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2402.00038

Andrea Esposito

Antonio Curci and Andrea Esposito

Detecting Brain Tumors through Multimodal Neural Networks

Presented at NeroPRAI 2024 (co-located with ICPRAM 2024). This version did not undergo peer review: refer to the open access version of record (see DOI)

Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2024) - NeroPRAI 2024

10.5220/0012608600003654

eess.IV cs.CV cs.LG q-bio.QM

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

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.

2024-03-18

2402.00046

Sofiene Lassoued

Sofiene Lassoued, Andreas Schwung

Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learning

Journal of Manufacturing Systems (2024)

10.1016/j.jmsy.2024.04.028

cs.AI cs.LG

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

Resource utilization and production process optimization are crucial for companies in today's competitive industrial landscape. Addressing the complexities of job shop scheduling problems (JSSP) is essential to improving productivity, reducing costs, and ensuring timely delivery. We propose PetriRL, a novel framework integrating Petri nets and deep reinforcement learning (DRL) for JSSP optimization. PetriRL capitalizes on the inherent strengths of Petri nets in modelling discrete event systems while leveraging the advantages of a graph structure. The Petri net governs automated components of the process, ensuring adherence to JSSP constraints. This allows for synergistic collaboration with optimization algorithms such as DRL, particularly in critical decision-making. Unlike traditional methods, PetriRL eliminates the need to preprocess JSSP instances into disjunctive graphs and enhances the explainability of process status through its graphical structure based on places and transitions. Additionally, the inherent graph structure of Petri nets enables the dynamic additions of job operations during the inference phase without requiring agent retraining, thus enhancing flexibility. Experimental results demonstrate PetriRL's robust generalization across various instance sizes and its competitive performance on public test benchmarks and randomly generated instances. Results are compared to a wide range of optimization solutions such as heuristics, metaheuristics, and learning-based algorithms. Finally, the added values of the framework's key elements, such as event-based control and action masking, are studied in the ablation study.

2024-05-09

2402.00085

Xuecheng Niu

Xuecheng Niu, Akinori Ito, Takashi Nose

Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning

Accepted to IEEE Access

IEEE Access, vol. 12, pp. 46940-46952, 2024

10.1109/ACCESS.2024.3376418

cs.LG cs.AI

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

Training task-oriented dialog agents based on reinforcement learning is time-consuming and requires a large number of interactions with real users. How to grasp dialog policy within limited dialog experiences remains an obstacle that makes the agent training process less efficient. In addition, most previous frameworks start training by randomly choosing training samples, which differs from the human learning method and hurts the efficiency and stability of training. Therefore, we propose Scheduled Curiosity-Deep Dyna-Q (SC-DDQ), a curiosity-driven curriculum learning framework based on a state-of-the-art model-based reinforcement learning dialog model, Deep Dyna-Q (DDQ). Furthermore, we designed learning schedules for SC-DDQ and DDQ, respectively, following two opposite training strategies: classic curriculum learning and its reverse version. Our results show that by introducing scheduled learning and curiosity, the new framework leads to a significant improvement over the DDQ and Deep Q-learning(DQN). Surprisingly, we found that traditional curriculum learning was not always effective. Specifically, according to the experimental results, the easy-first and difficult-first strategies are more suitable for SC-DDQ and DDQ. To analyze our results, we adopted the entropy of sampled actions to depict action exploration and found that training strategies with high entropy in the first stage and low entropy in the last stage lead to better performance.

2024-05-21

2402.00089

Dr Peter J. Bentley

Soo Ling Lim, Peter J Bentley, Fuyuki Ishikawa

SCAPE: Searching Conceptual Architecture Prompts using Evolution

8 pages

IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence 2024), Yokohama, Japan

cs.NE cs.AI

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

Conceptual architecture involves a highly creative exploration of novel ideas, often taken from other disciplines as architects consider radical new forms, materials, textures and colors for buildings. While today's generative AI systems can produce remarkable results, they lack the creativity demonstrated for decades by evolutionary algorithms. SCAPE, our proposed tool, combines evolutionary search with generative AI, enabling users to explore creative and good quality designs inspired by their initial input through a simple point and click interface. SCAPE injects randomness into generative AI, and enables memory, making use of the built-in language skills of GPT-4 to vary prompts via text-based mutation and crossover. We demonstrate that compared to DALL-E 3, SCAPE enables a 67% improvement in image novelty, plus improvements in quality and effectiveness of use; we show that in just three iterations SCAPE has a 24% image novelty increase enabling effective exploration, plus optimization of images by users. We use more than 20 independent architects to assess SCAPE, who provide markedly positive feedback.

2024-04-03

2402.00312

Trond Arne Undheim

Trond Arne Undheim

The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks

Front. Bioeng. Biotechnol. 12:1359768.

10.3389/fbioe.2024.1359768

q-bio.OT cs.AI

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

AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks and brings about a new set of dual use concerns. The picture is complicated given the vast innovations envisioned to emerge by combining emerging technologies, as AI-enabled synthetic biology potentially scales up bioengineering into industrial biomanufacturing. However, the literature review indicates that goals such as maintaining a reasonable scope for innovation, or more ambitiously to foster a huge bioeconomy don't necessarily contrast with biosafety, but need to go hand in hand. This paper presents a literature review of the issues and describes emerging frameworks for policy and practice that transverse the options of command-and control, stewardship, bottom-up, and laissez-faire governance. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab, from deliberate misuse, or from the public realm, will constantly need to evolve, and adaptive, interactive approaches should emerge. Although biorisk is subject to an established governance regime, and scientists generally adhere to biosafety protocols, even experimental, but legitimate use by scientists could lead to unexpected developments. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations. Given these sets of issues, society needs to rethink how AI-enabled synthetic biology should be governed. The suggested way to visualize the challenge at hand is whack-a-mole governance, although the emerging solutions are perhaps not so different either.

2024-03-08

2402.00491

Aditya Bhattacharya

Aditya Bhattacharya, Simone Stumpf, Lucija Gosak, Gregor Stiglic, Katrien Verbert

EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations

This is a pre-print version only for early release. Please view the conference published version from ACM CHI 2024 to get the latest version of the paper

Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA

10.1145/3613904.3642106

cs.AI cs.HC

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

Explanations in interactive machine-learning systems facilitate debugging and improving prediction models. However, the effectiveness of various global model-centric and data-centric explanations in aiding domain experts to detect and resolve potential data issues for model improvement remains unexplored. This research investigates the influence of data-centric and model-centric global explanations in systems that support healthcare experts in optimising models through automated and manual data configurations. We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts to explore the impact of different explanations on trust, understandability and model improvement. Our results reveal the insufficiency of global model-centric explanations for guiding users during data configuration. Although data-centric explanations enhanced understanding of post-configuration system changes, a hybrid fusion of both explanation types demonstrated the highest effectiveness. Based on our study results, we also present design implications for effective explanation-driven interactive machine-learning systems.

2024-02-02

2402.00525

Lukas Radl

Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, Markus Steinberger

StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering

SIGGRAPH 2024 (Journal Track); Project Page: https://r4dl.github.io/StopThePop/

ACM Transactions on Graphics, volume 43(4), July 2024

cs.GR cs.CV

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

Gaussian Splatting has emerged as a prominent model for constructing 3D representations from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting rendering pipeline relies on several simplifications. Notably, reducing Gaussian to 2D splats with a single view-space depth introduces popping and blending artifacts during view rotation. Addressing this issue requires accurate per-pixel depth computation, yet a full per-pixel sort proves excessively costly compared to a global sort operation. In this paper, we present a novel hierarchical rasterization approach that systematically resorts and culls splats with minimal processing overhead. Our software rasterizer effectively eliminates popping artifacts and view inconsistencies, as demonstrated through both quantitative and qualitative measurements. Simultaneously, our method mitigates the potential for cheating view-dependent effects with popping, ensuring a more authentic representation. Despite the elimination of cheating, our approach achieves comparable quantitative results for test images, while increasing the consistency for novel view synthesis in motion. Due to its design, our hierarchical approach is only 4% slower on average than the original Gaussian Splatting. Notably, enforcing consistency enables a reduction in the number of Gaussians by approximately half with nearly identical quality and view-consistency. Consequently, rendering performance is nearly doubled, making our approach 1.6x faster than the original Gaussian Splatting, with a 50% reduction in memory requirements.

2024-10-10

2402.00593

Ariadna Jim\'enez-Partinen

Ariadna Jim\'enez-Partinen, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodr\'iguez-Capit\'an, Ana I. Molina-Ramos

Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning

IEEE Access, vol. 12, pp. 69229-69239, 2024

10.1109/ACCESS.2024.3401465

eess.IV cs.CV

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

Invasive Coronary Angiography (ICA) images are considered the gold standard for assessing the state of the coronary arteries. Deep learning classification methods are widely used and well-developed in different areas where medical imaging evaluation has an essential impact due to the development of computer-aided diagnosis systems that can support physicians in their clinical procedures. In this paper, a new performance analysis of deep learning methods for binary ICA classification with different lesion degrees is reported. To reach this goal, an annotated dataset of ICA images that contains the ground truth, the location of lesions and seven possible severity degrees ranging between 0% and 100% was employed. The ICA images were divided into 'lesion' or 'non-lesion' patches. We aim to study how binary classification performance is affected by the different lesion degrees considered in the positive class. Therefore, five known convolutional neural network architectures were trained with different input images where different lesion degree ranges were gradually incorporated until considering the seven lesion degrees. Besides, four types of experiments with and without data augmentation were designed, whose F-measure and Area Under Curve (AUC) were computed. Reported results achieved an F-measure and AUC of 92.7% and 98.1%, respectively. However, lesion classification is highly affected by the degree of the lesion intended to classify, with 15% less accuracy when <99% lesion patches are present.

2024-06-25

2402.00676

Raul Fernandez

Raul Fernandez-Fernandez, Juan G. Victores, Carlos Balaguer

Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching

Cognitive Systems Research, Volume 81, September 2023, pages 57 to 63

10.1016/j.cogsys.2023.05.004

cs.RO cs.AI cs.CV cs.LG cs.NE

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

The current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic environments are studied within the cognitive science community as rich, natural, multi-sensory, multi-cultural environments. In this work, we propose the introduction of Reinforcement Learning for improving the control of artistic robot applications. Deep Q-learning Neural Networks (DQN) is one of the most successful algorithms for the implementation of Reinforcement Learning in robotics. DQN methods generate complex control policies for the execution of complex robot applications in a wide set of environments. Current art painting robot applications use simple control laws that limits the adaptability of the frameworks to a set of simple environments. In this work, the introduction of DQN within an art painting robot application is proposed. The goal is to study how the introduction of a complex control policy impacts the performance of a basic art painting robot application. The main expected contribution of this work is to serve as a first baseline for future works introducing DQN methods for complex art painting robot frameworks. Experiments consist of real world executions of human drawn sketches using the DQN generated policy and TEO, the humanoid robot. Results are compared in terms of similarity and obtained reward with respect to the reference inputs

2024-02-02

2402.00677

Raul Fernandez

Raul Fernandez-Fernandez, Juan G. Victores, Jennifer J. Gago, David Estevez, Carlos Balaguer

Neural Policy Style Transfer

Cognitive Systems Research, Volume 72, March 2022, Pages 23 to 32

10.1016/j.cogsys.2021.11.003

cs.RO cs.AI cs.LG cs.NE

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

Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the content. The expressive power of deep neural networks enables encoding a secondary task, which can be described as the style. The Neural Policy Style Transfer (NPST) algorithm is proposed to transfer the style of one policy to another, while maintaining the content of the latter. Different policies are defined via Deep Q-Network architectures. These models are trained using demonstrations through Inverse Reinforcement Learning. Two different sets of user demonstrations are performed, one for content and other for style. Different styles are encoded as defined by user demonstrations. The generated policy is the result of feeding a content policy and a style policy to the NPST algorithm. Experiments are performed in a catch-ball game inspired by the Deep Reinforcement Learning classical Atari games; and a real-world painting scenario with a full-sized humanoid robot, based on previous works of the authors. The implementation of three different Q-Network architectures (Shallow, Deep and Deep Recurrent Q-Network) to encode the policies within the NPST framework is proposed and the results obtained in the experiments with each of these architectures compared.

2024-02-02

2402.00678

Raul Fernandez

Raul Fernandez-Fernandez, Juan G. Victores, David Estevez, and Carlos Balaguer

Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications

Sensors, Volume 18, Issue 11, 2018

10.3390/s18113818

cs.RO cs.AI cs.LG

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

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through demonstrations. In classical frameworks, actions are modeled using joint or Cartesian space trajectories. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these methods, as it encodes actions as changes of any feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. Current strategies involve performing evaluations in a simulation, transferring the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within CGDA: naive PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO). The second approach studied the introduction of geometrical and velocity constraints within CGDA. The effects of both approaches were analyzed and compared in the wax and paint actions, two CGDA commonly studied use cases. Results from this paper depict an important reduction in the number of evaluations.

