540-1546. IEEE, 2023

10.1109/SSCI52147.2023.10371958

cs.LG cs.AI

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

Training Artificial Neural Networks poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously; for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search methods, for training neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the weights. In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method, in some cases, outperforms the gradient-based approach, particularly, in situations with insufficient labeled training data. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such as SGD or Adam. In this paper we introduce an alternative method for training ANN.

2024-02-21

2402.12754

Wentian Zhang

Haozhe Liu, Wentian Zhang, Feng Liu, Haoqian Wu, Linlin Shen

Fingerprint Presentation Attack Detector Using Global-Local Model

This paper was accepted by IEEE Transactions on Cybernetics. Current version is updated with minor revisions on introduction and related works

IEEE TRANSACTIONS ON CYBERNETICS, VOL. 52, NO. 11, 12315-12328, November 2022

10.1109/TCYB.2021.3081764

cs.CV

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

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module; 2) the local module; and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by $\sim$10% in terms of TDR (91.19% versus 80.74%).

2024-02-21

2402.12862

Wen Wu

Wen Wu, Bo Li, Chao Zhang, Chung-Cheng Chiu, Qiujia Li, Junwen Bai, Tara N. Sainath, Philip C. Woodland

Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2024

10.18653/v1/2024.acl-long.114

cs.CL

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

The subjective perception of emotion leads to inconsistent labels from human annotators. Typically, utterances lacking majority-agreed labels are excluded when training an emotion classifier, which cause problems when encountering ambiguous emotional expressions during testing. This paper investigates three methods to handle ambiguous emotion. First, we show that incorporating utterances without majority-agreed labels as an additional class in the classifier reduces the classification performance of the other emotion classes. Then, we propose detecting utterances with ambiguous emotions as out-of-domain samples by quantifying the uncertainty in emotion classification using evidential deep learning. This approach retains the classification accuracy while effectively detects ambiguous emotion expressions. Furthermore, to obtain fine-grained distinctions among ambiguous emotions, we propose representing emotion as a distribution instead of a single class label. The task is thus re-framed from classification to distribution estimation where every individual annotation is taken into account, not just the majority opinion. The evidential uncertainty measure is extended to quantify the uncertainty in emotion distribution estimation. Experimental results on the IEMOCAP and CREMA-D datasets demonstrate the superior capability of the proposed method in terms of majority class prediction, emotion distribution estimation, and uncertainty estimation.

2024-10-14

2402.12923

Anju Rani

Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic

Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey

27 pages, 13 figures, 3 tables, review paper

Information Fusion, 2024

10.1016/j.inffus.2024.102575

cs.CV

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

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.

2024-07-24

2402.12950

Zimeng Xiao

Jinjing Shi, Zimeng Xiao, Heyuan Shi, Yu Jiang and Xuelong Li

QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems

This paper has been accepted by TOSEM 2024

ACM Transactions on Software Engineering and Methodology, 2024

10.1145/3688840

cs.SE cs.AI

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

Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with the fundamental theory of quantum mechanics to achieve machine learning tasks with quantum acceleration. Recently, QNN systems have been found to manifest robustness issues similar to classical DL systems. There is an urgent need for ways to test their correctness and security. However, QNN systems differ significantly from traditional quantum software and classical DL systems, posing critical challenges for QNN testing. These challenges include the inapplicability of traditional quantum software testing methods to QNN systems due to differences in programming paradigms and decision logic representations, the dependence of quantum test sample generation on perturbation operators, and the absence of effective information in quantum neurons. In this paper, we propose QuanTest, a quantum entanglement-guided adversarial testing framework to uncover potential erroneous behaviors in QNN systems. We design a quantum entanglement adequacy criterion to quantify the entanglement acquired by the input quantum states from the QNN system, along with two similarity metrics to measure the proximity of generated quantum adversarial examples to the original inputs. Subsequently, QuanTest formulates the problem of generating test inputs that maximize the quantum entanglement adequacy and capture incorrect behaviors of the QNN system as a joint optimization problem and solves it in a gradient-based manner to generate quantum adversarial examples. results demonstrate that QuanTest possesses the capability to capture erroneous behaviors in QNN systems. The entanglement-guided approach proves effective in adversarial testing, generating more adversarial examples.

2024-08-27

2402.13195

Collin Hague

Collin Hague, Nick Kakavitsas, Jincheng Zhang, Chris Beam, Andrew Willis, Artur Wolek

Design and Flight Demonstration of a Quadrotor for Urban Mapping and Target Tracking Research

7 pages, 10 figures, To be presented at IEEE SoutheastCon 2024

SoutheastCon 2024, 559-564

10.1109/SoutheastCon52093.2024.10500131

cs.RO cs.CV cs.SY eess.SY

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

This paper describes the hardware design and flight demonstration of a small quadrotor with imaging sensors for urban mapping, hazard avoidance, and target tracking research. The vehicle is equipped with five cameras, including two pairs of fisheye stereo cameras that enable a nearly omnidirectional view and a two-axis gimbaled camera. An onboard NVIDIA Jetson Orin Nano computer running the Robot Operating System software is used for data collection. An autonomous tracking behavior was implemented to coordinate the motion of the quadrotor and gimbaled camera to track a moving GPS coordinate. The data collection system was demonstrated through a flight test that tracked a moving GPS-tagged vehicle through a series of roads and parking lots. A map of the environment was reconstructed from the collected images using the Direct Sparse Odometry (DSO) algorithm. The performance of the quadrotor was also characterized by acoustic noise, communication range, battery voltage in hover, and maximum speed tests.

2024-05-03

2402.13219

Ammar Abbas M.Sc.

Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz, Houda Briwa, Andres Alonzo Perez, Gabriele Baldissone, Micaela Demichela, Georgios G. Chasparis, John D. Kelleher, and Maria Chiara Leva

Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies

International Journal of Human-Computer Interaction, 2024

cs.AI cs.HC cs.LG cs.MA cs.SY eess.SY

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

In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide valuable insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time. These predictions enable the development of more effective intervention strategies.

2024-08-09

2402.13287

Jose Manuel Camacho Rodriguez

William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro

Manipulating hidden-Markov-model inferences by corrupting batch data

42 pages, 8 figures, 11 tables

Caballero, W. N., Camacho, J. M., Ekin, T., & Naveiro, R. (2024). Manipulating hidden-Markov-model inferences by corrupting batch data. Computers & Operations Research, 162, 106478

10.1016/j.cor.2023.106478

cs.CR cs.AI cs.LG

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

Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker's inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov model inferences via corrupted data. In particular, we provision a suite of corruption problems for filtering, smoothing, and decoding inferences leveraging an adversarial risk analysis approach. Multiple stochastic programming models are set forth that incorporate realistic uncertainties and varied attacker objectives. Three general solution methods are developed by alternatively viewing the problem from frequentist and Bayesian perspectives. The efficacy of each method is illustrated via extensive, empirical testing. The developed methods are characterized by their solution quality and computational effort, resulting in a stratification of techniques across varying problem-instance architectures. This research highlights the weaknesses of hidden Markov models under adversarial activity, thereby motivating the need for robustification techniques to ensure their security.

2024-02-22

2402.13290

Goonmeet Bajaj

Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit Sheth

Grounding from an AI and Cognitive Science Lens

IEEE Intelligent Systems, 2024

10.1109/MIS.2024.3366669

cs.AI

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

Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities. The article examines the potential of neuro-symbolic approaches tailored for grounding tasks, showcasing how they can more comprehensively address grounding. Finally, we discuss areas for further exploration and development in grounding.

2024-02-22

2402.13301

Manvi Agarwal

Manvi Agarwal (S2A, IDS), Changhong Wang (S2A, IDS), Ga\"el Richard (S2A, IDS)

Structure-informed Positional Encoding for Music Generation

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2024, Seoul, South Korea

cs.SD cs.AI eess.AS

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

Music generated by deep learning methods often suffers from a lack of coherence and long-term organization. Yet, multi-scale hierarchical structure is a distinctive feature of music signals. To leverage this information, we propose a structure-informed positional encoding framework for music generation with Transformers. We design three variants in terms of absolute, relative and non-stationary positional information. We comprehensively test them on two symbolic music generation tasks: next-timestep prediction and accompaniment generation. As a comparison, we choose multiple baselines from the literature and demonstrate the merits of our methods using several musically-motivated evaluation metrics. In particular, our methods improve the melodic and structural consistency of the generated pieces.

2024-02-29

2402.13306

Jose Robledo Hernandez

Efren Hern\'andez-Molina, Benjamin Ojeda-Maga\~na, Jose Guadalupe Robledo-Hern\'andez and Ruben Ruelas

Vision System Prototype for Inspection and Monitoring with a Smart Camera

8 pages, 16 figures, in Spanish language

IEEE Latin America Transactions, vol. 18, no. 09, pp. 1614-1622, September 2020

10.1109/TLA.2020.9381804

cs.CV eess.IV

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

This paper presents the design of an artificial vision system prototype for automatic inspection and monitoring of objects over a conveyor belt and using a Smart camera 2D BOA-INS. The prototype consists of a conveyor belt and an embedded system based on an Arduino Mega card for system control, and it has as main peripherals the smart camera, a direct current motor, a photoelectric sensor, LED illumination and LEDs indicating the status (good or defect) of each evaluated object. The application of the prototype is for educational purposes, so that undergraduate, master and diploma students can simulate a continuous production line, controlled by an embedded system, and perform quality control by monitoring through a visual system and a personal computer. This allows implementing the topics of embedded systems, artificial vision, artificial intelligence, pattern recognition, automatic control, as well as automation of real processes.

2024-02-22

2402.13368

Md Rifat Arefin

Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi

Unsupervised Concept Discovery Mitigates Spurious Correlations

ICLM 2024

cs.LG cs.CV

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

Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. Addressing this challenge typically involves methods relying on prior knowledge and group annotation to remove spurious correlations, which may not be readily available in many applications. In this paper, we establish a novel connection between unsupervised object-centric learning and mitigation of spurious correlations. Instead of directly inferring subgroups with varying correlations with labels, our approach focuses on discovering concepts: discrete ideas that are shared across input samples. Leveraging existing object-centric representation learning, we introduce CoBalT: a concept balancing technique that effectively mitigates spurious correlations without requiring human labeling of subgroups. Evaluation across the benchmark datasets for sub-population shifts demonstrate superior or competitive performance compared state-of-the-art baselines, without the need for group annotation. Code is available at https://github.com/rarefin/CoBalT.

2024-07-31

2402.13432

Yanis Labrak

Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant dit Beaufils, Natalia Grabar, Beatrice Daille, Solen Quiniou, Emmanuel Morin, Pierre-Antoine Gourraud, Richard Dufour

DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain

Accepted at LREC-Coling 2024

Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

cs.CL cs.AI cs.LG

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

The biomedical domain has sparked a significant interest in the field of Natural Language Processing (NLP), which has seen substantial advancements with pre-trained language models (PLMs). However, comparing these models has proven challenging due to variations in evaluation protocols across different models. A fair solution is to aggregate diverse downstream tasks into a benchmark, allowing for the assessment of intrinsic PLMs qualities from various perspectives. Although still limited to few languages, this initiative has been undertaken in the biomedical field, notably English and Chinese. This limitation hampers the evaluation of the latest French biomedical models, as they are either assessed on a minimal number of tasks with non-standardized protocols or evaluated using general downstream tasks. To bridge this research gap and account for the unique sensitivities of French, we present the first-ever publicly available French biomedical language understanding benchmark called DrBenchmark. It encompasses 20 diversified tasks, including named-entity recognition, part-of-speech tagging, question-answering, semantic textual similarity, and classification. We evaluate 8 state-of-the-art pre-trained masked language models (MLMs) on general and biomedical-specific data, as well as English specific MLMs to assess their cross-lingual capabilities. Our experiments reveal that no single model excels across all tasks, while generalist models are sometimes still competitive.