2024-02-02

2402.00680

Wei Jiang

Wei Jiang, Junru Li, Kai Zhang, Li Zhang

LVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression

Accepted to ICASSP 2024 (lecture presentation). The first attempt to use cross attention for bits-free motion estimation and motion compensation

ICASSP (International Conference on Acoustics, Speech, and Signal Processing) pp. 2955-2959, 2024

10.1109/icassp48485.2024.10448081

eess.IV cs.CV

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

Existing learned video compression models employ flow net or deformable convolutional networks (DCN) to estimate motion information. However, the limited receptive fields of flow net and DCN inherently direct their attentiveness towards the local contexts. Global contexts, such as large-scale motions and global correlations among frames are ignored, presenting a significant bottleneck for capturing accurate motions. To address this issue, we propose a joint local and global motion compensation module (LGMC) for leaned video coding. More specifically, we adopt flow net for local motion compensation. To capture global context, we employ the cross attention in feature domain for motion compensation. In addition, to avoid the quadratic complexity of vanilla cross attention, we divide the softmax operations in attention into two independent softmax operations, leading to linear complexity. To validate the effectiveness of our proposed LGMC, we integrate it with DCVC-TCM and obtain learned video compression with joint local and global motion compensation (LVC-LGMC). Extensive experiments demonstrate that our LVC-LGMC has significant rate-distortion performance improvements over baseline DCVC-TCM.

2024-04-09

2402.00724

Jan Valo\v{s}ek

Jan Valosek, Theo Mathieu, Raphaelle Schlienger, Olivia S. Kowalczyk, Julien Cohen-Adad

Automatic Segmentation of the Spinal Cord Nerve Rootlets

Imaging Neuroscience, 2 (2024) 1-14

10.1162/imag_a_00218

cs.CV cs.LG

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

Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 +- 0.16 (mean +- standard deviation across testing images and rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation <= 1.41 %), as well as low inter-session variability (coefficient of variation <= 1.30 %) indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.

2024-07-26

2402.00856

Haozhe Ji

Haozhe Ji, Cheng Lu, Yilin Niu, Pei Ke, Hongning Wang, Jun Zhu, Jie Tang, Minlie Huang

Towards Efficient Exact Optimization of Language Model Alignment

24 pages, 9 figures

Forty-first International Conference on Machine Learning (ICML 2024)

cs.CL

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

The alignment of language models with human preferences is vital for their application in real-world tasks. The problem is formulated as optimizing the model's policy to maximize the expected reward that reflects human preferences with minimal deviation from the initial policy. While considered as a straightforward solution, reinforcement learning (RL) suffers from high variance in policy updates, which impedes efficient policy improvement. Recently, direct preference optimization (DPO) was proposed to directly optimize the policy from preference data. However, we show that DPO derived based on the optimal solution of the problem leads to a compromised mean-seeking approximation of the optimal solution in practice. In this paper, we propose efficient exact optimization (EXO) of the alignment objective. EXO is guaranteed to optimize in the same direction as RL algorithms asymptotically for arbitrary policy parametrization. This leads to the same mode-seeking solution, while enables efficient optimization by circumventing the complexities of RL. We also compare our method to DPO with both theoretical and empirical analyses, and further demonstrate the advantages of our method over existing approaches on realistic human preference data. Code is available at https://github.com/haozheji/exact-optimization.

2024-06-06

2402.00994

Kirolos Ataallah

Kirolos Attallah, Girgis Zaky, Nourhan Abdelrhim, Kyrillos Botros, Amjad Dife, and Nermin Negied

A Cost-Efficient Approach for Creating Virtual Fitting Room using Generative Adversarial Networks (GANs)

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 1, 2024

10.14569/IJACSA.2024.0150132

cs.CV cs.LG

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

Customers all over the world want to see how the clothes fit them or not before purchasing. Therefore, customers by nature prefer brick-and-mortar clothes shopping so they can try on products before purchasing them. But after the Pandemic of COVID19 many sellers either shifted to online shopping or closed their fitting rooms which made the shopping process hesitant and doubtful. The fact that the clothes may not be suitable for their buyers after purchase led us to think about using new AI technologies to create an online platform or a virtual fitting room (VFR) in the form of a mobile application and a deployed model using a webpage that can be embedded later to any online store where they can try on any number of cloth items without physically trying them. Besides, it will save much searching time for their needs. Furthermore, it will reduce the crowding and headache in the physical shops by applying the same technology using a special type of mirror that will enable customers to try on faster. On the other hand, from business owners' perspective, this project will highly increase their online sales, besides, it will save the quality of the products by avoiding physical trials issues. The main approach used in this work is applying Generative Adversarial Networks (GANs) combined with image processing techniques to generate one output image from two input images which are the person image and the cloth image. This work achieved results that outperformed the state-of-the-art approaches found in literature.

2024-02-05

2402.01018

Weijie Xu

Weijie Xu, Zicheng Huang, Wenxiang Hu, Xi Fang, Rajesh Kumar Cherukuri, Naumaan Nayyar, Lorenzo Malandri, Srinivasan H. Sengamedu

HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent

13 pages, 9 figures

EACL 2024

cs.CL cs.AI

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

Recent advancements in Large Language Models (LLMs) have been reshaping Natural Language Processing (NLP) task in several domains. Their use in the field of Human Resources (HR) has still room for expansions and could be beneficial for several time consuming tasks. Examples such as time-off submissions, medical claims filing, and access requests are noteworthy, but they are by no means the sole instances. However, the aforementioned developments must grapple with the pivotal challenge of constructing a high-quality training dataset. On one hand, most conversation datasets are solving problems for customers not employees. On the other hand, gathering conversations with HR could raise privacy concerns. To solve it, we introduce HR-Multiwoz, a fully-labeled dataset of 550 conversations spanning 10 HR domains to evaluate LLM Agent. Our work has the following contributions: (1) It is the first labeled open-sourced conversation dataset in the HR domain for NLP research. (2) It provides a detailed recipe for the data generation procedure along with data analysis and human evaluations. The data generation pipeline is transferable and can be easily adapted for labeled conversation data generation in other domains. (3) The proposed data-collection pipeline is mostly based on LLMs with minimal human involvement for annotation, which is time and cost-efficient.

2024-02-05

2402.01182

Meishan Zhang

Meishan Zhang, Bin Wang, Hao Fei, Min Zhang

In-Context Learning for Few-Shot Nested Named Entity Recognition

5 figures

ICASSP 2024

cs.CL

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

In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address. This leads to the development of few-shot nested NER, where the prevalence of pretrained language models with in-context learning (ICL) offers promising solutions. In this work, we introduce an effective and innovative ICL framework for the setting of few-shot nested NER. We improve the ICL prompt by devising a novel example demonstration selection mechanism, EnDe retriever. In EnDe retriever, we employ contrastive learning to perform three types of representation learning, in terms of semantic similarity, boundary similarity, and label similarity, to generate high-quality demonstration examples. Extensive experiments over three nested NER and four flat NER datasets demonstrate the efficacy of our system.

2024-02-05

2402.01195

Henrik Schopmans

Henrik Schopmans, Pascal Friederich

Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations

Proceedings of the 41st International Conference on Machine Learning (ICML 2024), PMLR 235:43804-43827, 2024

cs.LG cs.AI physics.chem-ph

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

Efficient sampling of the Boltzmann distribution of molecular systems is a long-standing challenge. Recently, instead of generating long molecular dynamics simulations, generative machine learning methods such as normalizing flows have been used to learn the Boltzmann distribution directly, without samples. However, this approach is susceptible to mode collapse and thus often does not explore the full configurational space. In this work, we address this challenge by separating the problem into two levels, the fine-grained and coarse-grained degrees of freedom. A normalizing flow conditioned on the coarse-grained space yields a probabilistic connection between the two levels. To explore the configurational space, we employ coarse-grained simulations with active learning which allows us to update the flow and make all-atom potential energy evaluations only when necessary. Using alanine dipeptide as an example, we show that our methods obtain a speedup to molecular dynamics simulations of approximately 15.9 to 216.2 compared to the speedup of 4.5 of the current state-of-the-art machine learning approach.

2024-08-06

2402.01219

Roberto Natella

Roberto Natella, Pietro Liguori, Cristina Improta, Bojan Cukic, Domenico Cotroneo

AI Code Generators for Security: Friend or Foe?

Dataset available at: https://github.com/dessertlab/violent-python

IEEE Security & Privacy, Early Access, February 2024

10.1109/MSEC.2024.3355713

cs.CR cs.AI cs.SE

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

Recent advances of artificial intelligence (AI) code generators are opening new opportunities in software security research, including misuse by malicious actors. We review use cases for AI code generators for security and introduce an evaluation benchmark.

2024-02-05

2402.01393

Carmen Martin-Turrero

Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein, Pietro Zanuttigh, Vincent Parret

ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data

Originally published in the Proceedings of Machine Learning Research ICML 2024

Proceedings of the 41st International Conference on Machine Learning (2024), in Proceedings of Machine Learning Research 235:48837-48854

cs.CV cs.LG cs.NE

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

We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method. These embeddings are then processed by a transformer model trained for object and gesture recognition. Using this approach, we achieve performances at the state-of-the-art with a lower latency than competitors. We also demonstrate that our asynchronous model can operate at any desired sampling rate.

2024-07-31

2402.01461

Bruno Berenguel-Baeta

Bruno Berenguel-Baeta, Antoine N. Andre, Guillaume Caron, Jesus Bermudez-Cameo, Jose J. Guerrero

Visual Gyroscope: Combination of Deep Learning Features and Direct Alignment for Panoramic Stabilization

IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 6444-6447, 2023

10.1109/CVPRW59228.2023.00685

cs.CV

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

In this article we present a visual gyroscope based on equirectangular panoramas. We propose a new pipeline where we take advantage of combining three different methods to obtain a robust and accurate estimation of the attitude of the camera. We quantitatively and qualitatively validate our method on two image sequences taken with a $360^\circ$ dual-fisheye camera mounted on different aerial vehicles.

2024-02-05

2402.01466

Bruno Berenguel-Baeta

Bruno Berenguel-Baeta, Jesus Bermudez-Cameo, Jose J. Guerrero

Scaled 360 layouts: Revisiting non-central panoramas

arXiv admin note: substantial text overlap with arXiv:2401.17058

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3702-3705) 2021

10.1109/CVPRW53098.2021.00410

cs.CV

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

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts.

2024-02-05

2402.01472

Pietro Melzi

Pietro Melzi and Christian Rathgeb and Ruben Tolosana and Ruben Vera-Rodriguez and Aythami Morales and Dominik Lawatsch and Florian Domin and Maxim Schaubert

Synthetic Data for the Mitigation of Demographic Biases in Face Recognition

8 pages, 3 figures

Proceedings of the International Joint Conference on Biometrics 2023, special session on "Synthetic Data in Biometrics"

cs.CV

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

This study investigates the possibility of mitigating the demographic biases that affect face recognition technologies through the use of synthetic data. Demographic biases have the potential to impact individuals from specific demographic groups, and can be identified by observing disparate performance of face recognition systems across demographic groups. They primarily arise from the unequal representations of demographic groups in the training data. In recent times, synthetic data have emerged as a solution to some problems that affect face recognition systems. In particular, during the generation process it is possible to specify the desired demographic and facial attributes of images, in order to control the demographic distribution of the synthesized dataset, and fairly represent the different demographic groups. We propose to fine-tune with synthetic data existing face recognition systems that present some demographic biases. We use synthetic datasets generated with GANDiffFace, a novel framework able to synthesize datasets for face recognition with controllable demographic distribution and realistic intra-class variations. We consider multiple datasets representing different demographic groups for training and evaluation. Also, we fine-tune different face recognition systems, and evaluate their demographic fairness with different metrics. Our results support the proposed approach and the use of synthetic data to mitigate demographic biases in face recognition.

2024-02-05

2402.01510

Pratik K. Biswas

Pratik K. Biswas

A Hybrid Strategy for Chat Transcript Summarization

Journal Paper (13 Pages, 8 Figures, 4 Tables). arXiv admin note: text overlap with arXiv:2103.10599

IEEE Access, October 2024

10.1109/ACCESS.2024.3473968

cs.CL

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

Text summarization is the process of condensing a piece of text to fewer sentences, while still preserving its content. Chat transcript, in this context, is a textual copy of a digital or online conversation between a customer (caller) and agent(s). This paper presents an indigenously (locally) developed hybrid method that first combines extractive and abstractive summarization techniques in compressing ill-punctuated or un-punctuated chat transcripts to produce more readable punctuated summaries and then optimizes the overall quality of summarization through reinforcement learning. Extensive testing, evaluations, comparisons, and validation have demonstrated the efficacy of this approach for large-scale deployment of chat transcript summarization, in the absence of manually generated reference (annotated) summaries.

2024-10-14

2402.01557

Nergis Tomen

Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert

Deep Continuous Networks

Presented at ICML 2021

In International Conference on Machine Learning 2021 Jul 1 (pp. 10324-10335). PMLR

cs.CV

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

CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research. However, fruitful cross-field research is hampered by conventional CNN architectures being based on spatially and depthwise discrete representations, which cannot accommodate certain aspects of biological complexity such as continuously varying receptive field sizes and dynamics of neuronal responses. Here we propose deep continuous networks (DCNs), which combine spatially continuous filters, with the continuous depth framework of neural ODEs. This allows us to learn the spatial support of the filters during training, as well as model the continuous evolution of feature maps, linking DCNs closely to biological models. We show that DCNs are versatile and highly applicable to standard image classification and reconstruction problems, where they improve parameter and data efficiency, and allow for meta-parametrization. We illustrate the biological plausibility of the scale distributions learned by DCNs and explore their performance in a neuroscientifically inspired pattern completion task. Finally, we investigate an efficient implementation of DCNs by changing input contrast.

2024-02-05

2402.01654

Bal\'azs Andr\'as Tolnai

Bal\'azs Andr\'as Tolnai and Zheng Ma and Bo N{\o}rregaard J{\o}rgensen

A Scoping Review of Energy Load Disaggregation

Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science, vol 14116

10.1007/978-3-031-49011-8_17

eess.SP cs.AI cs.CY

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

Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper con-ducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.

2024-02-08

2402.01668

Enrique Yeguas

Enrique Yeguas-Bol\'ivar, Jos\'e M. Alcalde-Llergo, Pilar Aparicio-Mart\'inez, Juri Taborri, Andrea Zingoni and Sara Pinzi

Determining the Difficulties of Students With Dyslexia via Virtual Reality and Artificial Intelligence: An Exploratory Analysis

7 pages, 5 figures, 3 tables, MetroXRAINE 2022 Conference, VRAILEXIA european project

2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Rome, Italy, 2022, pp. 585-590

10.1109/MetroXRAINE54828.2022.9967589

cs.CY cs.AI cs.CV cs.GR cs.HC

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

Learning disorders are neurological conditions that affect the brain's ability to interconnect communication areas. Dyslexic students experience problems with reading, memorizing, and exposing concepts; however the magnitude of these can be mitigated through both therapies and the creation of compensatory mechanisms. Several efforts have been made to mitigate these issues, leading to the creation of digital resources for students with specific learning disorders attending primary and secondary education levels. Conversely, a standard approach is still missed in higher education. The VRAIlexia project has been created to tackle this issue by proposing two different tools: a mobile application integrating virtual reality (VR) to collect data quickly and easily, and an artificial intelligencebased software (AI) to analyze the collected data for customizing the supporting methodology for each student. The first one has been created and is being distributed among dyslexic students in Higher Education Institutions, for the conduction of specific psychological and psychometric tests. The second tool applies specific artificial intelligence algorithms to the data gathered via the application and other surveys. These AI techniques have allowed us to identify the most relevant difficulties faced by the students' cohort. Our different models have obtained around 90\% mean accuracy for predicting the support tools and learning strategies.

2024-02-06

2402.01672

Sao Mai Nguyen

Louis Annabi (Flowers, U2IS), Sao Mai Nguyen

Prerequisite Structure Discovery in Intelligent Tutoring Systems

2023 IEEE International Conference on Development and Learning (ICDL), Nov 2023, Macau, China. pp.176-181

10.1109/icdl55364.2023.10364416

cs.CY cs.AI cs.LG

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

This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students.

2024-02-06

2402.01673

Sascha Ossowski

Jos\'e-Antonio Santos, Alberto Fern\'andez, Mar Moreno-Rebato, Holger Billhardt, Jos\'e-A. Rodr\'iguez-Garc\'ia, Sascha Ossowski

Legal and ethical implications of applications based on agreement technologies: the case of auction-based road intersections

Artif. Intell. Law 28(4): 385-414 (2020)

10.1007/s10506-019-09259-8

cs.CY cs.AI

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

Agreement Technologies refer to a novel paradigm for the construction of distributed intelligent systems, where autonomous software agents negotiate to reach agreements on behalf of their human users. Smart Cities are a key application domain for Agreement Technologies. While several proofs of concept and prototypes exist, such systems are still far from ready for being deployed in the real-world. In this paper we focus on a novel method for managing elements of smart road infrastructures of the future, namely the case of auction-based road intersections. We show that, even though the key technological elements for such methods are already available, there are multiple non-technical issues that need to be tackled before they can be applied in practice. For this purpose, we analyse legal and ethical implications of auction-based road intersections in the context of international regulations and from the standpoint of the Spanish legislation. From this exercise, we extract a set of required modifications, of both technical and legal nature, which need to be addressed so as to pave the way for the potential real-world deployment of such systems in a future that may not be too far away.

2024-02-06

2402.01676

Jennifer Hu

Jennifer Hu, Kyle Mahowald, Gary Lupyan, Anna Ivanova, Roger Levy

Language models align with human judgments on key grammatical constructions

Published in PNAS at https://www.pnas.org/doi/10.1073/pnas.2400917121 as response to Dentella et al. (2023)

Proceedings of the National Academy of Sciences, 121(36), e2400917121 (2024)

10.1073/pnas.2400917121

cs.CL cs.AI

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

Do large language models (LLMs) make human-like linguistic generalizations? Dentella et al. (2023) ("DGL") prompt several LLMs ("Is the following sentence grammatically correct in English?") to elicit grammaticality judgments of 80 English sentences, concluding that LLMs demonstrate a "yes-response bias" and a "failure to distinguish grammatical from ungrammatical sentences". We re-evaluate LLM performance using well-established practices and find that DGL's data in fact provide evidence for just how well LLMs capture human behaviors. Models not only achieve high accuracy overall, but also capture fine-grained variation in human linguistic judgments.

2024-09-02

2402.01686

Liliana Marie Prikler

Liliana Marie Prikler, Franz Wotawa (Graz University of Technology, Institute for Software Technology)

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

2023 10th International Conference on Dependable Systems and Their Applications (DSA), Tokyo, Japan, 2023, pp. 431-442

10.1109/DSA59317.2023.00058

cs.CY cs.AI cs.HC

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

In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -- and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.

2024-02-06

2402.01712

Hamideh Ghanadian

Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

IEEE Access

10.1109/ACCESS.2024.3358206

cs.CL cs.AI cs.LG

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

Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models. To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection. Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation. In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures. When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging from 0.75 to 0.87. Our synthetic data-driven method, informed by social factors, offers consistent F1-scores of 0.82 for both models, suggesting that the richness of topics in synthetic data can bridge the performance gap across different model complexities. Most impressively, when we combined a mere 30% of the UMD dataset with our synthetic data, we witnessed a substantial increase in performance, achieving an F1-score of 0.88 on the UMD test set. Such results underscore the cost-effectiveness and potential of our approach in confronting major challenges in the field, such as data scarcity and the quest for diversity in data representation.

2024-02-06

2402.01714

Sourav Ghosh

Vibhav Agarwal, Sourav Ghosh, Harichandana BSS, Himanshu Arora, Barath Raj Kandur Raja

TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy

Published in the IEEE/ACM Transactions on Audio, Speech, and Language Processing. (Sourav Ghosh and Vibhav Agarwal contributed equally to this work.)

IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 1173-1184, 2024

10.1109/TASLP.2024.3353574

cs.CL cs.AI cs.LG

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

Data-to-text (D2T) generation is a crucial task in many natural language understanding (NLU) applications and forms the foundation of task-oriented dialog systems. In the context of conversational AI solutions that can work directly with local data on the user's device, architectures utilizing large pre-trained language models (PLMs) are impractical for on-device deployment due to a high memory footprint. To this end, we propose TrICy, a novel lightweight framework for an enhanced D2T task that generates text sequences based on the intent in context and may further be guided by user-provided triggers. We leverage an attention-copy mechanism to predict out-of-vocabulary (OOV) words accurately. Performance analyses on E2E NLG dataset (BLEU: 66.43%, ROUGE-L: 70.14%), WebNLG dataset (BLEU: Seen 64.08%, Unseen 52.35%), and our Custom dataset related to text messaging applications, showcase our architecture's effectiveness. Moreover, we show that by leveraging an optional trigger input, data-to-text generation quality increases significantly and achieves the new SOTA score of 69.29% BLEU for E2E NLG. Furthermore, our analyses show that TrICy achieves at least 24% and 3% improvement in BLEU and METEOR respectively over LLMs like GPT-3, ChatGPT, and Llama 2. We also demonstrate that in some scenarios, performance improvement due to triggers is observed even when they are absent in training.

2024-02-07

2402.01716

Hapnes Toba

H. Toba, Y. T. Hernita, M. Ayub, M. C. Wijanto

Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

11 pages, 7 figures

International Journal of Evaluation and Research in Education 13 (2024) 80-90

10.11591/ijere.v13i1.26024

cs.CY cs.CL cs.LG

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

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom\'s epistemic taxonomy based on textual comments in educational discussion forums. Our proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom\'s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.

2024-02-06

2402.01720

Goitom Ybrah Hailu Mr

Goitom Ybrah Hailu, Shishay Welay

Deep Learning Based Amharic Chatbot for FAQs in Universities

Machine Learning (cs.LG), V1, 2024

10.48550/arXiv.2402.01720

AksumUniv-CS-2024

cs.CY cs.AI cs.CL cs.LG

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

University students often spend a considerable amount of time seeking answers to common questions from administrators or teachers. This can become tedious for both parties, leading to a need for a solution. In response, this paper proposes a chatbot model that utilizes natural language processing and deep learning techniques to answer frequently asked questions (FAQs) in the Amharic language. Chatbots are computer programs that simulate human conversation through the use of artificial intelligence (AI), acting as a virtual assistant to handle questions and other tasks. The proposed chatbot program employs tokenization, normalization, stop word removal, and stemming to analyze and categorize Amharic input sentences. Three machine learning model algorithms were used to classify tokens and retrieve appropriate responses: Support Vector Machine (SVM), Multinomial Na\"ive Bayes, and deep neural networks implemented through TensorFlow, Keras, and NLTK. The deep learning model achieved the best results with 91.55% accuracy and a validation loss of 0.3548 using an Adam optimizer and SoftMax activation function. The chatbot model was integrated with Facebook Messenger and deployed on a Heroku server for 24-hour accessibility. The experimental results demonstrate that the chatbot framework achieved its objectives and effectively addressed challenges such as Amharic Fidel variation, morphological variation, and lexical gaps. Future research could explore the integration of Amharic WordNet to narrow the lexical gap and support more complex questions.

2024-02-08

2402.01728

Weimin Fu

Weimin Fu, Shijie Li, Yifang Zhao, Haocheng Ma, Raj Dutta, Xuan Zhang, Kaichen Yang, Yier Jin, Xiaolong Guo

Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge

6 pages, 6 figures

29th IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC); 2024 January; Incheon Songdo Convensia, South Korea

cs.CL cs.AI cs.AR

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

In the rapidly evolving semiconductor industry, where research, design, verification, and manufacturing are intricately linked, the potential of Large Language Models to revolutionize hardware design and security verification is immense. The primary challenge, however, lies in the complexity of hardware specific issues that are not adequately addressed by the natural language or software code knowledge typically acquired during the pretraining stage. Additionally, the scarcity of datasets specific to the hardware domain poses a significant hurdle in developing a foundational model. Addressing these challenges, this paper introduces Hardware Phi 1.5B, an innovative large language model specifically tailored for the hardware domain of the semiconductor industry. We have developed a specialized, tiered dataset comprising small, medium, and large subsets and focused our efforts on pretraining using the medium dataset. This approach harnesses the compact yet efficient architecture of the Phi 1.5B model. The creation of this first pretrained, hardware domain specific large language model marks a significant advancement, offering improved performance in hardware design and verification tasks and illustrating a promising path forward for AI applications in the semiconductor sector.

2024-02-07

2402.01738

Yaiza Aragon\'es-Soria

Yaiza Aragon\'es-Soria and Manuel Oriol

C4Q: A Chatbot for Quantum

Paper accepted in the 5th International Workshop on Quantum Software Engineering (Q-SE 2024)

In Proceedings of the 5th ACM/IEEE International Workshop on Quantum Software Engineering (Q-SE 2024). Association for Computing Machinery, New York, NY, USA, 49-52

10.1145/3643667.3648222

cs.CL quant-ph

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

Quantum computing is a growing field that promises many real-world applications such as quantum cryptography or quantum finance. The number of people able to use quantum computing is however still very small. This limitation comes from the difficulty to understand the concepts and to know how to start coding. Therefore, there is a need for tools that can assist non-expert in overcoming this complexity. One possibility would be to use existing conversational agents. Unfortunately ChatGPT and other Large-Language Models produce inaccurate results. This article presents C4Q, a chatbot that answers accurately basic questions and guides users when trying to code quantum programs. Contrary to other approaches C4Q uses a pre-trained large language model only to discover and classify user requests. It then generates an accurate answer using an own engine. Thanks to this architectural design, C4Q's answers are always correct, and thus C4Q can become a support tool that makes quantum computing more available to non-experts.

2024-08-27

2402.01746

Liang Zhang

Liang Zhang, Jionghao Lin, Conrad Borchers, Meng Cao, Xiangen Hu

3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems

LAK 2024: International Workshop on Generative AI for Learning Analytics (GenAI-LA)

cs.CY cs.AI cs.LG

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

Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring Systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN's superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology.