2024-06-11

2402.13452

Vijeta Deshpande

Vijeta Deshpande, Minhwa Lee, Zonghai Yao, Zihao Zhang, Jason Brian Gibbons, Hong Yu

LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data

LREC-COLING 2024

cs.SI cs.CL cs.LG

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

Prior research on Twitter (now X) data has provided positive evidence of its utility in developing supplementary health surveillance systems. In this study, we present a new framework to surveil public health, focusing on mental health (MH) outcomes. We hypothesize that locally posted tweets are indicative of local MH outcomes and collect tweets posted from 765 neighborhoods (census block groups) in the USA. We pair these tweets from each neighborhood with the corresponding MH outcome reported by the Center for Disease Control (CDC) to create a benchmark dataset, LocalTweets. With LocalTweets, we present the first population-level evaluation task for Twitter-based MH surveillance systems. We then develop an efficient and effective method, LocalHealth, for predicting MH outcomes based on LocalTweets. When used with GPT3.5, LocalHealth achieves the highest F1-score and accuracy of 0.7429 and 79.78\%, respectively, a 59\% improvement in F1-score over the GPT3.5 in zero-shot setting. We also utilize LocalHealth to extrapolate CDC's estimates to proxy unreported neighborhoods, achieving an F1-score of 0.7291. Our work suggests that Twitter data can be effectively leveraged to simulate neighborhood-level MH outcomes.

2024-03-27

2402.13542

Yue Yu

Lingxi Zhang, Yue Yu, Kuan Wang, Chao Zhang

ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling

ACL 2024

ACL 2024

cs.CL cs.AI cs.IR cs.LG

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

Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific domains and mitigate hallucinations in knowledge-intensive tasks. However, existing retrievers are often misaligned with LLMs due to their separate training processes and the black-box nature of LLMs. To address this challenge, we propose ARL2, a retriever learning technique that harnesses LLMs as labelers. ARL2 leverages LLMs to annotate and score relevant evidence, enabling learning the retriever from robust LLM supervision. Furthermore, ARL2 uses an adaptive self-training strategy for curating high-quality and diverse relevance data, which can effectively reduce the annotation cost. Extensive experiments demonstrate the effectiveness of ARL2, achieving accuracy improvements of 5.4% on NQ and 4.6% on MMLU compared to the state-of-the-art methods. Additionally, ARL2 exhibits robust transfer learning capabilities and strong zero-shot generalization abilities. Our code will be published at \url{https://github.com/zhanglingxi-cs/ARL2}.

2024-06-05

2402.13573

Nayan Saxena

Ethan Smith, Nayan Saxena, Aninda Saha

ToDo: Token Downsampling for Efficient Generation of High-Resolution Images

2024, Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence

cs.CV cs.AI cs.LG

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

Attention mechanism has been crucial for image diffusion models, however, their quadratic computational complexity limits the sizes of images we can process within reasonable time and memory constraints. This paper investigates the importance of dense attention in generative image models, which often contain redundant features, making them suitable for sparser attention mechanisms. We propose a novel training-free method ToDo that relies on token downsampling of key and value tokens to accelerate Stable Diffusion inference by up to 2x for common sizes and up to 4.5x or more for high resolutions like 2048x2048. We demonstrate that our approach outperforms previous methods in balancing efficient throughput and fidelity.

2024-05-09

2402.13615

Tomas Veloz

Tomas Veloz, Olha Sobetska

Analyizing the Conjunction Fallacy as a Fact

book chapter

In: Veloz, Khrennikov, Toni, Castillo (eds) Trends and Challenges in Cognitive Modeling. STEAM-H: Springer (2023)

cs.AI math.PR nlin.AO

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

Since the seminal paper by Tversky and Kahneman, the conjunction fallacy has been the subject of multiple debates and become a fundamental challenge for cognitive theories in decision-making. In this article, we take a rather uncommon perspective on this phenomenon. Instead of trying to explain the nature or causes of the conjunction fallacy (intensional definition), we analyze its range of factual possibilities (extensional definition). We show that the majority of research on the conjunction fallacy, according to our sample of experiments reviewed which covers literature between 1983 and 2016, has focused on a narrow part of the a priori factual possibilities, implying that explanations of the conjunction fallacy are fundamentally biased by the short scope of possibilities explored. The latter is a rather curious aspect of the research evolution in the conjunction fallacy considering that the very nature of it is motivated by extensional considerations.

2024-02-22

2402.13651

Mikolaj Czerkawski

Mikolaj Czerkawski and Carmine Clemente and Craig Michie and Christos Tachtatzis

Robustness of Deep Neural Networks for Micro-Doppler Radar Classification

International Radar Symposium 2022

10.23919/IRS54158.2022.9905017

cs.CV cs.LG eess.SP

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

With the great capabilities of deep classifiers for radar data processing come the risks of learning dataset-specific features that do not generalize well. In this work, the robustness of two deep convolutional architectures, trained and tested on the same data, is evaluated. When standard training practice is followed, both classifiers exhibit sensitivity to subtle temporal shifts of the input representation, an augmentation that carries minimal semantic content. Furthermore, the models are extremely susceptible to adversarial examples. Both small temporal shifts and adversarial examples are a result of a model overfitting on features that do not generalize well. As a remedy, it is shown that training on adversarial examples and temporally augmented samples can reduce this effect and lead to models that generalise better. Finally, models operating on cadence-velocity diagram representation rather than Doppler-time are demonstrated to be naturally more immune to adversarial examples.

2024-02-23

2402.13718

Xinrong Zhang

Xinrong Zhang and Yingfa Chen and Shengding Hu and Zihang Xu and Junhao Chen and Moo Khai Hao and Xu Han and Zhen Leng Thai and Shuo Wang and Zhiyuan Liu and Maosong Sun

$\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens

2023.12.15ARR

cs.CL

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

Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction. Despite recent strides in making LLMs process contexts with more than 100K tokens, there is currently a lack of a standardized benchmark to evaluate this long-context capability. Existing public benchmarks typically focus on contexts around 10K tokens, limiting the assessment and comparison of LLMs in processing longer contexts. In this paper, we propose $\infty$Bench, the first LLM benchmark featuring an average data length surpassing 100K tokens. $\infty$Bench comprises synthetic and realistic tasks spanning diverse domains, presented in both English and Chinese. The tasks in $\infty$Bench are designed to require well understanding of long dependencies in contexts, and make simply retrieving a limited number of passages from contexts not sufficient for these tasks. In our experiments, based on $\infty$Bench, we evaluate the state-of-the-art proprietary and open-source LLMs tailored for processing long contexts. The results indicate that existing long context LLMs still require significant advancements to effectively process 100K+ context. We further present three intriguing analyses regarding the behavior of LLMs processing long context.

2024-02-27

2402.13782

Vincent Derkinderen

Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires, Luc De Raedt

Semirings for Probabilistic and Neuro-Symbolic Logic Programming

International Journal of Approximate Reasoning (2024): 109130

10.1016/j.ijar.2024.109130

cs.AI

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

The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learning in probabilistic logic programs. While originally PLP focused on discrete probability, more recent approaches have incorporated continuous distributions as well as neural networks, effectively yielding neural-symbolic methods. We provide a unified algebraic perspective on PLP, showing that many if not most of the extensions of PLP can be cast within a common algebraic logic programming framework, in which facts are labeled with elements of a semiring and disjunction and conjunction are replaced by addition and multiplication. This does not only hold for the PLP variations itself but also for the underlying execution mechanism that is based on (algebraic) model counting.

2024-02-22

2402.13852

Azmine Toushik Wasi

Azmine Toushik Wasi

Neural Control System for Continuous Glucose Monitoring and Maintenance

9 Pages, 4 figures, ICLR 2024 Tiny Papers Track https://openreview.net/forum?id=Te4P3Cn54g

The Second Tiny Papers Track at ICLR 2024

cs.LG cs.AI cs.NE cs.SY eess.SY stat.ML

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

Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap, we provide a novel neural control system for continuous glucose monitoring and management that uses differential predictive control. Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time, thereby improving glucose level optimization in the body. This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes, as confirmed by empirical evidence. Code and data are available at: \url{https://github.com/azminewasi/NeuralCGMM}.

2024-06-10

2402.13897

Lo\"ic Rakotoson

Lo\"ic Rakotoson, Sylvain Massip, Fr\'ejus A. A. Laleye

Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning

6 pages, 3 figures

NTERNET 2024, The Sixteenth International Conference on Evolving Internet, volume 16, pages 6-11

cs.IR cs.AI cs.CL cs.LG

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

Information retrieval is a rapidly evolving field. However it still faces significant limitations in the scientific and industrial vast amounts of information, such as semantic divergence and vocabulary gaps in sparse retrieval, low precision and lack of interpretability in semantic search, or hallucination and outdated information in generative models. In this paper, we introduce a two-block approach to tackle these hurdles for long documents. The first block enhances language understanding in sparse retrieval by query expansion to retrieve relevant documents. The second block deepens the result by providing comprehensive and informative answers to the complex question using only the information spread in the long document, enabling bidirectional engagement. At various stages of the pipeline, intermediate results are presented to users to facilitate understanding of the system's reasoning. We believe this bidirectional approach brings significant advancements in terms of transparency, logical thinking, and comprehensive understanding in the field of scientific information retrieval.

2024-03-15

2402.13914

Przemyslaw Biecek

Przemyslaw Biecek, Wojciech Samek

Position: Explain to Question not to Justify

Proceedings of the 41st International Conference on Machine Learning, PMLR 235:3996-4006, 2024

cs.AI cs.CR cs.LG

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

Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.

2024-07-30

2402.14033

Yongquan He

Yongquan He and Zihan Wang and Peng Zhang and Zhaopeng Tu and Zhaochun Ren

VN Network: Embedding Newly Emerging Entities with Virtual Neighbors

10 pages, 5 figures

CIKM (2020) 505-514

10.1145/3340531.3411865

cs.LG cs.AI

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

Embedding entities and relations into continuous vector spaces has attracted a surge of interest in recent years. Most embedding methods assume that all test entities are available during training, which makes it time-consuming to retrain embeddings for newly emerging entities. To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities. In this paper, we propose a novel framework, namely Virtual Neighbor (VN) network, to address three key challenges. Firstly, to reduce the neighbor sparsity problem, we introduce the concept of the virtual neighbors inferred by rules. And we assign soft labels to these neighbors by solving a rule-constrained problem, rather than simply regarding them as unquestionably true. Secondly, many existing methods only use one-hop or two-hop neighbors for aggregation and ignore the distant information that may be helpful. Instead, we identify both logic and symmetric path rules to capture complex patterns. Finally, instead of one-time injection of rules, we employ an iterative learning scheme between the embedding method and virtual neighbor prediction to capture the interactions within. Experimental results on two knowledge graph completion tasks demonstrate that our VN network significantly outperforms state-of-the-art baselines. Furthermore, results on Subject/Object-R show that our proposed VN network is highly robust to the neighbor sparsity problem.