2024-02-06

2402.01775

Rosana Montes

Rosana Montes, Cristina Zuheros, Jeovani M. Morales, Noe Zerme\~no, Jer\'onimo Duran, Francsico Herrera

Design and consensus content validity of the questionnaire for b-learning education: A 2-Tuple Fuzzy Linguistic Delphi based Decision Support Tool

47 pages, 7 figures

Open Access Volume 147 November 2023 Article number 110755

10.1016/j.asoc.2023.110755

cs.CY cs.CL cs.HC

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

Classic Delphi and Fuzzy Delphi methods are used to test content validity of data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solves it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution.

2024-02-08

2402.01817

Subbarao Kambhampati

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Saldyt, Anil Murthy

LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

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

cs.AI cs.LG

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

There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks. On one side are over-optimistic claims that LLMs can indeed do these tasks with just the right prompting or self-verification strategies. On the other side are perhaps over-pessimistic claims that all that LLMs are good for in planning/reasoning tasks are as mere translators of the problem specification from one syntactic format to another, and ship the problem off to external symbolic solvers. In this position paper, we take the view that both these extremes are misguided. We argue that auto-regressive LLMs cannot, by themselves, do planning or self-verification (which is after all a form of reasoning), and shed some light on the reasons for misunderstandings in the literature. We will also argue that LLMs should be viewed as universal approximate knowledge sources that have much more meaningful roles to play in planning/reasoning tasks beyond simple front-end/back-end format translators. We present a vision of {\bf LLM-Modulo Frameworks} that combine the strengths of LLMs with external model-based verifiers in a tighter bi-directional interaction regime. We will show how the models driving the external verifiers themselves can be acquired with the help of LLMs. We will also argue that rather than simply pipelining LLMs and symbolic components, this LLM-Modulo Framework provides a better neuro-symbolic approach that offers tighter integration between LLMs and symbolic components, and allows extending the scope of model-based planning/reasoning regimes towards more flexible knowledge, problem and preference specifications.

2024-06-13

2402.01821

Akshay Kumar Jagadish

Akshay K. Jagadish, Julian Coda-Forno, Mirko Thalmann, Eric Schulz, and Marcel Binz

Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks

27 pages (9 pages of main text, 4 pages of references, and 14 pages of appendix), 13 figures, and 7 Tables

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/

Ecological rationality refers to the notion that humans are rational agents adapted to their environment. However, testing this theory remains challenging due to two reasons: the difficulty in defining what tasks are ecologically valid and building rational models for these tasks. In this work, we demonstrate that large language models can generate cognitive tasks, specifically category learning tasks, that match the statistics of real-world tasks, thereby addressing the first challenge. We tackle the second challenge by deriving rational agents adapted to these tasks using the framework of meta-learning, leading to a class of models called ecologically rational meta-learned inference (ERMI). ERMI quantitatively explains human data better than seven other cognitive models in two different experiments. It additionally matches human behavior on a qualitative level: (1) it finds the same tasks difficult that humans find difficult, (2) it becomes more reliant on an exemplar-based strategy for assigning categories with learning, and (3) it generalizes to unseen stimuli in a human-like way. Furthermore, we show that ERMI's ecologically valid priors allow it to achieve state-of-the-art performance on the OpenML-CC18 classification benchmark.

2024-05-29

2402.01828

Izhak Shafran

Mingqiu Wang, Izhak Shafran, Hagen Soltau, Wei Han, Yuan Cao, Dian Yu, Laurent El Shafey

Retrieval Augmented End-to-End Spoken Dialog Models

Proc. ICASSP 2024

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

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

We recently developed SLM, a joint speech and language model, which fuses a pretrained foundational speech model and a large language model (LLM), while preserving the in-context learning capability intrinsic to the pretrained LLM. In this paper, we apply SLM to speech dialog applications where the dialog states are inferred directly from the audio signal. Task-oriented dialogs often contain domain-specific entities, i.e., restaurants, hotels, train stations, and city names, which are difficult to recognize, however, critical for the downstream applications. Inspired by the RAG (retrieval-augmented generation) paradigm, we propose a retrieval augmented SLM (ReSLM) that overcomes this weakness. We first train a speech retriever to retrieve text entities mentioned in the audio. The retrieved entities are then added as text inputs to the underlying SLM to bias model predictions. We evaluated ReSLM on speech MultiWoz task (DSTC-11 challenge), and found that this retrieval augmentation boosts model performance, achieving joint goal accuracy (38.6% vs 32.7%), slot error rate (20.6% vs 24.8%) and ASR word error rate (5.5% vs 6.7%). While demonstrated on dialog state tracking, our approach is broadly applicable to other speech tasks requiring contextual information or domain-specific entities, such as contextual ASR with biasing capability.

2024-02-08

2402.01849

Elena Monta\~n\'es

Laura Fern\'andez D\'iaz, Miriam Fern\'andez D\'iaz, Jos\'e Ram\'on Quevedo, Elena Monta\~n\'es

Capturing waste collection planning expert knowledge in a fitness function through preference learning

Engineering Applications of Artificial Intelligence 2021 Volume 99 104113

10.1016/j.engappai.2020.104113

cs.LG cs.AI

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

This paper copes with the COGERSA waste collection process. Up to now, experts have been manually designed the process using a trial and error mechanism. This process is not globally optimized, since it has been progressively and locally built as council demands appear. Planning optimization algorithms usually solve it, but they need a fitness function to evaluate a route planning quality. The drawback is that even experts are not able to propose one in a straightforward way due to the complexity of the process. Hence, the goal of this paper is to build a fitness function though a preference framework, taking advantage of the available expert knowledge and expertise. Several key performance indicators together with preference judgments are carefully established according to the experts for learning a promising fitness function. Particularly, the additivity property of them makes the task be much more affordable, since it allows to work with routes rather than with route plannings. Besides, a feature selection analysis is performed over such indicators, since the experts suspect of a potential existing (but unknown) redundancy among them. The experiment results confirm this hypothesis, since the best $C-$index ($98\%$ against around $94\%$) is reached when 6 or 8 out of 21 indicators are taken. Particularly, truck load seems to be a highly promising key performance indicator, together to the travelled distance along non-main roads. A comparison with other existing approaches shows that the proposed method clearly outperforms them, since the $C-$index goes from $72\%$ or $90\%$ to $98\%$.

2024-02-07

2402.01916

Francisco J. Ribadas-Pena

Francisco J. Ribadas-Pena, Shuyuan Cao, Elmurod Kuriyozov

CoLe and LYS at BioASQ MESINESP8 Task: similarity based descriptor assignment in Spanish

Accepted at the 8th BioASQ Workshop at the 11th Conference and Labs of the Evaluation Forum (CLEF) 2020. 11 pages

Working Notes of CLEF 2020. Vol. 2696 of CEUR Workshop Proceedings (CEUR-WS.org)

cs.IR cs.CL cs.LG

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

In this paper, we describe our participation in the MESINESP Task of the BioASQ biomedical semantic indexing challenge. The participating system follows an approach based solely on conventional information retrieval tools. We have evaluated various alternatives for extracting index terms from IBECS/LILACS documents in order to be stored in an Apache Lucene index. Those indexed representations are queried using the contents of the article to be annotated and a ranked list of candidate labels is created from the retrieved documents. We also have evaluated a sort of limited Label Powerset approach which creates meta-labels joining pairs of DeCS labels with high co-occurrence scores, and an alternative method based on label profile matching. Results obtained in official runs seem to confirm the suitability of this approach for languages like Spanish.

2024-02-06

2402.01935

Dejiao Zhang

Dejiao Zhang, Wasi Ahmad, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang

Code Representation Learning At Scale

10 pages

ICLR 2024

cs.CL

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

Recent studies have shown that code language models at scale demonstrate significant performance gains on downstream tasks, i.e., code generation. However, most of the existing works on code representation learning train models at a hundred million parameter scale using very limited pretraining corpora. In this work, we fuel code representation learning with a vast amount of code data via a two-stage pretraining scheme. We first train the encoders via a mix that leverages both randomness in masking language modeling and the structure aspect of programming language. We then enhance the representations via contrastive learning with hard negative and hard positive constructed in an unsupervised manner. We establish an off-the-shelf encoder model that persistently outperforms the existing models on a wide variety of downstream tasks by large margins. To comprehend the factors contributing to successful code representation learning, we conduct detailed ablations and share our findings on (i) a customized and effective token-level denoising scheme for source code; (ii) the importance of hard negatives and hard positives; (iii) how the proposed bimodal contrastive learning boost the cross-lingual semantic search performance; and (iv) how the pretraining schemes decide the downstream task performance scales with the model size.

2024-02-06

2402.01963

Francisco J. Ribadas-Pena

Francisco J. Ribadas-Pena, Shuyuan Cao, V\'ictor M. Darriba Bilbao

Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders

22 pages, 4 figures

Mathematics 2022, 10(16), 2867

10.3390/math10162867

cs.LG cs.CL cs.IR

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

In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is an evolution of the traditional k-Nearest Neighbors algorithm which uses a large autoencoder trained to map the large label space to a reduced size latent space and to regenerate the predicted labels from this latent space. We have evaluated our proposal in a large portion of the MEDLINE biomedical document collection which uses the Medical Subject Headings (MeSH) thesaurus as a controlled vocabulary. In our experiments we propose and evaluate several document representation approaches and different label autoencoder configurations.

2024-02-06

2402.02094

Wenjia Xu

Wenjia Xu, Jiuniu Wang, Zhiwei Wei, Mugen Peng, Yirong Wu

Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification

Published in ISPRS P&RS. The code is available at https://github.com/wenjiaXu/RS_Scene_ZSL

ISPRS Journal of Photogrammetry and Remote Sensing, Volume 198, 2023, Pages 140-152

10.1016/j.isprsjprs.2023.02.012

cs.CV cs.AI

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

Deep neural networks have achieved promising progress in remote sensing (RS) image classification, for which the training process requires abundant samples for each class. However, it is time-consuming and unrealistic to annotate labels for each RS category, given the fact that the RS target database is increasing dynamically. Zero-shot learning (ZSL) allows for identifying novel classes that are not seen during training, which provides a promising solution for the aforementioned problem. However, previous ZSL models mainly depend on manually-labeled attributes or word embeddings extracted from language models to transfer knowledge from seen classes to novel classes. Besides, pioneer ZSL models use convolutional neural networks pre-trained on ImageNet, which focus on the main objects appearing in each image, neglecting the background context that also matters in RS scene classification. To address the above problems, we propose to collect visually detectable attributes automatically. We predict attributes for each class by depicting the semantic-visual similarity between attributes and images. In this way, the attribute annotation process is accomplished by machine instead of human as in other methods. Moreover, we propose a Deep Semantic-Visual Alignment (DSVA) that take advantage of the self-attention mechanism in the transformer to associate local image regions together, integrating the background context information for prediction. The DSVA model further utilizes the attribute attention maps to focus on the informative image regions that are essential for knowledge transfer in ZSL, and maps the visual images into attribute space to perform ZSL classification. With extensive experiments, we show that our model outperforms other state-of-the-art models by a large margin on a challenging large-scale RS scene classification benchmark.

2024-02-06

2402.02110

Guang-Yuan Hao

Guang-Yuan Hao, Hengguan Huang, Haotian Wang, Jie Gao, Hao Wang

Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees

AAAI 2024

cs.LG cs.AI cs.CV cs.NE

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

Active learning (AL) aims to improve model performance within a fixed labeling budget by choosing the most informative data points to label. Existing AL focuses on the single-domain setting, where all data come from the same domain (e.g., the same dataset). However, many real-world tasks often involve multiple domains. For example, in visual recognition, it is often desirable to train an image classifier that works across different environments (e.g., different backgrounds), where images from each environment constitute one domain. Such a multi-domain AL setting is challenging for prior methods because they (1) ignore the similarity among different domains when assigning labeling budget and (2) fail to handle distribution shift of data across different domains. In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain. Our theoretical analysis shows that our method achieves a better error bound compared to current AL methods. Our empirical results demonstrate that our approach significantly outperforms the state-of-the-art AL methods on both synthetic and real-world multi-domain datasets. Code is available at https://github.com/Wang-ML-Lab/multi-domain-active-learning.

2024-02-12

2402.02121

Hossein Bagheri

Ali Mirzaei, Hossein Bagheri, and Iman Khosravi

Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning

ISPRS Int. J. Geo-Inf. 2023, 12(11), 450

10.3390/ijgi12110450

cs.CV cs.LG eess.IV

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

Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing SAR and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is the limited availability of training data, which adversely affects the performance of classifiers. In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. Consequently, when collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Conversely, samples from other crops are scarce, representing the minority classes. Addressing this issue requires overcoming several challenges and weaknesses associated with traditional data generation methods. These methods have been employed to tackle the imbalanced nature of the training data. Nevertheless, they still face limitations in effectively handling the minority classes. Overall, the issue of inadequate training data, particularly for minority classes, remains a hurdle that traditional methods struggle to overcome. In this research, We explore the effectiveness of conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, in addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data. Our findings demonstrate that the proposed method generates synthetic data with higher quality that can significantly increase the number of samples for minority classes leading to better performance of crop classifiers.