2024-02-23

2402.14147

Tzu-Sheng Kuo

Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng, Jiwoo Kim, Meng-Hsin Wu, Tongshuang Wu, Kenneth Holstein, Haiyi Zhu

Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia

Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24)

10.1145/3613904.3642278

cs.HC cs.AI

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

AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which can yield misleading conclusions about AI performance. How might we empower communities to drive the intentional design and curation of evaluation datasets for AI that impacts them? We investigate this question on Wikipedia, an online community with multiple AI-based content moderation tools deployed. We introduce Wikibench, a system that enables communities to collaboratively curate AI evaluation datasets, while navigating ambiguities and differences in perspective through discussion. A field study on Wikipedia shows that datasets curated using Wikibench can effectively capture community consensus, disagreement, and uncertainty. Furthermore, study participants used Wikibench to shape the overall data curation process, including refining label definitions, determining data inclusion criteria, and authoring data statements. Based on our findings, we propose future directions for systems that support community-driven data curation.

2024-02-23

2402.14340

Duksu Kim

Sangwon Choi, Daejune Choi, Duksu Kim

TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth Estimation

13 pages, 8 figures, under review for a journal

Image and Vision Computing, 148 (2024), 105110

10.1016/j.imavis.2024.105110

cs.CV

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

Monocular depth estimation (MDE) is essential for numerous applications yet is impeded by the substantial computational demands of accurate deep learning models. To mitigate this, we introduce a novel Teacher-Independent Explainable Knowledge Distillation (TIE-KD) framework that streamlines the knowledge transfer from complex teacher models to compact student networks, eliminating the need for architectural similarity. The cornerstone of TIE-KD is the Depth Probability Map (DPM), an explainable feature map that interprets the teacher's output, enabling feature-based knowledge distillation solely from the teacher's response. This approach allows for efficient student learning, leveraging the strengths of feature-based distillation. Extensive evaluation of the KITTI dataset indicates that TIE-KD not only outperforms conventional response-based KD methods but also demonstrates consistent efficacy across diverse teacher and student architectures. The robustness and adaptability of TIE-KD underscore its potential for applications requiring efficient and interpretable models, affirming its practicality for real-world deployment.

2024-07-16

2402.14346

Francesco Malandrino

Francesco Malandrino and Giuseppe Di Giacomo and Marco Levorato and Carla Fabiana Chiasserini

Dependable Distributed Training of Compressed Machine Learning Models

IEEE WoWMoM 2024

cs.LG cs.AI

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

The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its average value. This leads to a poor dependability}of the resulting ML models, whose performance may be much worse than expected. We fill this gap by proposing DepL, a framework for dependable learning orchestration, able to make high-quality, efficient decisions on (i) the data to leverage for learning, (ii) the models to use and when to switch among them, and (iii) the clusters of nodes, and the resources thereof, to exploit. For concreteness, we consider as possible available models a full DNN and its compressed versions. Unlike previous studies, DepL guarantees that a target learning quality is reached with a target probability, while keeping the training cost at a minimum. We prove that DepL has constant competitive ratio and polynomial complexity, and show that it outperforms the state-of-the-art by over 27% and closely matches the optimum.

2024-02-23

2402.14424

Song Tong

Song Tong, Kai Mao, Zhen Huang, Yukun Zhao, Kaiping Peng

Automating psychological hypothesis generation with AI: when large language models meet causal graph

Humanities and Social Sciences Communications, (2024) 11:896

10.1057/s41599-024-03407-5

cs.AI cs.CY

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

Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on `well-being', then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34, p=0.007 and t(59) = 4.32, p<0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.

2024-08-19

2402.14473

Jiajie Su

Jiajie Su, Chaochao Chen, Zibin Lin, Xi Li, Weiming Liu, and Xiaolin Zheng

Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation

Proceedings of the 31st ACM International Conference on Multimedia. 2023: 6321-6331

cs.IR cs.AI

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

Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short. To tackle this problem, we focus on Multi-Behavior Sequential Recommendation (MBSR) in this paper, which aims to leverage time-evolving heterogeneous behavioral dependencies for better exploring users' potential intents on the target behavior. Solving MBSR is challenging. On the one hand, users exhibit diverse multi-behavior patterns due to personal characteristics. On the other hand, there exists comprehensive co-influence between behavior correlations and item collaborations, the intensity of which is deeply affected by temporal factors. To tackle these challenges, we propose a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem, which models personalized patterns and multifaceted sequential collaborations in a novel way to boost recommendation performance. First, PBAT develops a personalized behavior pattern generator in the representation layer, which extracts dynamic and discriminative behavior patterns for sequential learning. Second, PBAT reforms the self-attention layer with a behavior-aware collaboration extractor, which introduces a fused behavior-aware attention mechanism for incorporating both behavioral and temporal impacts into collaborative transitions. We conduct experiments on three benchmark datasets and the results demonstrate the effectiveness and interpretability of our framework. Our implementation code is released at https://github.com/TiliaceaeSU/PBAT.

2024-02-23

2402.14741

Daniel Capell\'an-Mart\'in Mr.

Daniel Capell\'an-Mart\'in, Abhijeet Parida, Juan J. G\'omez-Valverde, Ramon Sanchez-Jacob, Pooneh Roshanitabrizi, Marius G. Linguraru, Mar\'ia J. Ledesma-Carbayo, Syed M. Anwar

Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning

5 pages, 3 figures, 2 tables. This paper has been accepted at IEEE ISBI 2024

21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece

10.1109/ISBI56570.2024.10635520

cs.CV eess.IV

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

Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\sim$12.7% and $\sim$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce.

2024-08-29

2402.14743

\c{S}aziye Bet\"ul \"Ozate\c{s}

\c{S}aziye Bet\"ul \"Ozate\c{s}, Tar{\i}k Emre T{\i}ra\c{s}, Efe Eren Gen\c{c}, Esma Fat{\i}ma Bilgin Ta\c{s}demir

Dependency Annotation of Ottoman Turkish with Multilingual BERT

9 pages, 5 figures. Accepted to LAW-XVIII

\c{S}aziye Bet\"ul \"Ozate\c{s}, Tar{\i}k T{\i}ra\c{s}, Efe Gen\c{c}, and Esma Bilgin Ta\c{s}demir. 2024. Dependency Annotation of Ottoman Turkish with Multilingual BERT. LAW-XVIII, pages 188-196, St. Julians, Malta

cs.CL

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

This study introduces a pretrained large language model-based annotation methodology for the first de dency treebank in Ottoman Turkish. Our experimental results show that, iteratively, i) pseudo-annotating data using a multilingual BERT-based parsing model, ii) manually correcting the pseudo-annotations, and iii) fine-tuning the parsing model with the corrected annotations, we speed up and simplify the challenging dependency annotation process. The resulting treebank, that will be a part of the Universal Dependencies (UD) project, will facilitate automated analysis of Ottoman Turkish documents, unlocking the linguistic richness embedded in this historical heritage.

2024-08-23

2402.14810

Xueyi Liu

Xueyi Liu, Li Yi

GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion

Accepted to ICLR 2024. Project website: https://meowuu7.github.io/GeneOH-Diffusion/; Huggingface Demo: https://huggingface.co/spaces/xymeow7/gene-hoi-denoising; Code: https://github.com/Meowuu7/GeneOH-Diffusion

ICLR 2024

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

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

In this work, we tackle the challenging problem of denoising hand-object interactions (HOI). Given an erroneous interaction sequence, the objective is to refine the incorrect hand trajectory to remove interaction artifacts for a perceptually realistic sequence. This challenge involves intricate interaction noise, including unnatural hand poses and incorrect hand-object relations, alongside the necessity for robust generalization to new interactions and diverse noise patterns. We tackle those challenges through a novel approach, GeneOH Diffusion, incorporating two key designs: an innovative contact-centric HOI representation named GeneOH and a new domain-generalizable denoising scheme. The contact-centric representation GeneOH informatively parameterizes the HOI process, facilitating enhanced generalization across various HOI scenarios. The new denoising scheme consists of a canonical denoising model trained to project noisy data samples from a whitened noise space to a clean data manifold and a "denoising via diffusion" strategy which can handle input trajectories with various noise patterns by first diffusing them to align with the whitened noise space and cleaning via the canonical denoiser. Extensive experiments on four benchmarks with significant domain variations demonstrate the superior effectiveness of our method. GeneOH Diffusion also shows promise for various downstream applications. Project website: https://meowuu7.github.io/GeneOH-Diffusion/.

2024-02-23

2402.14846

Grgur Kova\v{c}

Grgur Kova\v{c}, R\'emy Portelas, Masataka Sawayama, Peter Ford Dominey, Pierre-Yves Oudeyer

Stick to your Role! Stability of Personal Values Expressed in Large Language Models

The project website and code are available at https://sites.google.com/view/llmvaluestability Published in PLOS ONE ( https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0309114 ), and a shorter version at CogSci 24 ( https://escholarship.org/uc/item/7w4823c6 )

PLOS ONE, August 2024

10.1371/journal.pone.0309114

cs.CL cs.AI cs.LG

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

The standard way to study Large Language Models (LLMs) with benchmarks or psychology questionnaires is to provide many different queries from similar minimal contexts (e.g. multiple choice questions). However, due to LLMs' highly context-dependent nature, conclusions from such minimal-context evaluations may be little informative about the model's behavior in deployment (where it will be exposed to many new contexts). We argue that context-dependence (specifically, value stability) should be studied as a specific property of LLMs and used as another dimension of LLM comparison (alongside others such as cognitive abilities, knowledge, or model size). We present a case-study on the stability of value expression over different contexts (simulated conversations on different topics) as measured using a standard psychology questionnaire (PVQ) and on behavioral downstream tasks. Reusing methods from psychology, we study Rank-order stability on the population (interpersonal) level, and Ipsative stability on the individual (intrapersonal) level. We consider two settings (with and without instructing LLMs to simulate particular personas), two simulated populations, and three downstream tasks. We observe consistent trends in the stability of models and model families - Mixtral, Mistral, GPT-3.5 and Qwen families are more stable than LLaMa-2 and Phi. The consistency of these trends implies that some models exhibit higher value stability than others, and that stability can be estimated with the set of introduced methodological tools. When instructed to simulate particular personas, LLMs exhibit low Rank-order stability, which further diminishes with conversation length. This highlights the need for future research on LLMs that coherently simulate different personas. This paper provides a foundational step in that direction, and, to our knowledge, it is the first study of value stability in LLMs.

2024-08-29

2402.14847

Michal Bou\v{s}ka

Michal Bou\v{s}ka, P\v{r}emysl \v{S}\r{u}cha, Anton\'in Nov\'ak, Zden\v{e}k Hanz\'alek

Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness

European Journal of Operational Research, Volume 308, Issue 3, 1 August 2023, Pages 990-1006

10.1016/j.ejor.2022.11.034

math.OC cs.AI cs.LG

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

In this paper, we investigate the use of the deep learning method for solving a well-known NP-hard single machine scheduling problem with the objective of minimizing the total tardiness. We propose a deep neural network that acts as a polynomial-time estimator of the criterion value used in a single-pass scheduling algorithm based on Lawler's decomposition and symmetric decomposition proposed by Della Croce et al. Essentially, the neural network guides the algorithm by estimating the best splitting of the problem into subproblems. The paper also describes a new method for generating the training data set, which speeds up the training dataset generation and reduces the average optimality gap of solutions. The experimental results show that our machine learning-driven approach can efficiently generalize information from the training phase to significantly larger instances. Even though the instances used in the training phase have from 75 to 100 jobs, the average optimality gap on instances with up to 800 jobs is 0.26%, which is almost five times less than the gap of the state-of-the-art heuristic.