2024-02-07

2402.02141

Bo Yang

Bo Yang, Chen Wang, Xiaoshuang Ma, Beiping Song, Zhuang Liu and Fangde Sun

Zero-shot sketch-based remote sensing image retrieval based on multi-level and attention-guided tokenization

44 pages, 6 figures

Remote Sens. 2024, 16, 1653

10.3390/rs16101653

cs.CV

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

Effectively and efficiently retrieving images from remote sensing databases is a critical challenge in the realm of remote sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modality attention update. This approach employs only vision information and does not require semantic knowledge concerning the sketch and image. It starts by employing multi-level self-attention guided feature extraction to tokenize the query sketches, as well as self-attention feature extraction to tokenize the candidate images. It then employs cross-attention mechanisms to establish token correspondence between these two modalities, facilitating the computation of sketch-to-image similarity. Our method significantly outperforms existing sketch-based remote sensing image retrieval techniques, as evidenced by tests on multiple datasets. Notably, it also exhibits robust zero-shot learning capabilities and strong generalizability in handling unseen categories and novel remote sensing data. The method's scalability can be further enhanced by the pre-calculation of retrieval tokens for all candidate images in a database. This research underscores the significant potential of multi-level, attention-guided tokenization in cross-modal remote sensing image retrieval. For broader accessibility and research facilitation, we have made the code and dataset used in this study publicly available online. Code and dataset are available at https://github.com/Snowstormfly/Cross-modal-retrieval-MLAGT.

2024-05-20

2402.02181

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

Jos\'e Alberto Ben\'itez-Andrades, Isa\'ias Garc\'ia-Rodr\'iguez, Carmen Benavides, H\'ector Al\'aiz-Moret\'on and Jos\'e Emilio Labra Gayo

An Ontology-Based multi-domain model in Social Network Analysis: Experimental validation and case study

Information Sciences, Volume 540, November 2020, Pages 390-413

10.1016/j.ins.2020.06.008

cs.SI cs.AI

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

The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically gathering data and carrying out different social network analyses in different domains, without errors and obtaining the same conclusions that an expert in SNA would obtain. The model is represented in an ontology called OntoSNAQA, which is made up of classes, properties and rules representing the domains of People, Questionnaires and Social Network Analysis. Besides the ontology itself, different rules are represented by SWRL and SPARQL queries. A Knowledge Based System was created using OntoSNAQA and applied to a real case study in order to show the advantages of the approach. Finally, the results of an SNA analysis obtained through the model were compared to those obtained from some of the most widely used SNA applications: UCINET, Pajek, Cytoscape and Gephi, to test and confirm the validity of the model.

2024-02-06

2402.02183

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

Mar\'ia Teresa Garc\'ia-Ord\'as, Jos\'e Alberto Ben\'itez-Andrades, Isa\'ias Garc\'ia-Rodr\'iguez, Carmen Benavides and H\'ector Alaiz-Moret\'on

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

Sensors 2020, Volume 20 Issue 4, ID 1214

10.3390/s20041214

cs.CV eess.IV

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

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

2024-02-07

2402.02188

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

Mar\'ia Teresa Garc\'ia-Ord\'as, Carmen Benavides, Jos\'e Alberto Ben\'itez-Andrades, H\'ector Alaiz-Moret\'on and Isa\'ias Garc\'ia-Rodr\'iguez

Diabetes detection using deep learning techniques with oversampling and feature augmentation

Computer Methods and Programs in Biomedicine, Volume 202, April 2021, ID 105968

10.1016/j.cmpb.2021.105968

eess.IV cs.CV cs.LG

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

Background and objective: Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of their health status early enough. Late diagnosis brings about numerous health problems and a large number of deaths each year so the development of methods for the early diagnosis of this pathology is essential. Methods: In this paper, a pipeline based on deep learning techniques is proposed to predict diabetic people. It includes data augmentation using a variational autoencoder (VAE), feature augmentation using an sparse autoencoder (SAE) and a convolutional neural network for classification. Pima Indians Diabetes Database, which takes into account information on the patients such as the number of pregnancies, glucose or insulin level, blood pressure or age, has been evaluated. Results: A 92.31% of accuracy was obtained when CNN classifier is trained jointly the SAE for featuring augmentation over a well balanced dataset. This means an increment of 3.17% of accuracy with respect the state-of-the-art. Conclusions: Using a full deep learning pipeline for data preprocessing and classification has demonstrate to be very promising in the diabetes detection field outperforming the state-of-the-art proposals.

2024-02-07

2402.02209

Orazio Pontorno

Orazio Pontorno (1), Luca Guarnera (1), Sebastiano Battiato (1) ((1) University of Catania)

On the Exploitation of DCT-Traces in the Generative-AI Domain

2024 IEEE International Conference on Image Processing (ICIP)

10.1109/ICIP51287.2024.10648013

cs.CV cs.LG

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

Deepfakes represent one of the toughest challenges in the world of Cybersecurity and Digital Forensics, especially considering the high-quality results obtained with recent generative AI-based solutions. Almost all generative models leave unique traces in synthetic data that, if analyzed and identified in detail, can be exploited to improve the generalization limitations of existing deepfake detectors. In this paper we analyzed deepfake images in the frequency domain generated by both GAN and Diffusion Model engines, examining in detail the underlying statistical distribution of Discrete Cosine Transform (DCT) coefficients. Recognizing that not all coefficients contribute equally to image detection, we hypothesize the existence of a unique ``discriminative fingerprint", embedded in specific combinations of coefficients. To identify them, Machine Learning classifiers were trained on various combinations of coefficients. In addition, the Explainable AI (XAI) LIME algorithm was used to search for intrinsic discriminative combinations of coefficients. Finally, we performed a robustness test to analyze the persistence of traces by applying JPEG compression. The experimental results reveal the existence of traces left by the generative models that are more discriminative and persistent at JPEG attacks. Code and dataset are available at https://github.com/opontorno/dcts_analysis_deepfakes.

2024-10-04

2402.02210

Haochen Chang

Haochen Chang, Jing Chen, Yilin Li, Jixiang Chen, Xiaofeng Zhang

Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition

Accepted by ICASSP 2024

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

10.1109/ICASSP48485.2024.10448199

cs.CV cs.MM

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

Skeleton-based action recognition has attracted much attention, benefiting from its succinctness and robustness. However, the minimal inter-class variation in similar action sequences often leads to confusion. The inherent spatiotemporal coupling characteristics make it challenging to mine the subtle differences in joint motion trajectories, which is critical for distinguishing confusing fine-grained actions. To alleviate this problem, we propose a Wavelet-Attention Decoupling (WAD) module that utilizes discrete wavelet transform to effectively disentangle salient and subtle motion features in the time-frequency domain. Then, the decoupling attention adaptively recalibrates their temporal responses. To further amplify the discrepancies in these subtle motion features, we propose a Fine-grained Contrastive Enhancement (FCE) module to enhance attention towards trajectory features by contrastive learning. Extensive experiments are conducted on the coarse-grained dataset NTU RGB+D and the fine-grained dataset FineGYM. Our methods perform competitively compared to state-of-the-art methods and can discriminate confusing fine-grained actions well.

2024-04-02

2402.02314

Haowei Lin

Haowei Lin, Baizhou Huang, Haotian Ye, Qinyu Chen, Zihao Wang, Sujian Li, Jianzhu Ma, Xiaojun Wan, James Zou, Yitao Liang

Selecting Large Language Model to Fine-tune via Rectified Scaling Law

ICML 2024

cs.LG cs.AI cs.CL

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

The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine-tuning all models and making selections afterward is unrealistic. In this work, we formulate this resource-constrained selection task into predicting fine-tuning performance and illustrate its natural connection with Scaling Law. Unlike pre-training, we find that the fine-tuning scaling curve includes not just the well-known "power phase" but also the previously unobserved "pre-power phase". We also explain why existing Scaling Law fails to capture this phase transition phenomenon both theoretically and empirically. To address this, we introduce the concept of "pre-learned data size" into our Rectified Scaling Law, which overcomes theoretical limitations and fits experimental results much better. By leveraging our law, we propose a novel LLM selection algorithm that selects the near-optimal model with hundreds of times less resource consumption, while other methods may provide negatively correlated selection. The project page is available at rectified-scaling-law.github.io.

2024-05-29

2402.02388

Tong Niu

Tong Niu, Weihao Zhang, Rong Zhao

Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning

International Conference on Autonomous Agents and Multiagent Systems 2024

cs.CL cs.AI cs.LG cs.SE

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

Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies aimed at addressing challenges posed by complex systems and achieving various objectives. This process demands labor-intensive endeavors and multidisciplinary expertise. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. However, LLMs excel in handling sequential information, making it challenging for analyzing the intricate interactions and nonlinear dynamics inherent in ABMs. Additionally, due to the lack of self-evaluation capability of LLMs, relying solely on LLMs is insufficient to effectively accomplish this process. In this paper, we present SAGE, a general solution-oriented ABM generation framework designed for automatic modeling and generating solutions for targeted problems. Unlike approaches reliant on expert handcrafting or resource-intensive neural network training, SAGE establishes a verifier-assisted iterative in-context learning process employing large language models (LLMs) to leverages their inherent cross-domain knowledge for tackling intricate demands from diverse domain scenarios. In SAGE, we introduce an semi-structured conceptual representation expliciting the intricate structures of ABMs and an objective representation to guide LLMs in modeling scenarios and proposing hypothetical solutions through in-context learning. To ensure the model executability and solution feasibility, SAGE devises a two-level verifier with chain-of-thought prompting tailored to the complex interactions and non-linear dynamics of ABMs, driving the iterative generation optimization. Moreover, we construct an evaluation dataset of solution-oriented ABMs from open sources.It contains practical models across various domains.

2024-04-02

2402.02397

Aydogan Ozcan

Guangdong Ma, Xilin Yang, Bijie Bai, Jingxi Li, Yuhang Li, Tianyi Gan, Che-Yung Shen, Yijie Zhang, Yuzhu Li, Mona Jarrahi, Aydogan Ozcan

Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network

37 Pages, 10 Figures

Laser & Photonics Reviews (2024)

10.1002/lpor.202400238

physics.optics cs.CV cs.NE

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

Large-scale and high-dimensional permutation operations are important for various applications in e.g., telecommunications and encryption. Here, we demonstrate the use of all-optical diffractive computing to execute a set of high-dimensional permutation operations between an input and output field-of-view through layer rotations in a diffractive optical network. In this reconfigurable multiplexed material designed by deep learning, every diffractive layer has four orientations: 0, 90, 180, and 270 degrees. Each unique combination of these rotatable layers represents a distinct rotation state of the diffractive design tailored for a specific permutation operation. Therefore, a K-layer rotatable diffractive material is capable of all-optically performing up to 4^K independent permutation operations. The original input information can be decrypted by applying the specific inverse permutation matrix to output patterns, while applying other inverse operations will lead to loss of information. We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers. We also experimentally validated this reconfigurable diffractive network using terahertz radiation and 3D-printed diffractive layers, providing a decent match to our numerical results. The presented rotation-multiplexed diffractive processor design is particularly useful due to its mechanical reconfigurability, offering multifunctional representation through a single fabrication process.

2024-07-08

2402.02449

Francisco J. Ribadas-Pena

Manuel Vilares Ferro, V\'ictor M. Darriba Bilbao, Francisco J. Ribadas-Pena, Jorge Gra\~na Gil

Surfing the modeling of PoS taggers in low-resource scenarios

17 papes, 5 figures

Mathematics 2022, 10(19), 3526

10.3390/math10193526

cs.CL cs.LG

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

The recent trend towards the application of deep structured techniques has revealed the limits of huge models in natural language processing. This has reawakened the interest in traditional machine learning algorithms, which have proved still to be competitive in certain contexts, in particular low-resource settings. In parallel, model selection has become an essential task to boost performance at reasonable cost, even more so when we talk about processes involving domains where the training and/or computational resources are scarce. Against this backdrop, we evaluate the early estimation of learning curves as a practical mechanism for selecting the most appropriate model in scenarios characterized by the use of non-deep learners in resource-lean settings. On the basis of a formal approximation model previously evaluated under conditions of wide availability of training and validation resources, we study the reliability of such an approach in a different and much more demanding operationalenvironment. Using as case study the generation of PoS taggers for Galician, a language belonging to the Western Ibero-Romance group, the experimental results are consistent with our expectations.

2024-02-06

2402.02513

V\'ictor Manuel Darriba Bilbao

Manuel Vilares Ferro, Yerai Doval Mosquera, Francisco J. Ribadas Pena, Victor M. Darriba Bilbao

Early stopping by correlating online indicators in neural networks

26 pages, 6 figures

Neural Networks, 159 (2023), pp 109-124. ISSN 1879-2782. Elsevier

10.1016/j.jcss.2022.05.002

cs.LG cs.AI cs.CL cs.NE

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

In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus improving the predictive power of that type of modeling. Our proposal exploits the correlation over time in a collection of online indicators, namely characteristic functions for indicating if a set of hypotheses are met, associated with a range of independent stopping conditions built from a canary judgment to evaluate the presence of overfitting. That way, we provide a formal basis for decision making in terms of interrupting the learning process. As opposed to previous approaches focused on a single criterion, we take advantage of subsidiarities between independent assessments, thus seeking both a wider operating range and greater diagnostic reliability. With a view to illustrating the effectiveness of the halting condition described, we choose to work in the sphere of natural language processing, an operational continuum increasingly based on machine learning. As a case study, we focus on parser generation, one of the most demanding and complex tasks in the domain. The selection of cross-validation as a canary function enables an actual comparison with the most representative early stopping conditions based on overfitting identification, pointing to a promising start toward an optimal bias and variance control.