2024-02-28

2402.14854

Hyolim Jeon

Hyolim Jeon, Dongje Yoo, Daeun Lee, Sejung Son, Seungbae Kim, Jinyoung Han

A Dual-Prompting for Interpretable Mental Health Language Models

Proceedings of the Ninth Workshop on Computational Linguistics and Clinical Psychology 2024

cs.CL cs.AI

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

Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability.The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific LLM; and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach's potential to aid clinicians in assessing mental state progression.

2024-02-26

2402.14881

Chen Qian

Shanker Ram and Chen Qian

A Study on the Vulnerability of Test Questions against ChatGPT-based Cheating

2023 International Conference on Machine Learning and Applications (ICMLA)

2023 International Conference on Machine Learning and Applications (ICMLA)

cs.CL cs.AI cs.CY

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

ChatGPT is a chatbot that can answer text prompts fairly accurately, even performing very well on postgraduate-level questions. Many educators have found that their take-home or remote tests and exams are vulnerable to ChatGPT-based cheating because students may directly use answers provided by tools like ChatGPT. In this paper, we try to provide an answer to an important question: how well ChatGPT can answer test questions and how we can detect whether the questions of a test can be answered correctly by ChatGPT. We generated ChatGPT's responses to the MedMCQA dataset, which contains over 10,000 medical school entrance exam questions. We analyzed the responses and uncovered certain types of questions ChatGPT answers more inaccurately than others. In addition, we have created a basic natural language processing model to single out the most vulnerable questions to ChatGPT in a collection of questions or a sample exam. Our tool can be used by test-makers to avoid ChatGPT-vulnerable test questions.

2024-02-26

2402.14958

Jakub Kol\'a\v{r}

Jakub Kol\'a\v{r}, Radim \v{S}petl\'ik, Ji\v{r}\'i Matas

EE3P: Event-based Estimation of Periodic Phenomena Properties

9 pages, 55 figures, accepted and presented at CVWW24, published in Proceedings of the 27th Computer Vision Winter Workshop, 2024

Proceedings of the 27th Computer Vision Winter Workshop, February 14-16, 2024, Terme Olimia, Slovenia, pages 66-74, CIP data: COBISS.SI-ID 185271043 ISBN 978-961-96564-0-2

cs.CV

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

We introduce a novel method for measuring properties of periodic phenomena with an event camera, a device asynchronously reporting brightness changes at independently operating pixels. The approach assumes that for fast periodic phenomena, in any spatial window where it occurs, a very similar set of events is generated at the time difference corresponding to the frequency of the motion. To estimate the frequency, we compute correlations of spatio-temporal windows in the event space. The period is calculated from the time differences between the peaks of the correlation responses. The method is contactless, eliminating the need for markers, and does not need distinguishable landmarks. We evaluate the proposed method on three instances of periodic phenomena: (i) light flashes, (ii) vibration, and (iii) rotational speed. In all experiments, our method achieves a relative error lower than 0.04%, which is within the error margin of ground truth measurements.

2024-02-26

2402.15010

Yanis Labrak

Yanis Labrak, Adrien Bazoge, Beatrice Daille, Mickael Rouvier, Richard Dufour

How Important Is Tokenization in French Medical Masked Language Models?

Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

cs.CL cs.AI cs.LG

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

Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began with Byte-Pair Encoding (BPE) and was later followed by the adoption of SentencePiece and WordPiece. While subword tokenization consistently outperforms character and word-level tokenization, the precise factors contributing to its success remain unclear. Key aspects such as the optimal segmentation granularity for diverse tasks and languages, the influence of data sources on tokenizers, and the role of morphological information in Indo-European languages remain insufficiently explored. This is particularly pertinent for biomedical terminology, characterized by specific rules governing morpheme combinations. Despite the agglutinative nature of biomedical terminology, existing language models do not explicitly incorporate this knowledge, leading to inconsistent tokenization strategies for common terms. In this paper, we seek to delve into the complexities of subword tokenization in French biomedical domain across a variety of NLP tasks and pinpoint areas where further enhancements can be made. We analyze classical tokenization algorithms, including BPE and SentencePiece, and introduce an original tokenization strategy that integrates morpheme-enriched word segmentation into existing tokenization methods.

2024-06-11

2402.15255

Vy Vo

Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung

Optimal Transport for Structure Learning Under Missing Data

Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

cs.LG cs.AI

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

Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considering the dependencies or, preferably, causal relations among variables. Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirically shown to be sub-optimal. To address this problem, we propose a score-based algorithm for learning causal structures from missing data based on optimal transport. This optimal transport viewpoint diverges from existing score-based approaches that are dominantly based on expectation maximization. We formulate structure learning as a density fitting problem, where the goal is to find the causal model that induces a distribution of minimum Wasserstein distance with the observed data distribution. Our framework is shown to recover the true causal graphs more effectively than competing methods in most simulations and real-data settings. Empirical evidence also shows the superior scalability of our approach, along with the flexibility to incorporate any off-the-shelf causal discovery methods for complete data.

2024-06-04

2402.15464

Kaveh Fathian

Kaveh Fathian, Tyler Summers

CLIPPER+: A Fast Maximal Clique Algorithm for Robust Global Registration

IEEE ROBOTICS AND AUTOMATION LETTERS, 2024

10.1109/LRA.2024.3368233

cs.RO cs.CV

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

We present CLIPPER+, an algorithm for finding maximal cliques in unweighted graphs for outlier-robust global registration. The registration problem can be formulated as a graph and solved by finding its maximum clique. This formulation leads to extreme robustness to outliers; however, finding the maximum clique is an NP-hard problem, and therefore approximation is required in practice for large-size problems. The performance of an approximation algorithm is evaluated by its computational complexity (the lower the runtime, the better) and solution accuracy (how close the solution is to the maximum clique). Accordingly, the main contribution of CLIPPER+ is outperforming the state-of-the-art in accuracy while maintaining a relatively low runtime. CLIPPER+ builds on prior work (CLIPPER [1] and PMC [2]) and prunes the graph by removing vertices that have a small core number and cannot be a part of the maximum clique. This will result in a smaller graph, on which the maximum clique can be estimated considerably faster. We evaluate the performance of CLIPPER+ on standard graph benchmarks, as well as synthetic and real-world point cloud registration problems. These evaluations demonstrate that CLIPPER+ has the highest accuracy and can register point clouds in scenarios where over $99\%$ of associations are outliers. Our code and evaluation benchmarks are released at https://github.com/ariarobotics/clipperp.

2024-02-26

2402.15518

Pedro Reviriego

Gonzalo Mart\'inez, Jos\'e Alberto Hern\'andez, Javier Conde, Pedro Reviriego and Elena Merino

Beware of Words: Evaluating the Lexical Richness of Conversational Large Language Models

ACM Transactions on Intelligent Systems and Technology, 2024

cs.CL

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

The performance of conversational Large Language Models (LLMs) in general, and of ChatGPT in particular, is currently being evaluated on many different tasks, from logical reasoning or maths to answering questions on a myriad of topics. Instead, much less attention is being devoted to the study of the linguistic features of the texts generated by these LLMs. This is surprising since LLMs are models for language, and understanding how they use the language is important. Indeed, conversational LLMs are poised to have a significant impact on the evolution of languages as they may eventually dominate the creation of new text. This means that for example, if conversational LLMs do not use a word it may become less and less frequent and eventually stop being used altogether. Therefore, evaluating the linguistic features of the text they produce and how those depend on the model parameters is the first step toward understanding the potential impact of conversational LLMs on the evolution of languages. In this paper, we consider the evaluation of the lexical richness of the text generated by LLMs and how it depends on the model parameters. A methodology is presented and used to conduct a comprehensive evaluation of lexical richness using ChatGPT as a case study. The results show how lexical richness depends on the version of ChatGPT and some of its parameters, such as the presence penalty, or on the role assigned to the model. The dataset and tools used in our analysis are released under open licenses with the goal of drawing the much-needed attention to the evaluation of the linguistic features of LLM-generated text.

2024-09-10

2402.15584

Nikola Zubi\'c

Nikola Zubi\'c, Mathias Gehrig, Davide Scaramuzza

State Space Models for Event Cameras

18 pages, 5 figures, 6 tables, CVPR 2024 Camera Ready paper

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 2024

cs.CV cs.LG

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

Today, state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense, grid-like input representations. As such, they exhibit poor generalizability when deployed at higher inference frequencies (i.e., smaller temporal windows) than the ones they were trained on. We address this challenge by introducing state-space models (SSMs) with learnable timescale parameters to event-based vision. This design adapts to varying frequencies without the need to retrain the network at different frequencies. Additionally, we investigate two strategies to counteract aliasing effects when deploying the model at higher frequencies. We comprehensively evaluate our approach against existing methods based on RNN and Transformer architectures across various benchmarks, including Gen1 and 1 Mpx event camera datasets. Our results demonstrate that SSM-based models train 33% faster and also exhibit minimal performance degradation when tested at higher frequencies than the training input. Traditional RNN and Transformer models exhibit performance drops of more than 20 mAP, with SSMs having a drop of 3.76 mAP, highlighting the effectiveness of SSMs in event-based vision tasks.

2024-04-19

2402.15666

Shu-Ting Pi

Shu-Ting Pi, Cheng-Ping Hsieh, Qun Liu, Yuying Zhu

Universal Model in Online Customer Service

Companion Proceedings of the ACM Web Conference 2023

10.1145/3543873.3587630

cs.LG cs.AI cs.IR

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

Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting a universal model for predict-ing labels based on customer questions, without requiring training. Our novel approach involves using machine learning techniques to tag customer questions in transcripts and create a repository of questions and corresponding labels. When a customer requests assistance, an information retrieval model searches the repository for similar questions, and statistical analysis is used to predict the corresponding label. By eliminating the need for individual model training and maintenance, our approach reduces both the model development cycle and costs. The repository only requires periodic updating to maintain accuracy.

2024-02-27

2402.15810

Fanjin Zhang

Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen, Yukuo Cen, Jifan Yu, Yelin Chen, Lulu Wang, Qingfei Zhao, Yuqing Cheng, Tianyi Han, Yuwei An, Dan Zhang, Weng Lam Tam, Kun Cao, Yunhe Pang, Xinyu Guan, Huihui Yuan, Jian Song, Xiaoyan Li, Yuxiao Dong, Jie Tang

OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining

KDD'24, 9 pages, 5 appendix pages

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), August 25--29, 2024, Barcelona, Spain

10.1145/3637528.3672354

cs.DL cs.CL cs.LG

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

With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often fall short in multi-aspect and fine-grained annotations, are constrained to specific task types and domains, or lack underlying real academic graphs. In this paper, we present OAG-Bench, a comprehensive, multi-aspect, and fine-grained human-curated benchmark based on the Open Academic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines, and 120+ experimental results to date. We propose new data annotation strategies for certain tasks and offer a suite of data pre-processing codes, algorithm implementations, and standardized evaluation protocols to facilitate academic graph mining. Extensive experiments reveal that even advanced algorithms like large language models (LLMs) encounter difficulties in addressing key challenges in certain tasks, such as paper source tracing and scholar profiling. We also introduce the Open Academic Graph Challenge (OAG-Challenge) to encourage community input and sharing. We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field. OAG-Bench is accessible at https://www.aminer.cn/data/.

2024-06-21

2402.15858

Jieming Bian

Yuanzhe Peng, Jieming Bian, Jie Xu

FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology

2024 International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)

cs.CV cs.DC

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

The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics. However, existing multimodal learning approaches necessitate access to users' raw data, posing substantial privacy risks. While Federated Learning (FL) serves as a privacy-preserving alternative, it falls short in addressing the challenges posed by heterogeneous (yet possibly overlapped) modalities data across various hospitals. To bridge this gap, we propose a Federated Multi-Modal (FedMM) learning framework that federatedly trains multiple single-modal feature extractors to enhance subsequent classification performance instead of existing FL that aims to train a unified multimodal fusion model. Any participating hospital, even with small-scale datasets or limited devices, can leverage these federated trained extractors to perform local downstream tasks (e.g., classification) while ensuring data privacy. Through comprehensive evaluations of two publicly available datasets, we demonstrate that FedMM notably outperforms two baselines in accuracy and AUC metrics.

2024-02-27

2402.15987

Masanari Ohi

Masanari Ohi, Masahiro Kaneko, Ryuto Koike, Mengsay Loem, Naoaki Okazaki

Likelihood-based Mitigation of Evaluation Bias in Large Language Models

5 main pages

ACL2024 (findings)

cs.CL cs.AI

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

Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. However, the likelihood, a measure of LLM's plausibility for a sentence, can vary due to superficial differences in sentences, such as word order and sentence structure. It is therefore possible that there might be a likelihood bias if LLMs are used for evaluation: they might overrate sentences with higher likelihoods while underrating those with lower likelihoods. In this paper, we investigate the presence and impact of likelihood bias in LLM-based evaluators. We also propose a method to mitigate the likelihood bias. Our method utilizes highly biased instances as few-shot examples for in-context learning. Our experiments in evaluating the data-to-text and grammatical error correction tasks reveal that several LLMs we test display a likelihood bias. Furthermore, our proposed method successfully mitigates this bias, also improving evaluation performance (in terms of correlation of models with human scores) significantly.

2024-10-15

2402.15990

Abdul Ali Bangash

Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan

An Empirical Study of Challenges in Machine Learning Asset Management

Empirical Software Engineering 2024

10.1007/s10664-024-10474-4

cs.SE cs.AI

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

In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle. This enables quicker iterations, adaptability, reduced development-to-deployment time, and reliable outputs. Despite existing research, a significant knowledge gap remains in operational challenges like model versioning, data traceability, and collaboration, which are crucial for the success of ML projects. Our study aims to address this gap by analyzing 15,065 posts from developer forums and platforms, employing a mixed-method approach to classify inquiries, extract challenges using BERTopic, and identify solutions through open card sorting and BERTopic clustering. We uncover 133 topics related to asset management challenges, grouped into 16 macro-topics, with software dependency, model deployment, and model training being the most discussed. We also find 79 solution topics, categorized under 18 macro-topics, highlighting software dependency, feature development, and file management as key solutions. This research underscores the need for further exploration of identified pain points and the importance of collaborative efforts across academia, industry, and the research community.

2024-06-19

2402.16012

Bocheng Wang

Mulin Chen, Bocheng Wang, Xuelong Li

Deep Contrastive Graph Learning with Clustering-Oriented Guidance

Accept at AAAI24

AAAI (2024) Vol. 38, No. 10, pages 11364-11372

10.1609/aaai.v38i10.29016

cs.LG cs.AI

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

Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. To handle the general clustering scenario without a prior graph, these models estimate an initial graph beforehand to apply GCN. Throughout the literature, we have witnessed that 1) most models focus on the initial graph while neglecting the original features. Therefore, the discriminability of the learned representation may be corrupted by a low-quality initial graph; 2) the training procedure lacks effective clustering guidance, which may lead to the incorporation of clustering-irrelevant information into the learned graph. To tackle these problems, the Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering. Specifically, we establish a pseudo-siamese network, which incorporates auto-encoder with GCN to emphasize both the graph structure and the original features. On this basis, feature-level contrastive learning is introduced to enhance the discriminative capacity, and the relationship between samples and centroids is employed as the clustering-oriented guidance. Afterward, a two-branch graph learning mechanism is designed to extract the local and global structural relationships, which are further embedded into a unified graph under the cluster-level contrastive guidance. Experimental results on several benchmark datasets demonstrate the superiority of DCGL against state-of-the-art algorithms.

2024-04-04

2402.16013

Sahal Shaji Mullappilly

Sahal Shaji Mullappilly, Abhishek Singh Gehlot, Rao Muhammad Anwer, Fahad Shahbaz Khan, Hisham Cholakkal

Semi-supervised Open-World Object Detection

Accepted to AAAI 2024 (Main Track)

Proceedings of the AAAI Conference on Artificial Intelligence 2024

10.1609/aaai.v38i5.28227

cs.CV

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

Conventional open-world object detection (OWOD) problem setting first distinguishes known and unknown classes and then later incrementally learns the unknown objects when introduced with labels in the subsequent tasks. However, the current OWOD formulation heavily relies on the external human oracle for knowledge input during the incremental learning stages. Such reliance on run-time makes this formulation less realistic in a real-world deployment. To address this, we introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD), that reduces the annotation cost by casting the incremental learning stages of OWOD in a semi-supervised manner. We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Therefore, we introduce a novel SS-OWOD detector, named SS-OWFormer, that utilizes a feature-alignment scheme to better align the object query representations between the original and augmented images to leverage the large unlabeled and few labeled data. We further introduce a pseudo-labeling scheme for unknown detection that exploits the inherent capability of decoder object queries to capture object-specific information. We demonstrate the effectiveness of our SS-OWOD problem setting and approach for remote sensing object detection, proposing carefully curated splits and baseline performance evaluations. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach. Our source code, models and splits are available here - https://github.com/sahalshajim/SS-OWFormer

2024-04-15

2402.16039

Han Li

Zihan Liu, Han Li, Anfan Chen, Renwen Zhang, Yi-Chieh Lee

Understanding Public Perceptions of AI Conversational Agents: A Cross-Cultural Analysis

17 pages, 4 figures, 7 tables

CHI2024

10.1145/3613904.3642840

cs.HC cs.CL

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

Conversational Agents (CAs) have increasingly been integrated into everyday life, sparking significant discussions on social media. While previous research has examined public perceptions of AI in general, there is a notable lack in research focused on CAs, with fewer investigations into cultural variations in CA perceptions. To address this gap, this study used computational methods to analyze about one million social media discussions surrounding CAs and compared people's discourses and perceptions of CAs in the US and China. We find Chinese participants tended to view CAs hedonically, perceived voice-based and physically embodied CAs as warmer and more competent, and generally expressed positive emotions. In contrast, US participants saw CAs more functionally, with an ambivalent attitude. Warm perception was a key driver of positive emotions toward CAs in both countries. We discussed practical implications for designing contextually sensitive and user-centric CAs to resonate with various users' preferences and needs.

2024-02-27

2402.16086

Feng Lu

Feng Lu, Shuting Dong, Lijun Zhang, Bingxi Liu, Xiangyuan Lan, Dongmei Jiang, Chun Yuan

Deep Homography Estimation for Visual Place Recognition

Accepted by AAAI2024

AAAI 2024

10.1609/aaai.v38i9.28901

cs.CV cs.AI

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

Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received considerable attention due to the trade-off between accuracy and efficiency. They usually first use global features to retrieve the candidate images, then verify the spatial consistency of matched local features for re-ranking. However, the latter typically relies on the RANSAC algorithm for fitting homography, which is time-consuming and non-differentiable. This makes existing methods compromise to train the network only in global feature extraction. Here, we propose a transformer-based deep homography estimation (DHE) network that takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Moreover, we design a re-projection error of inliers loss to train the DHE network without additional homography labels, which can also be jointly trained with the backbone network to help it extract the features that are more suitable for local matching. Extensive experiments on benchmark datasets show that our method can outperform several state-of-the-art methods. And it is more than one order of magnitude faster than the mainstream hierarchical VPR methods using RANSAC. The code is released at https://github.com/Lu-Feng/DHE-VPR.

2024-04-09

2402.16139

Antonio San Mart\'in

Antonio San Mart\'in

What Generative Artificial Intelligence Means for Terminological Definitions

37 pages, 1 figure

Proceedings of the 3rd International Conference on Multilingual Digital Terminology Today (MDTT 2024)

cs.CL cs.AI

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

This paper examines the impact of Generative Artificial Intelligence (GenAI) tools like ChatGPT on the creation and consumption of terminological definitions. From the terminologist's point of view, the strategic use of GenAI tools can streamline the process of crafting definitions, reducing both time and effort, while potentially enhancing quality. GenAI tools enable AI-assisted terminography, notably post-editing terminography, where the machine produces a definition that the terminologist then corrects or refines. However, the potential of GenAI tools to fulfill all the terminological needs of a user, including term definitions, challenges the very existence of terminological definitions and resources as we know them. Unlike terminological definitions, GenAI tools can describe the knowledge activated by a term in a specific context. However, a main drawback of these tools is that their output can contain errors. For this reason, users requiring reliability will likely still resort to terminological resources for definitions. Nevertheless, with the inevitable integration of AI into terminology work, the distinction between human-created and AI-created content will become increasingly blurred.

2024-07-01

2402.16188

Vincent Christlein

Alexander Schmidt, Prathmesh Madhu, Andreas Maier, Vincent Christlein, Ronak Kosti

ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Salzburg, Austria, 2022, pp. 1-6

10.1109/IPTA54936.2022.9784144

cs.CV

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

Image enhancement algorithms are very useful for real world computer vision tasks where image resolution is often physically limited by the sensor size. While state-of-the-art deep neural networks show impressive results for image enhancement, they often struggle to enhance real-world images. In this work, we tackle a real-world setting: inpainting of images from Dunhuang caves. The Dunhuang dataset consists of murals, half of which suffer from corrosion and aging. These murals feature a range of rich content, such as Buddha statues, bodhisattvas, sponsors, architecture, dance, music, and decorative patterns designed by different artists spanning ten centuries, which makes manual restoration challenging. We modify two different existing methods (CAR, HINet) that are based upon state-of-the-art (SOTA) super resolution and deblurring networks. We show that those can successfully inpaint and enhance these deteriorated cave paintings. We further show that a novel combination of CAR and HINet, resulting in our proposed inpainting network (ARIN), is very robust to external noise, especially Gaussian noise. To this end, we present a quantitative and qualitative comparison of our proposed approach with existing SOTA networks and winners of the Dunhuang challenge. One of the proposed methods HINet) represents the new state of the art and outperforms the 1st place of the Dunhuang Challenge, while our combination ARIN, which is robust to noise, is comparable to the 1st place. We also present and discuss qualitative results showing the impact of our method for inpainting on Dunhuang cave images.

2024-02-27

2402.16268

Rishi Bommasani

Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang

Foundation Model Transparency Reports

Published in AIES 2024

cs.LG cs.AI cs.CY

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

Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transparency about the development and deployment of their models, we propose Foundation Model Transparency Reports, drawing upon the transparency reporting practices in social media. While external documentation of societal harms prompted social media transparency reports, our objective is to institutionalize transparency reporting for foundation models while the industry is still nascent. To design our reports, we identify 6 design principles given the successes and shortcomings of social media transparency reporting. To further schematize our reports, we draw upon the 100 transparency indicators from the Foundation Model Transparency Index. Given these indicators, we measure the extent to which they overlap with the transparency requirements included in six prominent government policies (e.g., the EU AI Act, the US Executive Order on Safe, Secure, and Trustworthy AI). Well-designed transparency reports could reduce compliance costs, in part due to overlapping regulatory requirements across different jurisdictions. We encourage foundation model developers to regularly publish transparency reports, building upon recommendations from the G7 and the White House.