2024-02-06

2402.02515

V\'ictor Manuel Darriba Bilbao

Manuel Vilares Ferro, Victor M. Darriba Bilbao, Francisco J. Ribadas Pena

Modeling of learning curves with applications to pos tagging

30 pages, 11 figures

Computer Speech & Language, 41, pp 1-28 (2017). ISSN 0885-2308. Elsevier

10.1016/j.csl.2016.06.001

cs.CL cs.AI cs.LG

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

An algorithm to estimate the evolution of learning curves on the whole of a training data base, based on the results obtained from a portion and using a functional strategy, is introduced. We approximate iteratively the sought value at the desired time, independently of the learning technique used and once a point in the process, called prediction level, has been passed. The proposal proves to be formally correct with respect to our working hypotheses and includes a reliable proximity condition. This allows the user to fix a convergence threshold with respect to the accuracy finally achievable, which extends the concept of stopping criterion and seems to be effective even in the presence of distorting observations. Our aim is to evaluate the training effort, supporting decision making in order to reduce the need for both human and computational resources during the learning process. The proposal is of interest in at least three operational procedures. The first is the anticipation of accuracy gain, with the purpose of measuring how much work is needed to achieve a certain degree of performance. The second relates the comparison of efficiency between systems at training time, with the objective of completing this task only for the one that best suits our requirements. The prediction of accuracy is also a valuable item of information for customizing systems, since we can estimate in advance the impact of settings on both the performance and the development costs. Using the generation of part-of-speech taggers as an example application, the experimental results are consistent with our expectations.

2024-02-06

2402.02516

V\'ictor Manuel Darriba Bilbao

Manuel Vilares Ferro, Victor M. Darriba Bilbao, Jes\'us Vilares Ferro

Adaptive scheduling for adaptive sampling in POS taggers construction

23 pager, 10 figures

Computer Speech & Language, 60, 101020 (2020), pp 1-18. ISSN 0885-2308. Elsevier

10.1016/j.csl.2019.101020

cs.CL cs.AI cs.LG

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

We introduce an adaptive scheduling for adaptive sampling as a novel way of machine learning in the construction of part-of-speech taggers. The goal is to speed up the training on large data sets, without significant loss of performance with regard to an optimal configuration. In contrast to previous methods using a random, fixed or regularly rising spacing between the instances, ours analyzes the shape of the learning curve geometrically in conjunction with a functional model to increase or decrease it at any time. The algorithm proves to be formally correct regarding our working hypotheses. Namely, given a case, the following one is the nearest ensuring a net gain of learning ability from the former, it being possible to modulate the level of requirement for this condition. We also improve the robustness of sampling by paying greater attention to those regions of the training data base subject to a temporary inflation in performance, thus preventing the learning from stopping prematurely. The proposal has been evaluated on the basis of its reliability to identify the convergence of models, corroborating our expectations. While a concrete halting condition is used for testing, users can choose any condition whatsoever to suit their own specific needs.

2024-02-06

2402.02522

V\'ictor Manuel Darriba Bilbao

Manuel Vilares Ferro, Victor M. Darriba Bilbao, Jes\'us Vilares Ferro

Absolute convergence and error thresholds in non-active adaptive sampling

27 pages, 10 figures

Journal of Computer and System Sciences, 129 (2020) , pp 39-61. ISSN 1090-2724. Elsevier

10.1016/j.jcss.2022.05.002

cs.CL cs.AI cs.LG

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

Non-active adaptive sampling is a way of building machine learning models from a training data base which are supposed to dynamically and automatically derive guaranteed sample size. In this context and regardless of the strategy used in both scheduling and generating of weak predictors, a proposal for calculating absolute convergence and error thresholds is described. We not only make it possible to establish when the quality of the model no longer increases, but also supplies a proximity condition to estimate in absolute terms how close it is to achieving such a goal, thus supporting decision making for fine-tuning learning parameters in model selection. The technique proves its correctness and completeness with respect to our working hypotheses, in addition to strengthening the robustness of the sampling scheme. Tests meet our expectations and illustrate the proposal in the domain of natural language processing, taking the generation of part-of-speech taggers as case study.

2024-02-06

2402.02574

Guanxiong Sun

Guanxiong Sun, Chi Wang, Zhaoyu Zhang, Jiankang Deng, Stefanos Zafeiriou, Yang Hua

Spatio-temporal Prompting Network for Robust Video Feature Extraction

2023 International Conference on Computer Vision (ICCV) 13541-13551

10.1109/ICCV51070.2023.01250

cs.CV cs.LG

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

Frame quality deterioration is one of the main challenges in the field of video understanding. To compensate for the information loss caused by deteriorated frames, recent approaches exploit transformer-based integration modules to obtain spatio-temporal information. However, these integration modules are heavy and complex. Furthermore, each integration module is specifically tailored for its target task, making it difficult to generalise to multiple tasks. In this paper, we present a neat and unified framework, called Spatio-Temporal Prompting Network (STPN). It can efficiently extract robust and accurate video features by dynamically adjusting the input features in the backbone network. Specifically, STPN predicts several video prompts containing spatio-temporal information of neighbour frames. Then, these video prompts are prepended to the patch embeddings of the current frame as the updated input for video feature extraction. Moreover, STPN is easy to generalise to various video tasks because it does not contain task-specific modules. Without bells and whistles, STPN achieves state-of-the-art performance on three widely-used datasets for different video understanding tasks, i.e., ImageNetVID for video object detection, YouTubeVIS for video instance segmentation, and GOT-10k for visual object tracking. Code is available at https://github.com/guanxiongsun/vfe.pytorch.

2024-02-07

2402.02591

Jes\'us Vilares

Yerai Doval, Manuel Vilares, Jes\'us Vilares

On the performance of phonetic algorithms in microtext normalization

Accepted for publication in journal Expert Systems with Applications

Expert Systems with Applications, Volume 113, 2018, Pages 213-222

10.1016/j.eswa.2018.07.016

cs.CL

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

User-generated content published on microblogging social networks constitutes a priceless source of information. However, microtexts usually deviate from the standard lexical and grammatical rules of the language, thus making its processing by traditional intelligent systems very difficult. As an answer, microtext normalization consists in transforming those non-standard microtexts into standard well-written texts as a preprocessing step, allowing traditional approaches to continue with their usual processing. Given the importance of phonetic phenomena in non-standard text formation, an essential element of the knowledge base of a normalizer would be the phonetic rules that encode these phenomena, which can be found in the so-called phonetic algorithms. In this work we experiment with a wide range of phonetic algorithms for the English language. The aim of this study is to determine the best phonetic algorithms within the context of candidate generation for microtext normalization. In other words, we intend to find those algorithms that taking as input non-standard terms to be normalized allow us to obtain as output the smallest possible sets of normalization candidates which still contain the corresponding target standard words. As it will be stated, the choice of the phonetic algorithm will depend heavily on the capabilities of the candidate selection mechanism which we usually find at the end of a microtext normalization pipeline. The faster it can make the right choices among big enough sets of candidates, the more we can sacrifice on the precision of the phonetic algorithms in favour of coverage in order to increase the overall performance of the normalization system. KEYWORDS: microtext normalization; phonetic algorithm; fuzzy matching; Twitter; texting

2024-02-06

2402.02639

Ben Hutchinson

Ned Cooper, Courtney Heldreth, Ben Hutchinson

"It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologies

Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024)

cs.CL

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

Indigenous languages are historically under-served by Natural Language Processing (NLP) technologies, but this is changing for some languages with the recent scaling of large multilingual models and an increased focus by the NLP community on endangered languages. This position paper explores ethical considerations in building NLP technologies for Indigenous languages, based on the premise that such projects should primarily serve Indigenous communities. We report on interviews with 17 researchers working in or with Aboriginal and/or Torres Strait Islander communities on language technology projects in Australia. Drawing on insights from the interviews, we recommend practices for NLP researchers to increase attention to the process of engagements with Indigenous communities, rather than focusing only on decontextualised artefacts.

2024-02-07

2402.02768

Salwa Mostafa

Salwa Mostafa, Mohammed S. Elbamby, Mohamed K. Abdel-Aziz, and Mehdi Bennis

Intent Profiling and Translation Through Emergent Communication

IEEE International Conference on Communications (ICC2024)

cs.NI cs.AI cs.LG

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

To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline.

2024-02-08

2402.02837

Amandine Decker

Amandine Decker (LORIA, UL, CNRS, SEMAGRAMME, GU), Maxime Amblard (SEMAGRAMME, LORIA)

With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations

CODI 2024 - 5th workshop on Computational Approaches to Discourse, Mar 2024, Malta, Malta

cs.CL

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

Topics play an important role in the global organisation of a conversation as what is currently discussed constrains the possible contributions of the participant. Understanding the way topics are organised in interaction would provide insight on the structure of dialogue beyond the sequence of utterances. However, studying this high-level structure is a complex task that we try to approach by first segmenting dialogues into smaller topically coherent sets of utterances. Understanding the interactions between these segments would then enable us to propose a model of topic organisation at a dialogue level. In this paper we work with open-domain conversations and try to reach a comparable level of accuracy as recent machine learning based topic segmentation models but with a formal approach. The features we identify as meaningful for this task help us understand better the topical structure of a conversation.

2024-02-06

2402.02936

Farhad Pakdaman

Li Yu, Yanjun Gao, Farhad Pakdaman, Moncef Gabbouj

Panoramic Image Inpainting With Gated Convolution And Contextual Reconstruction Loss

Copyright 2024 IEEE - to appear in IEEE ICASSP 2024

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

10.1109/ICASSP48485.2024.10446469

eess.IV cs.CV cs.LG cs.MM

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

Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish valid pixels from invalid pixels and find suitable references for corrupted areas, thus leading to artifacts in the inpainted results. In response to these challenges, we propose a panoramic image inpainting framework that consists of a Face Generator, a Cube Generator, a side branch, and two discriminators. We use the Cubemap Projection (CMP) format as network input. The generator employs gated convolutions to distinguish valid pixels from invalid ones, while a side branch is designed utilizing contextual reconstruction (CR) loss to guide the generators to find the most suitable reference patch for inpainting the missing region. The proposed method is compared with state-of-the-art (SOTA) methods on SUN360 Street View dataset in terms of PSNR and SSIM. Experimental results and ablation study demonstrate that the proposed method outperforms SOTA both quantitatively and qualitatively.

2024-03-20

2402.03067

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

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

Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian

Trajanovic, M., Filipovic, N., Zdravkovic, M. (eds) Disruptive Information Technologies for a Smart Society. ICIST 2023. Lecture Notes in Networks and Systems, vol 872. Springer, Cham

10.1007/978-3-031-50755-7_16

cs.CL cs.AI

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

This paper presents the results of the first application of BERTopic, a state-of-the-art topic modeling technique, to short text written in a morphologi-cally rich language. We applied BERTopic with three multilingual embed-ding models on two levels of text preprocessing (partial and full) to evalu-ate its performance on partially preprocessed short text in Serbian. We also compared it to LDA and NMF on fully preprocessed text. The experiments were conducted on a dataset of tweets expressing hesitancy toward COVID-19 vaccination. Our results show that with adequate parameter setting, BERTopic can yield informative topics even when applied to partially pre-processed short text. When the same parameters are applied in both prepro-cessing scenarios, the performance drop on partially preprocessed text is minimal. Compared to LDA and NMF, judging by the keywords, BERTopic offers more informative topics and gives novel insights when the number of topics is not limited. The findings of this paper can be significant for re-searchers working with other morphologically rich low-resource languages and short text.

2024-02-06

2402.03166

Jos\'e Morano

Jos\'e Morano and Guilherme Aresta and Hrvoje Bogunovi\'c

RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification

Expert Systems with Applications, 2024

10.1016/j.eswa.2024.124970

eess.IV cs.CV

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

The caliber and configuration of retinal blood vessels serve as important biomarkers for various diseases and medical conditions. A thorough analysis of the retinal vasculature requires the segmentation of the blood vessels and their classification into arteries and veins, typically performed on color fundus images obtained by retinography. However, manually performing these tasks is labor-intensive and prone to human error. While several automated methods have been proposed to address this task, the current state of art faces challenges due to manifest classification errors affecting the topological consistency of segmentation maps. In this work, we introduce RRWNet, a novel end-to-end deep learning framework that addresses this limitation. The framework consists of a fully convolutional neural network that recursively refines semantic segmentation maps, correcting manifest classification errors and thus improving topological consistency. In particular, RRWNet is composed of two specialized subnetworks: a Base subnetwork that generates base segmentation maps from the input images, and a Recursive Refinement subnetwork that iteratively and recursively improves these maps. Evaluation on three different public datasets demonstrates the state-of-the-art performance of the proposed method, yielding more topologically consistent segmentation maps with fewer manifest classification errors than existing approaches. In addition, the Recursive Refinement module within RRWNet proves effective in post-processing segmentation maps from other methods, further demonstrating its potential. The model code, weights, and predictions will be publicly available at https://github.com/j-morano/rrwnet.