2024-07-19

2402.16361

Shiwen Ni

Shiwen Ni, Min Yang, Ruifeng Xu, Chengming Li and Xiping Hu

Layer-wise Regularized Dropout for Neural Language Models

LREC-COLING 2024

cs.CL cs.AI

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

Among the various pre-trained neural language models that are popular today, dropout is already an indispensable regularization technique. To solve the inconsistency between training and inference caused by the randomness of dropout, some studies use consistency training to regularize dropout at the output layer. In this paper, we propose a novel Layer-wise Regularized Dropout (LR-Drop), which is specially designed for Transformer-based Language models. Specifically, LR-Drop layer-wise regularizes each Transformer layer using the consistency training strategy. Each training sample passes through the two siamese sub-models sampled by dropout, and then LR-Drop forces the hidden states, multi-head attention matrices, and output distribution of the two siamese sub-models to be consistent. The proposed LR-Drop can be regarded as a "self-distillation" framework, in which each sub-model generated by dropout is the other's "teacher" model and "student" model. Through extensive experiments on 8 natural language understanding datasets, 6 neural machine translation datasets, and 1 abstractive summarization dataset (a total of 15 datasets), we show that LR-Drop achieves superior performances, including state-of-the-art results.

2024-02-27

2402.16364

Tzuf Paz-Argaman

Tzuf Paz-Argaman, Sayali Kulkarni, John Palowitch, Jason Baldridge, and Reut Tsarfaty

Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions

EACL 2024

cs.CL cs.LG cs.MM

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

When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. However, NLP navigation studies often overlook the impact of such acquired knowledge on textual descriptions. Current navigation studies concentrate on egocentric local descriptions (e.g., `it will be on your right') that require reasoning over the agent's local perception. These instructions are typically given as a sequence of steps, with each action-step explicitly mentioning and being followed by a landmark that the agent can use to verify they are on the right path (e.g., `turn right and then you will see...'). In contrast, descriptions based on knowledge acquired through a map provide a complete view of the environment and capture its overall structure. These instructions (e.g., `it is south of Central Park and a block north of a police station') are typically non-sequential, contain allocentric relations, with multiple spatial relations and implicit actions, without any explicit verification. This paper introduces the Rendezvous (RVS) task and dataset, which includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. Our analysis reveals that RVS exhibits a richer use of spatial allocentric relations, and requires resolving more spatial relations simultaneously compared to previous text-based navigation benchmarks.

2024-08-06

2402.16389

Shiwen Ni

Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu, Ye Li, Jianping Fan

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property

LREC-COLING 2024

cs.CL cs.AI

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

Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs perform in specific domains (e.g, the intellectual property (IP) domain). In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain. The MoZIP benchmark includes three challenging tasks: IP multiple-choice quiz (IPQuiz), IP question answering (IPQA), and patent matching (PatentMatch). In addition, we also develop a new IP-oriented multilingual large language model (called MoZi), which is a BLOOMZ-based model that has been supervised fine-tuned with multilingual IP-related text data. We evaluate our proposed MoZi model and four well-known LLMs (i.e., BLOOMZ, BELLE, ChatGLM and ChatGPT) on the MoZIP benchmark. Experimental results demonstrate that MoZi outperforms BLOOMZ, BELLE and ChatGLM by a noticeable margin, while it had lower scores compared with ChatGPT. Notably, the performance of current LLMs on the MoZIP benchmark has much room for improvement, and even the most powerful ChatGPT does not reach the passing level. Our source code, data, and models are available at \url{https://github.com/AI-for-Science/MoZi}.

2024-02-27

2402.16420

Lev Kharlashkin

Lev Kharlashkin, Melany Macias, Leo Huovinen, Mika H\"am\"al\"ainen

Predicting Sustainable Development Goals Using Course Descriptions -- from LLMs to Conventional Foundation Models

3 figures, 2 tables

Journal of Data Mining & Digital Humanities, NLP4DH (April 29, 2024) jdmdh:13127

10.46298/jdmdh.13127

cs.CL

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

We present our work on predicting United Nations sustainable development goals (SDG) for university courses. We use an LLM named PaLM 2 to generate training data given a noisy human-authored course description input as input. We use this data to train several different smaller language models to predict SDGs for university courses. This work contributes to better university level adaptation of SDGs. The best performing model in our experiments was BART with an F1-score of 0.786.

2024-08-07

2402.16514

Luk\'a\v{s} Gajdo\v{s}ech

Katar\'ina Osvaldov\'a, Luk\'a\v{s} Gajdo\v{s}ech, Viktor Kocur, Martin Madaras

Enhancement of 3D Camera Synthetic Training Data with Noise Models

Published in 2024 Proceedings of the 27th Computer Vision Winter Workshop (CVWW). Accepted: 19.1.2024. Published: 16.2.2024. This work was funded by the Horizon-Widera-2021 European Twinning project TERAIS G.A. n. 101079338. Code: https://doi.org/10.5281/zenodo.10581562 Data: https://doi.org/10.5281/zenodo.10581278

Proceedings of the 27th Computer Vision Winter Workshop CVWW (2024) 29-37

10.5281/zenodo.10694437

cs.CV

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

The goal of this paper is to assess the impact of noise in 3D camera-captured data by modeling the noise of the imaging process and applying it on synthetic training data. We compiled a dataset of specifically constructed scenes to obtain a noise model. We specifically model lateral noise, affecting the position of captured points in the image plane, and axial noise, affecting the position along the axis perpendicular to the image plane. The estimated models can be used to emulate noise in synthetic training data. The added benefit of adding artificial noise is evaluated in an experiment with rendered data for object segmentation. We train a series of neural networks with varying levels of noise in the data and measure their ability to generalize on real data. The results show that using too little or too much noise can hurt the networks' performance indicating that obtaining a model of noise from real scanners is beneficial for synthetic data generation.

2024-02-27

2402.16654

Andrey Savchenko

Pavel Blinov, Konstantin Egorov, Ivan Sviridov, Nikolay Ivanov, Stepan Botman, Evgeniy Tagin, Stepan Kudin, Galina Zubkova, Andrey Savchenko

GigaPevt: Multimodal Medical Assistant

IJCAI 2024, 4 pages, 2 figures, 2 tables

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI) Demo Track, 2024, pp. 8614-8618

10.24963/ijcai.2024/992

cs.AI cs.CL cs.HC

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

Building an intelligent and efficient medical assistant is still a challenging AI problem. The major limitation comes from the data modality scarceness, which reduces comprehensive patient perception. This demo paper presents the GigaPevt, the first multimodal medical assistant that combines the dialog capabilities of large language models with specialized medical models. Such an approach shows immediate advantages in dialog quality and metric performance, with a 1.18% accuracy improvement in the question-answering task.

2024-07-31

2402.16871

Sascha Ossowski

Alberto Fern\'andez, Holger Billhardt, Sascha Ossowski, \'Oscar S\'anchez

Bike3S: A Tool for Bike Sharing Systems Simulation

Journal of Simulation 14(4), 2020

10.1080/17477778.2020.1718022

cs.MA cs.AI

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

Vehicle sharing systems are becoming increasingly popular. The effectiveness of such systems depends, among other factors, on different strategic and operational management decisions and policies, like the dimension of the fleet or the distribution of vehicles. It is of foremost importance to be able to anticipate and evaluate the potential effects of such strategies before they can be successfully deployed. In this paper we present Bike3S, a simulator for a station-based bike sharing system. The simulator performs semi-realistic simulations of the operation of a bike sharing system and allows for evaluating and testing different management decisions and strategies. In particular, the simulator has been designed to test different station capacities, station distributions, and balancing strategies. The simulator carries out microscopic agent-based simulations, where users of different types can be defined that act according to their individual goals and objectives which influences the overall dynamics of the whole system.

2024-02-28

2402.16898

Nguyen Do Hoang Khoi

Nguyen Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai

MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization

International Conference on Artificial Intelligence and Statistics (AISTATS) 2024

cs.SI cs.AI cs.LG math.PR stat.ML

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

Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one of central research topics, especially in nowadays social networking landscape where users participate in multiple online social networks (OSNs) and their influences can propagate among several OSNs simultaneously. Although there exist a couple combinatorial algorithms to MIM, learning-based solutions have been desired due to its generalization ability to heterogeneous networks and their diversified propagation characteristics. In this paper, we introduce MIM-Reasoner, coupling reinforcement learning with probabilistic graphical model, which effectively captures the complex propagation process within and between layers of a given multiplex network, thereby tackling the most challenging problem in MIM. We establish a theoretical guarantee for MIM-Reasoner as well as conduct extensive analyses on both synthetic and real-world datasets to validate our MIM-Reasoner's performance.

2024-03-12

2402.16998

Jerry Ngo

Jerry Ngo, Yoon Kim

What Do Language Models Hear? Probing for Auditory Representations in Language Models

2024.acl-long.297

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

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

This work explores whether language models encode meaningfully grounded representations of sounds of objects. We learn a linear probe that retrieves the correct text representation of an object given a snippet of audio related to that object, where the sound representation is given by a pretrained audio model. This probe is trained via a contrastive loss that pushes the language representations and sound representations of an object to be close to one another. After training, the probe is tested on its ability to generalize to objects that were not seen during training. Across different language models and audio models, we find that the probe generalization is above chance in many cases, indicating that despite being trained only on raw text, language models encode grounded knowledge of sounds for some objects.

2024-08-19

2402.17029

Vincent Christlein

Vincent Christlein, David Bernecker, Andreas Maier, Elli Angelopoulou

Offline Writer Identification Using Convolutional Neural Network Activation Features

fixed tab 1b

Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham

10.1007/978-3-319-24947-6_45

cs.CV

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

Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of mAP on the challenging bilingual ICDAR dataset.

2024-02-28

2402.17124

Xinran Zhao

Xinran Zhao, Hongming Zhang, Xiaoman Pan, Wenlin Yao, Dong Yu, Tongshuang Wu, Jianshu Chen

Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

17 pages, 10 figures

Findings of the Association for Computational Linguistics ACL 2024

cs.CL

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

For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by prompts, the confidence calibration in prompting LLMs has yet to be thoroughly explored. In this paper, we explore how different prompting strategies influence LLM confidence calibration and how it could be improved. We conduct extensive experiments on six prompting methods in the question-answering context and we observe that, while these methods help improve the expected LLM calibration, they also trigger LLMs to be over-confident when responding to some instances. Inspired by human cognition, we propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. First, FaR elicits the known "facts" that are relevant to the input prompt from the LLM. And then it asks the model to "reflect" over them to generate the final answer. Experiments show that FaR prompting achieves significantly better calibration; it lowers the Expected Calibration Error by 23.5% on our multi-purpose QA tasks. Notably, FaR prompting even elicits the capability of verbally expressing concerns in less confident scenarios, which helps trigger retrieval augmentation for solving these harder instances.

2024-09-10

2402.17256

Pei Wang

Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu

Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection

LREC-COLING 2024

cs.CL

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

Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems. Previous methods address it by fine-tuning discriminative models. Recently, some studies have been exploring the application of large language models (LLMs) represented by ChatGPT to various downstream tasks, but it is still unclear for their ability on OOD detection task.This paper conducts a comprehensive evaluation of LLMs under various experimental settings, and then outline the strengths and weaknesses of LLMs. We find that LLMs exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource. More deeply, through a series of additional analysis experiments, we discuss and summarize the challenges faced by LLMs and provide guidance for future work including injecting domain knowledge, strengthening knowledge transfer from IND(In-domain) to OOD, and understanding long instructions.