2024-08-09

2402.03176

Bayode Ogunleye

Bayode Ogunleye, Tonderai Maswera, Laurence Hirsch, Jotham Gaudoin, and Teresa Brunsdon

Comparison of Topic Modelling Approaches in the Banking Context

14 pages, Journal of Applied Science

Applied Sciences (2023), 13(2), 797

10.3390/app13020797

cs.IR cs.AI cs.LG stat.CO

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

Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discovery has shown great performances, however, they are not consistent in their results as these approaches suffer from data sparseness and inability to model the word order in a document. Thus, this study presents the use of Kernel Principal Component Analysis (KernelPCA) and K-means Clustering in the BERTopic architecture. We have prepared a new dataset using tweets from customers of Nigerian banks and we use this to compare the topic modelling approaches. Our findings showed KernelPCA and K-means in the BERTopic architecture-produced coherent topics with a coherence score of 0.8463.

2024-02-06

2402.03246

Shuhong Liu

Mingrui Li, Shuhong Liu, Heng Zhou, Guohao Zhu, Na Cheng, Tianchen Deng, Hongyu Wang

SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM

European Conference on Computer Vision (ECCV) 2024

cs.CV cs.AI cs.RO

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

We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. It incorporates appearance, geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems in high-quality rendering, scene understanding, and object-level geometry. We introduce a unique semantic feature loss that effectively compensates for the shortcomings of traditional depth and color losses in object optimization. Through a semantic-guided keyframe selection strategy, we prevent erroneous reconstructions caused by cumulative errors. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, precise semantic segmentation, and object-level geometric accuracy, while ensuring real-time rendering capabilities.

2024-07-08

2402.03337

Luis Marti

Eduardo Charles Vasconcellos (UFF), Ronald M Sampaio, Andr\'e P D Ara\'ujo (UFF), Esteban Walter Gonzales Clua, Philippe Preux (SEQUEL, GRAppA - LIFL), Raphael Guerra, Luiz M G Gon\c{c}alves (UFRN), Luis Mart\'i, Hernan Lira, Nayat Sanchez-Pi

Reinforcement-learning robotic sailboats: simulator and preliminary results

NeurIPS 2023 Workshop on Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models, Dec 2023, New Orelans, United States

cs.RO cs.AI cs.LG

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

This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins. We introduce the key features for building virtual worlds, considering using Reinforcement Learning (RL) agents for autonomous navigation and control. With this in mind, the main problems concern the definition of the simulation equations (physics and mathematics), their effective implementation, and how to include strategies for simulated control and perception (sensors) to be used with RL. We present the modeling, implementation steps, and challenges required to create a functional digital twin based on a real robotic sailing vessel. The application is immediate for developing navigation algorithms based on RL to be applied on real boats.

2024-02-07

2402.03369

Majbah Uddin

Majbah Uddin, Nathan Huynh, Jose M Vidal, Kevin M Taaffe, Lawrence D Fredendall, and Joel S Greenstein

Evaluation of Google's Voice Recognition and Sentence Classification for Health Care Applications

Engineering Management Journal, 27:3, 152-162, 2015

10.1080/10429247.2015.1054752

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

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

This study examined the use of voice recognition technology in perioperative services (Periop) to enable Periop staff to record workflow milestones using mobile technology. The use of mobile technology to improve patient flow and quality of care could be facilitated if such voice recognition technology could be made robust. The goal of this experiment was to allow the Periop staff to provide care without being interrupted with data entry and querying tasks. However, the results are generalizable to other situations where an engineering manager attempts to improve communication performance using mobile technology. This study enhanced Google's voice recognition capability by using post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy). The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual's voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that engineering managers could significantly enhance Google's voice recognition technology by using post-processing techniques, which would facilitate its use in health care and other applications.

2024-02-07

2402.03370

Cyril Labbe

El\'ena Martel (SIGMA, LIG), Martin Lentschat (SIGMA, GETALP), Cyril Labb\'e (LIG, SIGMA )

Detection of tortured phrases in scientific literature

Proceedings of the 2nd Workshop on Information Extraction from Scientific Publications, Nov 2023, Bali, Indonesia

cs.IR cs.AI cs.CL cs.DL

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

This paper presents various automatic detection methods to extract so called tortured phrases from scientific papers. These tortured phrases, e.g. flag to clamor instead of signal to noise, are the results of paraphrasing tools used to escape plagiarism detection. We built a dataset and evaluated several strategies to flag previously undocumented tortured phrases. The proposed and tested methods are based on language models and either on embeddings similarities or on predictions of masked token. We found that an approach using token prediction and that propagates the scores to the chunk level gives the best results. With a recall value of .87 and a precision value of .61, it could retrieve new tortured phrases to be submitted to domain experts for validation.

2024-02-07

2402.03384

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

Santiago Valbuena Rubio, Mar\'ia Teresa Garc\'ia-Ord\'as, Oscar Garc\'ia-Olalla Olivera, H\'ector Alaiz-Moret\'on, Maria-Inmaculada Gonz\'alez-Alonso and Jos\'e Alberto Ben\'itez-Andrades

Survival and grade of the glioma prediction using transfer learning

PeerJ Computer Science, Volume 9, December 2023, ID e1723

10.7717/peerj-cs.1723

cs.CV cs.AI cs.LG

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

Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3 to 6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.

2024-02-07

2402.03386

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

\'Angel Delgado-Panadero, Jos\'e Alberto Ben\'itez-Andrades and Mar\'ia Teresa Garc\'ia-Ord\'as

A generalized decision tree ensemble based on the NeuralNetworks architecture: Distributed Gradient Boosting Forest (DGBF)

Applied Intelligence, Volume 53, July 2023, pages 22991-23003

10.1007/s10489-023-04735-w

cs.LG cs.AI

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

Tree ensemble algorithms as RandomForest and GradientBoosting are currently the dominant methods for modeling discrete or tabular data, however, they are unable to perform a hierarchical representation learning from raw data as NeuralNetworks does thanks to its multi-layered structure, which is a key feature for DeepLearning problems and modeling unstructured data. This limitation is due to the fact that tree algorithms can not be trained with back-propagation because of their mathematical nature. However, in this work, we demonstrate that the mathematical formulation of bagging and boosting can be combined together to define a graph-structured-tree-ensemble algorithm with a distributed representation learning process between trees naturally (without using back-propagation). We call this novel approach Distributed Gradient Boosting Forest (DGBF) and we demonstrate that both RandomForest and GradientBoosting can be expressed as particular graph architectures of DGBT. Finally, we see that the distributed learning outperforms both RandomForest and GradientBoosting in 7 out of 9 datasets.

2024-02-07

2402.03473

Xiaodan Xing

Xiaodan Xing, Huiyu Zhou, Yingying Fang, and Guang Yang

Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images

5 pages

ISBI 2024

eess.IV cs.CV

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

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as DALLE and Imagen, have integrated digital watermarks aimed at facilitating the discernment of synthetic images' authenticity. These watermarks are embedded within the image pixels and are invisible to the human eye while remains their detectability. Nevertheless, a comprehensive investigation into the potential impact of these invisible watermarks on the utility of synthetic medical images has been lacking. In this study, we propose the incorporation of invisible watermarks into synthetic medical images and seek to evaluate their efficacy in the context of downstream classification tasks. Our goal is to pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.

2024-05-22

2402.03654

Ricardo De Deijn

Ricardo de Deijn, Aishwarya Batra, Brandon Koch, Naseef Mansoor, Hema Makkena

Reviewing FID and SID Metrics on Generative Adversarial Networks

14 pages 9 figures 1 table Included in IOTBS, NLTM, AIMLA, DBDM - 2024 Conference Proceedings Editor: David C. Wyld et al

CS & IT - CSCP (2024) 111-124

10.5121/csit.2024.140208

cs.CV eess.IV

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

The growth of generative adversarial network (GAN) models has increased the ability of image processing and provides numerous industries with the technology to produce realistic image transformations. However, with the field being recently established there are new evaluation metrics that can further this research. Previous research has shown the Fr\'echet Inception Distance (FID) to be an effective metric when testing these image-to-image GANs in real-world applications. Signed Inception Distance (SID), a founded metric in 2023, expands on FID by allowing unsigned distances. This paper uses public datasets that consist of fa\c{c}ades, cityscapes, and maps within Pix2Pix and CycleGAN models. After training these models are evaluated on both inception distance metrics which measure the generating performance of the trained models. Our findings indicate that usage of the metric SID incorporates an efficient and effective metric to complement, or even exceed the ability shown using the FID for the image-to-image GANs

2024-02-07

2402.03728

Hossein Rajaby Faghihi

Hossein Rajaby Faghihi and Parisa Kordjamshidi

Consistent Joint Decision-Making with Heterogeneous Learning Models

EACL 2024 Findings - Short Paper

EACL 2024

cs.AI cs.CL cs.LG cs.LO

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

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.

2024-02-07

2402.03732

Feng Xia

Huiling Tu, Shuo Yu, Vidya Saikrishna, Feng Xia, Karin Verspoor

Deep Outdated Fact Detection in Knowledge Graphs

10 pages, 6 figures

2023 IEEE International Conference on Data Mining Workshops (ICDMW), December 1-4, 2023, Shanghai, China

10.1109/ICDMW60847.2023.00184

cs.AI cs.CL cs.DL cs.LG

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

Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs. DEAN distinguishes itself by capturing implicit structural information among facts through comprehensive modeling of both entities and relations. To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities. Experimental results demonstrate the effectiveness and superiority of DEAN over state-of-the-art baseline methods.

2024-02-07

2402.03750

Feng Xia

Xin Chen, Mingliang Hou, Tao Tang, Achhardeep Kaur and Feng Xia

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

10 pages, 7 figures

The 7th IEEE International Conference on Data Science and Systems (DSS), Dec 20 - 22, 2021, Haikou, China

10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00182

cs.LG cs.AI cs.HC

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

With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP.

2024-02-07

2402.03758

Mingyue Guo

Mingyue Guo, Binghui Chen, Zhaoyi Yan, Yaowei Wang, Qixiang Ye

Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting

Multidomain learning; Domain-guided virtual classifier; Instance-specific batch normalization

IEEE Transactions on Neural Networks and Learning Systems,2024

10.1109/TNNLS.2024.3350363

cs.CV

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

Multidomain crowd counting aims to learn a general model for multiple diverse datasets. However, deep networks prefer modeling distributions of the dominant domains instead of all domains, which is known as domain bias. In this study, we propose a simple-yet-effective Modulating Domain-specific Knowledge Network (MDKNet) to handle the domain bias issue in multidomain crowd counting. MDKNet is achieved by employing the idea of `modulating', enabling deep network balancing and modeling different distributions of diverse datasets with little bias. Specifically, we propose an Instance-specific Batch Normalization (IsBN) module, which serves as a base modulator to refine the information flow to be adaptive to domain distributions. To precisely modulating the domain-specific information, the Domain-guided Virtual Classifier (DVC) is then introduced to learn a domain-separable latent space. This space is employed as an input guidance for the IsBN modulator, such that the mixture distributions of multiple datasets can be well treated. Extensive experiments performed on popular benchmarks, including Shanghai-tech A/B, QNRF and NWPU, validate the superiority of MDKNet in tackling multidomain crowd counting and the effectiveness for multidomain learning. Code is available at \url{https://github.com/csguomy/MDKNet}.

2024-02-07

2402.03824

Giuseppe Paolo Dr

Giuseppe Paolo, Jonas Gonzalez-Billandon, Bal\'azs K\'egl

A call for embodied AI

Published in ICML 2024 Position paper track

PMLR 235:39493-39508, 2024

cs.AI

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

We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself from the classical paradigm of static learning. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future Embodied AI research. Highlighting the importance of creating Embodied AI agents capable of seamless communication, collaboration, and coexistence with humans and other intelligent entities within real-world environments, we aim to steer the AI community towards addressing the multifaceted challenges and seizing the opportunities that lie ahead in the quest for AGI.

2024-09-16

2402.03948

V\'ictor M. S\'anchez-Cartagena

Juan Ram\'on Rico-Juan, V\'ictor M. S\'anchez-Cartagena, Jose J. Valero-Mas, Antonio Javier Gallego

Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence

IEEE Transactions on Learning Technologies ( Volume: 16, Issue: 6, December 2023)

10.1109/TLT.2023.3239110

cs.CY cs.AI

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

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes -- particularly, multi-instance learning (MIL) and classical machine learning formulations -- to model student behavior. Besides, explainable artificial intelligence (XAI) is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2500 submissions from roughly 90 different students from a programming-related course in a computer science degree. The results obtained validate the proposal: The model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioral pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.