2024-03-05

2402.17372

Matteo Bastico

Matteo Bastico, Etienne Decenci\`ere, Laurent Cort\'e, Yannick Tillier, David Ryckelynck

Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching

This paper has been accepted at Computer Vision and Patter Recognition (CVPR) 2024

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 3447-3458

cs.CV

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

Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is crucial for accurately identifying a correct match, thereby enhancing the overall robustness and reliability of the matching process. Commonly used shape descriptors have several limitations and often fail to provide meaningful local insights about the paired geometries. In this work, we propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures. To deal with the order and sign ambiguity of Laplacian eigenmaps, we introduce a new operator, called Coupled Laplacian (https://github.com/matteo-bastico/CoupLap), that allows to easily generate aligned eigenspaces for multiple registered geometries. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes. We firstly evaluate the performance of the proposed technique in a point-wise manner, focusing on the task of object anomaly localization on the MVTec 3D-AD dataset. Additionally, we define a new medical task, called automatic Bone Side Estimation (BSE), which we address through a global similarity score derived from coupled eigenspaces. In order to test it, we propose a benchmark collecting bone surface structures from various public datasets. Our matching technique, based on Coupled Laplacian, outperforms other methods by reaching an impressive accuracy on both tasks.

2024-07-29

2402.17386

Emanuel Pfeffer

Emanuel Pfeffer and Michael Wa{\ss}mer and Yee-Ying Cung and Roger Wolf and Ulrich Husemann

A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics

Comput Softw Big Sci 8, 13 (2024)

10.1007/s41781-024-00122-3

hep-ph cs.AI hep-ex

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

In high-energy particle collisions, the primary collision products usually decay further resulting in tree-like, hierarchical structures with a priori unknown multiplicity. At the stable-particle level all decay products of a collision form permutation invariant sets of final state objects. The analogy to mathematical graphs gives rise to the idea that graph neural networks (GNNs), which naturally resemble these properties, should be best-suited to address many tasks related to high-energy particle physics. In this paper we describe a benchmark test of a typical GNN against neural networks of the well-established deep fully-connected feed-forward architecture. We aim at performing this comparison maximally unbiased in terms of nodes, hidden layers, or trainable parameters of the neural networks under study. As physics case we use the classification of the final state X produced in association with top quark-antiquark pairs in proton-proton collisions at the Large Hadron Collider at CERN, where X stands for a bottom quark-antiquark pair produced either non-resonantly or through the decay of an intermediately produced Z or Higgs boson.

2024-07-15

2402.17392

Alexandra Kogam

Vasilii A. Gromov, Alexandra S. Kogan

Spot the bot: Coarse-Grained Partition of Semantic Paths for Bots and Humans

Pattern Recognition and Machine Intelligence, 2023. pp. 348--355

cs.CL

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

Nowadays, technology is rapidly advancing: bots are writing comments, articles, and reviews. Due to this fact, it is crucial to know if the text was written by a human or by a bot. This paper focuses on comparing structures of the coarse-grained partitions of semantic paths for human-written and bot-generated texts. We compare the clusterizations of datasets of n-grams from literary texts and texts generated by several bots. The hypothesis is that the structures and clusterizations are different. Our research supports the hypothesis. As the semantic structure may be different for different languages, we investigate Russian, English, German, and Vietnamese languages.

2024-03-03

2402.17433

Jiaqi Wang

Jiaqi Wang, Zhenxi Song, Zhengyu Ma, Xipeng Qiu, Min Zhang, Zhiguo Zhang

Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder

8 pages (excluding references), accepted by ACL 2024 Main Conference

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Volume 1, pages 7278-7292, August 2024, Bangkok, Thailand

10.18653/v1/2024.acl-long.393

cs.CL

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

Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs). However, EEG-based language decoding is still in its nascent stages, facing several technical issues such as: 1) Absence of a hybrid strategy that can effectively integrate cross-modality (between EEG and text) self-learning with intra-modality self-reconstruction of EEG features or textual sequences; 2) Under-utilization of large language models (LLMs) to enhance EEG-based language decoding. To address above issues, we propose the Contrastive EEG-Text Masked Autoencoder (CET-MAE), a novel model that orchestrates compound self-supervised learning across and within EEG and text through a dedicated multi-stream encoder. Furthermore, we develop a framework called E2T-PTR (EEG-to-Text decoding using Pretrained Transferable Representations), which leverages pre-trained modules alongside the EEG stream from CET-MAE and further enables an LLM (specifically BART) to decode text from EEG sequences. Comprehensive experiments conducted on the popular text-evoked EEG database, ZuCo, demonstrate the superiority of E2T-PTR, which outperforms the state-of-the-art in ROUGE-1 F1 and BLEU-4 scores by 8.34% and 32.21%, respectively. These results indicate significant advancements in the field and underscores the proposed framework's potential to enable more powerful and widespread BCI applications.

2024-09-27

2402.17482

Saja Al Ani

Saja Al Ani, Joanne Cleland, Ahmed Zoha

Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging

Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING, 2024, pages 326-331

10.5220/0012592700003657

cs.SD cs.AI cs.CV eess.AS

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

Speech sound disorder (SSD) is defined as a persistent impairment in speech sound production leading to reduced speech intelligibility and hindered verbal communication. Early recognition and intervention of children with SSD and timely referral to speech and language therapists (SLTs) for treatment are crucial. Automated detection of speech impairment is regarded as an efficient method for examining and screening large populations. This study focuses on advancing the automatic diagnosis of SSD in early childhood by proposing a technical solution that integrates ultrasound tongue imaging (UTI) with deep-learning models. The introduced FusionNet model combines UTI data with the extracted texture features to classify UTI. The overarching aim is to elevate the accuracy and efficiency of UTI analysis, particularly for classifying speech sounds associated with SSD. This study compared the FusionNet approach with standard deep-learning methodologies, highlighting the excellent improvement results of the FusionNet model in UTI classification and the potential of multi-learning in improving UTI classification in speech therapy clinics.

2024-02-28

2402.17706

Junzhe Chen

Junzhe Chen, Qiao Yang, Senmao Tian, Shunli Zhang

Adaptive quantization with mixed-precision based on low-cost proxy

accepted by icassp2024

ICASSP2024

10.1109/ICASSP48485.2024.10447866

cs.CV

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

It is critical to deploy complicated neural network models on hardware with limited resources. This paper proposes a novel model quantization method, named the Low-Cost Proxy-Based Adaptive Mixed-Precision Model Quantization (LCPAQ), which contains three key modules. The hardware-aware module is designed by considering the hardware limitations, while an adaptive mixed-precision quantization module is developed to evaluate the quantization sensitivity by using the Hessian matrix and Pareto frontier techniques. Integer linear programming is used to fine-tune the quantization across different layers. Then the low-cost proxy neural architecture search module efficiently explores the ideal quantization hyperparameters. Experiments on the ImageNet demonstrate that the proposed LCPAQ achieves comparable or superior quantization accuracy to existing mixed-precision models. Notably, LCPAQ achieves 1/200 of the search time compared with existing methods, which provides a shortcut in practical quantization use for resource-limited devices.

2024-04-04

2402.17903

Jingying Wang

Jingying Wang, Haoran Tang, Taylor Kantor, Tandis Soltani, Vitaliy Popov and Xu Wang

Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning

CHI'2024

10.1145/3613904.3642587

cs.HC cs.CV

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

Videos are prominent learning materials to prepare surgical trainees before they enter the operating room (OR). In this work, we explore techniques to enrich the video-based surgery learning experience. We propose Surgment, a system that helps expert surgeons create exercises with feedback based on surgery recordings. Surgment is powered by a few-shot-learning-based pipeline (SegGPT+SAM) to segment surgery scenes, achieving an accuracy of 92\%. The segmentation pipeline enables functionalities to create visual questions and feedback desired by surgeons from a formative study. Surgment enables surgeons to 1) retrieve frames of interest through sketches, and 2) design exercises that target specific anatomical components and offer visual feedback. In an evaluation study with 11 surgeons, participants applauded the search-by-sketch approach for identifying frames of interest and found the resulting image-based questions and feedback to be of high educational value.

2024-06-27

2402.17944

Weijie Xu

Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, Christos Faloutsos

Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey

41 pages, 4 figures, 8 tables

TMLR 2024

cs.CL

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

Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This survey aims to address this gap by consolidating recent progress in these areas, offering a thorough survey and taxonomy of the datasets, metrics, and methodologies utilized. It identifies strengths, limitations, unexplored territories, and gaps in the existing literature, while providing some insights for future research directions in this vital and rapidly evolving field. It also provides relevant code and datasets references. Through this comprehensive review, we hope to provide interested readers with pertinent references and insightful perspectives, empowering them with the necessary tools and knowledge to effectively navigate and address the prevailing challenges in the field.

2024-06-25

2402.18109

Qinglin Liu

Qinglin Liu, Xiaoqian Lv, Wei Yu, Changyong Guo, Shengping Zhang

Dual-Context Aggregation for Universal Image Matting

Multimed Tools Appl (2023)

10.1007/s11042-023-17517-w

cs.CV

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

Natural image matting aims to estimate the alpha matte of the foreground from a given image. Various approaches have been explored to address this problem, such as interactive matting methods that use guidance such as click or trimap, and automatic matting methods tailored to specific objects. However, existing matting methods are designed for specific objects or guidance, neglecting the common requirement of aggregating global and local contexts in image matting. As a result, these methods often encounter challenges in accurately identifying the foreground and generating precise boundaries, which limits their effectiveness in unforeseen scenarios. In this paper, we propose a simple and universal matting framework, named Dual-Context Aggregation Matting (DCAM), which enables robust image matting with arbitrary guidance or without guidance. Specifically, DCAM first adopts a semantic backbone network to extract low-level features and context features from the input image and guidance. Then, we introduce a dual-context aggregation network that incorporates global object aggregators and local appearance aggregators to iteratively refine the extracted context features. By performing both global contour segmentation and local boundary refinement, DCAM exhibits robustness to diverse types of guidance and objects. Finally, we adopt a matting decoder network to fuse the low-level features and the refined context features for alpha matte estimation. Experimental results on five matting datasets demonstrate that the proposed DCAM outperforms state-of-the-art matting methods in both automatic matting and interactive matting tasks, which highlights the strong universality and high performance of DCAM. The source code is available at \url{https://github.com/Windaway/DCAM}.

2024-02-29

2402.18115

Minghan Li

Minghan Li and Shuai Li and Xindong Zhang and Lei Zhang

UniVS: Unified and Universal Video Segmentation with Prompts as Queries

21 pages, 11 figures, 10 tabels, CVPR2024

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

cs.CV cs.CL

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

Despite the recent advances in unified image segmentation (IS), developing a unified video segmentation (VS) model remains a challenge. This is mainly because generic category-specified VS tasks need to detect all objects and track them across consecutive frames, while prompt-guided VS tasks require re-identifying the target with visual/text prompts throughout the entire video, making it hard to handle the different tasks with the same architecture. We make an attempt to address these issues and present a novel unified VS architecture, namely UniVS, by using prompts as queries. UniVS averages the prompt features of the target from previous frames as its initial query to explicitly decode masks, and introduces a target-wise prompt cross-attention layer in the mask decoder to integrate prompt features in the memory pool. By taking the predicted masks of entities from previous frames as their visual prompts, UniVS converts different VS tasks into prompt-guided target segmentation, eliminating the heuristic inter-frame matching process. Our framework not only unifies the different VS tasks but also naturally achieves universal training and testing, ensuring robust performance across different scenarios. UniVS shows a commendable balance between performance and universality on 10 challenging VS benchmarks, covering video instance, semantic, panoptic, object, and referring segmentation tasks. Code can be found at \url{https://github.com/MinghanLi/UniVS}.