2024-02-07

2402.03989

Anton Backhaus

Anton Backhaus, Thorsten Luettel, Hans-Joachim Wuensche

YOLOPoint Joint Keypoint and Object Detection

12 pages, 5 figures

Proceedings of Advanced Concepts for Intelligent Vision Systems, 14124, 112-123 (2023)

cs.CV

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

Intelligent vehicles of the future must be capable of understanding and navigating safely through their surroundings. Camera-based vehicle systems can use keypoints as well as objects as low- and high-level landmarks for GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a convolutional neural network model that simultaneously detects keypoints and objects in an image by combining YOLOv5 and SuperPoint to create a single forward-pass network that is both real-time capable and accurate. By using a shared backbone and a light-weight network structure, YOLOPoint is able to perform competitively on both the HPatches and KITTI benchmarks.

2024-02-07

2402.04082

Bayode Ogunleye

Hemlata Sharma, Hitesh Harsora, Bayode Ogunleye

An Optimal House Price Prediction Algorithm: XGBoost

16 pages, Journal of Analytics

Analytics, 3(1), 30-45 (2024)

10.3390/analytics3010003

cs.LG cs.AI stat.AP stat.ME

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

An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, multilayer perceptron and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction.

2024-02-07

2402.04088

Bayode Ogunleye

Bayode Ogunleye, Babitha Dharmaraj

The Use of a Large Language Model for Cyberbullying Detection

14 pages, Journal of Analytics

Analytics 2 (2023), no. 3: 694-707

10.3390/analytics2030038

cs.CL cs.AI cs.LG stat.AP

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

The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in todays cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance and generalisation issues. In recent years, large language models (LLMs) like BERT and RoBERTa have achieved state-of-the-art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively for CB detection. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed that RoBERTa outperformed other models.

2024-02-07

2402.04103

Bayode Ogunleye

Jeen Mary John, Olamilekan Shobayo, Bayode Ogunleye

An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market

15 pages, Journal of Analytics

Analytics, 2(4), 809-823 (2023)

10.3390/analytics2040042

cs.LG cs.AI stat.AP stat.CO

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

Recently, peoples awareness of online purchases has significantly risen. This has given rise to online retail platforms and the need for a better understanding of customer purchasing behaviour. Retail companies are pressed with the need to deal with a high volume of customer purchases, which requires sophisticated approaches to perform more accurate and efficient customer segmentation. Customer segmentation is a marketing analytical tool that aids customer-centric service and thus enhances profitability. In this paper, we aim to develop a customer segmentation model to improve decision-making processes in the retail market industry. To achieve this, we employed a UK-based online retail dataset obtained from the UCI machine learning repository. The retail dataset consists of 541,909 customer records and eight features. Our study adopted the RFM (recency, frequency, and monetary) framework to quantify customer values. Thereafter, we compared several state-of-the-art (SOTA) clustering algorithms, namely, K-means clustering, the Gaussian mixture model (GMM), density-based spatial clustering of applications with noise (DBSCAN), agglomerative clustering, and balanced iterative reducing and clustering using hierarchies (BIRCH). The results showed the GMM outperformed other approaches, with a Silhouette Score of 0.80.

2024-02-07

2402.04465

Jos\'e Miguel Buenaposada

Antonio Fern\'andez-Baldera, Jos\'e M. Buenaposada, Luis Baumela

BAdaCost: Multi-class Boosting with Costs

Pattern Recognition. Volume 79, July 2018, Pages 467-479

10.1016/j.patcog.2018.02.022

cs.CV

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

We present BAdaCost, a multi-class cost-sensitive classification algorithm. It combines a set of cost-sensitive multi-class weak learners to obtain a strong classification rule within the Boosting framework. To derive the algorithm we introduce CMEL, a Cost-sensitive Multi-class Exponential Loss that generalizes the losses optimized in various classification algorithms such as AdaBoost, SAMME, Cost-sensitive AdaBoost and PIBoost. Hence unifying them under a common theoretical framework. In the experiments performed we prove that BAdaCost achieves significant gains in performance when compared to previous multi-class cost-sensitive approaches. The advantages of the proposed algorithm in asymmetric multi-class classification are also evaluated in practical multi-view face and car detection problems.

2024-02-08

2402.04482

Jos\'e Miguel Buenaposada

Iago Su\'arez, Ghesn Sfeir, Jos\'e M. Buenaposada, Luis Baumela

BEBLID: Boosted efficient binary local image descriptor

Pattern Recognition Letters. Volume 133, May 2020, Pages 366-372

10.1016/j.patrec.2020.04.005

cs.CV

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

Efficient matching of local image features is a fundamental task in many computer vision applications. However, the real-time performance of top matching algorithms is compromised in computationally limited devices, such as mobile phones or drones, due to the simplicity of their hardware and their finite energy supply. In this paper we introduce BEBLID, an efficient learned binary image descriptor. It improves our previous real-valued descriptor, BELID, making it both more efficient for matching and more accurate. To this end we use AdaBoost with an improved weak-learner training scheme that produces better local descriptions. Further, we binarize our descriptor by forcing all weak-learners to have the same weight in the strong learner combination and train it in an unbalanced data set to address the asymmetries arising in matching and retrieval tasks. In our experiments BEBLID achieves an accuracy close to SIFT and better computational efficiency than ORB, the fastest algorithm in the literature.

2024-02-08

2402.04505

EPTCS

Kin Ian Lo, Mehrnoosh Sadrzadeh, Shane Mansfield

Developments in Sheaf-Theoretic Models of Natural Language Ambiguities

In Proceedings DCM 2023, arXiv:2409.19298

EPTCS 408, 2024, pp. 62-72

10.4204/EPTCS.408.4

cs.CL quant-ph

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

Sheaves are mathematical objects consisting of a base which constitutes a topological space and the data associated with each open set thereof, e.g. continuous functions defined on the open sets. Sheaves have originally been used in algebraic topology and logic. Recently, they have also modelled events such as physical experiments and natural language disambiguation processes. We extend the latter models from lexical ambiguities to discourse ambiguities arising from anaphora. To begin, we calculated a new measure of contextuality for a dataset of basic anaphoric discourses, resulting in a higher proportion of contextual models-82.9%-compared to previous work which only yielded 3.17% contextual models. Then, we show how an extension of the natural language processing challenge, known as the Winograd Schema, which involves anaphoric ambiguities can be modelled on the Bell-CHSH scenario with a contextual fraction of 0.096.

2024-10-02

2402.04519

Shiyu Hu

Xin Zhao and Shiyu Hu and Yipei Wang and Jing Zhang and Yimin Hu and Rongshuai Liu and Haibin Ling and Yin Li and Renshu Li and Kun Liu and Jiadong Li

BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision

This paper is published in IJCV (refer to DOI). Please cite the published IJCV

Int J Comput Vis (2023)

10.1007/s11263-023-01937-0

cs.CV

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

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.

2024-02-08

2402.04539

Guojian Wang

Guojian Wang, Faguo Wu, Xiao Zhang, Jianxiang Liu

Learning Diverse Policies with Soft Self-Generated Guidance

23 pages, 19 figures

International Journal of Intelligent Systems, Volume 2023

10.1155/2023/4705291

cs.LG cs.AI

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

Reinforcement learning (RL) with sparse and deceptive rewards is challenging because non-zero rewards are rarely obtained. Hence, the gradient calculated by the agent can be stochastic and without valid information. Recent studies that utilize memory buffers of previous experiences can lead to a more efficient learning process. However, existing methods often require these experiences to be successful and may overly exploit them, which can cause the agent to adopt suboptimal behaviors. This paper develops an approach that uses diverse past trajectories for faster and more efficient online RL, even if these trajectories are suboptimal or not highly rewarded. The proposed algorithm combines a policy improvement step with an additional exploration step using offline demonstration data. The main contribution of this paper is that by regarding diverse past trajectories as guidance, instead of imitating them, our method directs its policy to follow and expand past trajectories while still being able to learn without rewards and approach optimality. Furthermore, a novel diversity measurement is introduced to maintain the team's diversity and regulate exploration. The proposed algorithm is evaluated on discrete and continuous control tasks with sparse and deceptive rewards. Compared with the existing RL methods, the experimental results indicate that our proposed algorithm is significantly better than the baseline methods regarding diverse exploration and avoiding local optima.

2024-02-08

2402.04597

Francisco Chicano

Javier Ferrer, Francisco Chicano, Jos\'e Antonio Ortega Toro

CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines

Preprint of the submitted version of the article in Journal of Heuristics

J. Heuristics 27(1-2): 229-249 (2021)

10.1007/s10732-020-09462-w

cs.AI cs.SE

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

In Software Product Lines (SPLs) it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist. Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations (pairwise). Furthermore, when testing a single product is a great effort, it is desirable to first test products composed of a set of priority features. This problem is called Prioritized Pairwise Test Data Generation Problem. State-of-the-art algorithms based on Integer Linear Programming for this problema are faster enough for small and medium instances. However, there exists some real instances that are too large to be computed with these algorithms in a reasonable time because of the exponential growth of the number of candidate solutions. Also, these heuristics not always lead us to the best solutions. In this work we propose a new approach based on a hybrid metaheuristic algorithm called Construct, Merge, Solve & Adapt. We compare this matheuristic with four algorithms: a Hybrid algorithm based on Integer Linear Programming ((HILP), a Hybrid algorithm based on Integer Nonlinear Programming (HINLP), the Parallel Prioritized Genetic Solver (PPGS), and a greedy algorithm called prioritized-ICPL. The analysis reveals that CMSA results in statistically significantly better quality solutions in most instances and for most levels of weighted coverage, although it requires more execution time.

2024-02-08

2402.04841

Jianyuan Guo

Jianyuan Guo, Zhiwei Hao, Chengcheng Wang, Yehui Tang, Han Wu, Han Hu, Kai Han, Chang Xu

Data-efficient Large Vision Models through Sequential Autoregression

15 pages

ICML 2024

cs.CV

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

Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens. In this paper, we delve into the development of an efficient, autoregression-based vision model, innovatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://github.com/ggjy/DeLVM.

2024-06-07

2402.04938

Luis Costero

Jennifer Hern\'andez-B\'ecares, Luis Costero, Pedro Pablo G\'omez-Mart\'in

An approach to automated videogame beta testing

Entertainment Computing, Elsevier. 18. pp 79 to 92. (2017)

10.1016/j.entcom.2016.08.002

cs.AI

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

Videogames developed in the 1970s and 1980s were modest programs created in a couple of months by a single person, who played the roles of designer, artist and programmer. Since then, videogames have evolved to become a multi-million dollar industry. Today, AAA game development involves hundreds of people working together over several years. Management and engineering requirements have changed at the same pace. Although many of the processes have been adapted over time, this is not quite true for quality assurance tasks, which are still done mainly manually by human beta testers due to the specific peculiarities of videogames. This paper presents an approach to automate this beta testing.

2024-02-08

2402.04979

Thomas P\"ollabauer

Thomas P\"ollabauer, Fabian R\"ucker, Andreas Franek, Felix Gorschl\"uter

Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training

Scandinavian Conference on Image Analysis 2023

In Scandinavian Conference on Image Analysis 2023 (pp. 569-585). Cham: Springer Nature Switzerland

cs.CV cs.AI

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

Current state-of-the-art 6d pose estimation is too compute intensive to be deployed on edge devices, such as Microsoft HoloLens (2) or Apple iPad, both used for an increasing number of augmented reality applications. The quality of AR is greatly dependent on its capabilities to detect and overlay geometry within the scene. We propose a synthetically trained client-server-based augmented reality application, demonstrating state-of-the-art object pose estimation of metallic and texture-less industry objects on edge devices. Synthetic data enables training without real photographs, i.e. for yet-to-be-manufactured objects. Our qualitative evaluation on an AR-assisted sorting task, and quantitative evaluation on both renderings, as well as real-world data recorded on HoloLens 2, sheds light on its real-world applicability.

2024-02-08

2402.05149

Janaka Chathuranga

Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar

FlowPG: Action-constrained Policy Gradient with Normalizing Flows

Thirty-seventh Conference on Neural Information Processing Systems. 2023

cs.LG cs.AI

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

Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-critical and resource-allocation related decision making problems. A major challenge in ACRL is to ensure agent taking a valid action satisfying constraints in each RL step. Commonly used approach of using a projection layer on top of the policy network requires solving an optimization program which can result in longer training time, slow convergence, and zero gradient problem. To address this, first we use a normalizing flow model to learn an invertible, differentiable mapping between the feasible action space and the support of a simple distribution on a latent variable, such as Gaussian. Second, learning the flow model requires sampling from the feasible action space, which is also challenging. We develop multiple methods, based on Hamiltonian Monte-Carlo and probabilistic sentential decision diagrams for such action sampling for convex and non-convex constraints. Third, we integrate the learned normalizing flow with the DDPG algorithm. By design, a well-trained normalizing flow will transform policy output into a valid action without requiring an optimization solver. Empirically, our approach results in significantly fewer constraint violations (upto an order-of-magnitude for several instances) and is multiple times faster on a variety of continuous control tasks.

2024-02-09