2024-06-11

2402.18171

Zihua Liu

Zihua Liu, Songyan Zhang, Zhicheng Wang and Masatoshi Okutomi

Digging Into Normal Incorporated Stereo Matching

Proceedings of the 30th ACM International Conference on Multimedia (ACMMM2022), pp.6050-6060, October 2022

10.1145/3503161.3548312

cs.CV

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

Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, disparity estimation in low-texture, occluded, and bordered regions still remains a bottleneck that limits the performance. To tackle these challenges, geometric guidance like plane information is necessary as it provides intuitive guidance about disparity consistency and affinity similarity. In this paper, we propose a normal incorporated joint learning framework consisting of two specific modules named non-local disparity propagation(NDP) and affinity-aware residual learning(ARL). The estimated normal map is first utilized for calculating a non-local affinity matrix and a non-local offset to perform spatial propagation at the disparity level. To enhance geometric consistency, especially in low-texture regions, the estimated normal map is then leveraged to calculate a local affinity matrix, providing the residual learning with information about where the correction should refer and thus improving the residual learning efficiency. Extensive experiments on several public datasets including Scene Flow, KITTI 2015, and Middlebury 2014 validate the effectiveness of our proposed method. By the time we finished this work, our approach ranked 1st for stereo matching across foreground pixels on the KITTI 2015 dataset and 3rd on the Scene Flow dataset among all the published works.

2024-02-29

2402.18175

Zhuofeng Wu

Zhuofeng Wu, Yusuke Monno, and Masatoshi Okutomi

Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024

cs.CV eess.IV

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

In this paper, we address the task of aberration-aware depth-from-defocus (DfD), which takes account of spatially variant point spread functions (PSFs) of a real camera. To effectively obtain the spatially variant PSFs of a real camera without requiring any ground-truth PSFs, we propose a novel self-supervised learning method that leverages the pair of real sharp and blurred images, which can be easily captured by changing the aperture setting of the camera. In our PSF estimation, we assume rotationally symmetric PSFs and introduce the polar coordinate system to more accurately learn the PSF estimation network. We also handle the focus breathing phenomenon that occurs in real DfD situations. Experimental results on synthetic and real data demonstrate the effectiveness of our method regarding both the PSF estimation and the depth estimation.

2024-02-29

2402.18178

Wenjiao Bian

Wenjiao Bian, Yusuke Monno, Masatoshi Okutomi

Reflection Removal Using Recurrent Polarization-to-Polarization Network

ICASSP 2024

cs.CV

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

This paper addresses reflection removal, which is the task of separating reflection components from a captured image and deriving the image with only transmission components. Considering that the existence of the reflection changes the polarization state of a scene, some existing methods have exploited polarized images for reflection removal. While these methods apply polarized images as the inputs, they predict the reflection and the transmission directly as non-polarized intensity images. In contrast, we propose a polarization-to-polarization approach that applies polarized images as the inputs and predicts "polarized" reflection and transmission images using two sequential networks to facilitate the separation task by utilizing the interrelated polarization information between the reflection and the transmission. We further adopt a recurrent framework, where the predicted reflection and transmission images are used to iteratively refine each other. Experimental results on a public dataset demonstrate that our method outperforms other state-of-the-art methods.

2024-02-29

2402.18181

Zihua Liu

Zihua Liu, Yizhou Li and Masatoshi Okutomi

CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation

IEEE International Conference on Robotics and Automation (ICRA2024)

cs.CV

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

Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the visibility and results in less distinctive features for dense correspondence matching. While some previous learning-based methods integrated a physical scattering function for simultaneous stereo-matching and dehazing, simply removing fog might not aid depth estimation because the fog itself can provide crucial depth cues. In this work, we introduce a framework based on contrastive feature distillation (CFD). This strategy combines feature distillation from merged clean-fog features with contrastive learning, ensuring balanced dependence on fog depth hints and clean matching features. This framework helps to enhance model generalization across both clean and foggy environments. Comprehensive experiments on synthetic and real-world datasets affirm the superior strength and adaptability of our method.

2024-03-07

2402.18201

Sen Xu

Sen Xu, Shikui Wei, Tao Ruan, and Lixin Liao

Learning Invariant Inter-pixel Correlations for Superpixel Generation

Accepted by AAAI24

Proceedings of the AAAI Conference on Artificial Intelligence, 38(6), 6351-6359 (2024)

10.1609/aaai.v38i6.28454

cs.CV

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

Deep superpixel algorithms have made remarkable strides by substituting hand-crafted features with learnable ones. Nevertheless, we observe that existing deep superpixel methods, serving as mid-level representation operations, remain sensitive to the statistical properties (e.g., color distribution, high-level semantics) embedded within the training dataset. Consequently, learnable features exhibit constrained discriminative capability, resulting in unsatisfactory pixel grouping performance, particularly in untrainable application scenarios. To address this issue, we propose the Content Disentangle Superpixel (CDS) algorithm to selectively separate the invariant inter-pixel correlations and statistical properties, i.e., style noise. Specifically, We first construct auxiliary modalities that are homologous to the original RGB image but have substantial stylistic variations. Then, driven by mutual information, we propose the local-grid correlation alignment across modalities to reduce the distribution discrepancy of adaptively selected features and learn invariant inter-pixel correlations. Afterwards, we perform global-style mutual information minimization to enforce the separation of invariant content and train data styles. The experimental results on four benchmark datasets demonstrate the superiority of our approach to existing state-of-the-art methods, regarding boundary adherence, generalization, and efficiency. Code and pre-trained model are available at https://github.com/rookiie/CDSpixel.

2024-04-10

2402.18576

Sales Aribe Jr.

Sales Aribe Jr

Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network

9 pages, 4 figures, Published with International Journal of Engineering Trends and Technology (IJETT)

International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 11-19, 2024

10.14445/22315381/IJETT-V72I1P102

cs.NE cs.AI cs.LG

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

Decision making and planning have long relied heavily on AI-driven forecasts. The government and the general public are working to minimize the risks while maximizing benefits in the face of potential future public health uncertainties. This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight (RDV IW) technique to improve the convergence of Particle Swarm Optimization (PSO) and the accuracy of Artificial Neural Network (ANN). The IW technique, inspired by the motions of a golf ball, modified the particles' velocities as they approached the solution point to a parabolically descending structure. Simulation results revealed that the proposed forecasting model with [0.4, 0.9] combination of alpha and alpha_dump exhibits a 6.36% improvement in position error and 11.75% improvement in computational time compared to the old model, thus, improving its convergence. It reached the optimum level at minimal steps with 12.50% improvement as against the old model since it provides better velocity averages when speed stabilization occurs at the 24th iteration. Meanwhile, the computed p-values for NRMSE (0.04889174), MAE (0.02829063), MAPE (0.02226053), WAPE (0.01701545), and R2 (0.00000021) of the proposed algorithm are less than the set 0.05 level of significance, thus the values indicated a significant result in terms of accuracy performance. Applying the modified ANN-PSO using RDV IW technique greatly improved the new HIV/AIDS forecasting model compared with the two models.

2024-03-01

2402.18589

Nikola Milo\v{s}evi\'c Dr

Milo\v{s} Ko\v{s}prdi\'c, Adela Ljaji\'c, Bojana Ba\v{s}aragin, Darija Medvecki, Nikola Milo\v{s}evi\'c

Verif.ai: Towards an Open-Source Scientific Generative Question-Answering System with Referenced and Verifiable Answers

Accepted as a short paper at The Sixteenth International Conference on Evolving Internet (INTERNET 2024)

The Sixteenth International Conference on Evolving Internet (INTERNET 2024)

cs.IR cs.AI cs.CL cs.LG

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

In this paper, we present the current progress of the project Verif.ai, an open-source scientific generative question-answering system with referenced and verified answers. The components of the system are (1) an information retrieval system combining semantic and lexical search techniques over scientific papers (PubMed), (2) a fine-tuned generative model (Mistral 7B) taking top answers and generating answers with references to the papers from which the claim was derived, and (3) a verification engine that cross-checks the generated claim and the abstract or paper from which the claim was derived, verifying whether there may have been any hallucinations in generating the claim. We are reinforcing the generative model by providing the abstract in context, but in addition, an independent set of methods and models are verifying the answer and checking for hallucinations. Therefore, we believe that by using our method, we can make scientists more productive, while building trust in the use of generative language models in scientific environments, where hallucinations and misinformation cannot be tolerated.

2024-04-11

2402.18616

Jos\'e Ra\'ul Romero

Aurora Ram\'irez and Jos\'e Ra\'ul Romero and Carlos Garc\'ia-Mart\'inez and Sebasti\'an Ventura

JCLEC-MO: a Java suite for solving many-objective optimization engineering problems

41 pages, 5 figures, journal paper

Engineering Applications of Artificial Intelligence, Volume 81, May 2019, Pages 14-28

10.1016/j.engappai.2019.02.003

cs.NE cs.AI

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

Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheuristic optimization frameworks are a practical alternative as they provide a variety of algorithms composed of customized elements, as well as experimental support. Recently, many engineering problems require to optimize multiple or even many objectives, increasing the interest in appropriate metaheuristic algorithms and frameworks that might integrate new specific requirements while maintaining the generality and reusability principles they were conceived for. Based on this idea, this paper introduces JCLEC-MO, a Java framework for both multi- and many-objective optimization that enables engineers to apply, or adapt, a great number of multi-objective algorithms with little coding effort. A case study is developed and explained to show how JCLEC-MO can be used to address many-objective engineering problems, often requiring the inclusion of domain-specific elements, and to analyze experimental outcomes by means of conveniently connected R utilities.

2024-03-01

2402.18743

Cristian Ramirez-Atencia

Cristian Ramirez-Atencia and Victor Rodriguez-Fernandez and David Camacho

A revision on Multi-Criteria Decision Making methods for Multi-UAV Mission Planning Support

Preprint submitted and acepted in Expert Systems with Applications

Expert Systems with Applications, Volume 160, 2020, 113708

10.1016/j.eswa.2020.113708

cs.AI cs.RO

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

Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the Mission Planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions.

2024-03-01

2402.18817

Binh M. Le

Binh M. Le, Simon S. Woo

Gradient Alignment for Cross-Domain Face Anti-Spoofing

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

cs.CV

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

Recent advancements in domain generalization (DG) for face anti-spoofing (FAS) have garnered considerable attention. Traditional methods have focused on designing learning objectives and additional modules to isolate domain-specific features while retaining domain-invariant characteristics in their representations. However, such approaches often lack guarantees of consistent maintenance of domain-invariant features or the complete removal of domain-specific features. Furthermore, most prior works of DG for FAS do not ensure convergence to a local flat minimum, which has been shown to be advantageous for DG. In this paper, we introduce GAC-FAS, a novel learning objective that encourages the model to converge towards an optimal flat minimum without necessitating additional learning modules. Unlike conventional sharpness-aware minimizers, GAC-FAS identifies ascending points for each domain and regulates the generalization gradient updates at these points to align coherently with empirical risk minimization (ERM) gradient updates. This unique approach specifically guides the model to be robust against domain shifts. We demonstrate the efficacy of GAC-FAS through rigorous testing on challenging cross-domain FAS datasets, where it establishes state-of-the-art performance. The code is available at https://github.com/leminhbinh0209/CVPR24-FAS.

2024-03-13