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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Pengcheng Zhou, Jinxing Zhao, Yang Guo, Dan Chen, Yanxiang School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)157735897X

The Audio-Visual Video Parsing task aims to recognize and temporally localize all events occurring in either the audio or visual stream, or both. Capturing accurate event semantics for each audio/visual segment is vital. Prior works directly utilize the extracted holistic audio and visual features for intra- and cross-modal temporal interactions. However, each segment may contain multiple events, resulting in semantically mixed holistic features that can lead to semantic interference during intra- or cross-modal interactions: the event semantics of one segment may incorporate semantics of unrelated events from other segments. To address this issue, our method begins with a Class-Aware Feature Decoupling (CAFD) module, which explicitly decouples the semantically mixed features into distinct class-wise features, including multiple event-specific features and a dedicated background feature. The decoupled class-wise features enable our model to selectively aggregate useful semantics for each segment from clearly matched classes contained in other segments, preventing semantic interference from irrelevant classes. Specifically, we further design a Fine-Grained Semantic Enhancement module for encoding intra- and cross-modal relations. It comprises a Segment-wise Event Co-occurrence Modeling (SECM) block and a Local-Global Semantic Fusion (LGSF) block. The SECM exploits inter-class dependencies of concurrent events within the same timestamp with the aid of a new event co-occurrence loss. The LGSF further enhances the event semantics of each segment by incorporating relevant semantics from more informative global video features. Extensive experiments validate the effectiveness of the proposed modules and loss functions, resulting in a new state-of-the-art parsing performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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HASHL: Dynamic Hash Verification for Detecting and Preventing Eclipse Attacks

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IEEE Internet of Things Journal 2025年第13期12卷 23524-23535页

作者： He, Daojing Gong, Wei Tu, Chen Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong

With the rapid development of blockchain technology, P2P networks are facing increasing security threats, among which Eclipse attacks, as a type of network isolation attack, have seriously affected the normal operation of the network and the integrity of data. To address this challenge, this study implements node authentication and dynamic reputation evaluation by leveraging a dynamic hash computation mechanism that integrates challenge strings, node identifiers, and the latest active time, ensuring the uniqueness of node identities and the authenticity of operations. Based on a dynamic hash chain behavior evaluation mechanism, node behaviors are quantified across three dimensions: integrity, consistency, and temporal consistency, enabling precise identification of anomalous nodes. Furthermore, a network prevention repository framework is proposed, which dynamically adjusts the trust index of nodes by combining historical behavior with real-time data, effectively detecting and defending against stealthy Eclipse attacks. In addition, extensive testing on both Bitcoin and Ethereum platforms has shown that the method proposed in this study not only can effectively coexist on these two platforms, but also significantly improves the security and stability of the network, effectively reducing the occurrence of Eclipse attacks. © 2014 IEEE.

关键词： Blockchain

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Vehicle Accident Detection and Reporting System using Internet of Things 5

Vehicle Accident Detection and Reporting System using Intern...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Goyal, Atul Gupta, Priyansh Pandey, Kavita Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350379945

The rising number of vehicles on the road has led to a concerning increase in accidents, as reported by the Indian Government's Ministry of Road Transport and Highways. In many cases, prompt medical assistance can save lives. While numerous solutions exist to address this issue, most are limited to two-wheelers. In contrast, this research study presents a vehicle-independent solution that employs sensors to detect accidents in any vehicle type, making it highly effective and cost-efficient. The proposed model automatically identifies and report accidents in real-time, enhancing the road safety. It utilizes advanced sensors and communication technology to promptly notify emergency services and relevant authorities for timely response and assistance. © 2024 IEEE.

关键词： Vehicle detection

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Analytical study of the encoder-decoder models for ultrasound image segmentation

Analytical study of the encoder-decoder models for ultrasoun...

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作者： Srivastava, Somya Vidyarthi, Ankit Jain, Shikha Department of Computer Science Engineering & Information Technology Jaypee Institute of Information Technology Noida India

Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most complex as the internal organ’s visualization requires experience from the radiologist. Diagnosing the medical conditions and unavailability of experienced radiologists during an emergency requires automated segmentation which heavily depends on computer-aided diagnostic systems. The new generation CAD systems are found to incorporate advanced deep learning algorithms to produce accurate segmentation results. While most of the segmentation models relate to the encoder-decoder model as the base architecture and thus evolve a variety of modifications in its pipeline architecture. This paper presents the analytical study of the various Encoder- Decoder based models like UNet, Residual UNet (Res-U-Net), Dense UNet (DenseUNet), Attention UNet, UNet + +, Double UNet, and U2Net (U-Squared-Net) on ultrasound image segmentation. Further, the paper presents the various trade-offs, application areas, open challenges, and performance analysis of these models on benchmark datasets, namely the HC18 Challenge dataset, CUM dataset, and B-mode Ultrasound Nerve Segmentation dataset. The performance analysis of these models is presented using the six state-of-the-art metrics like Dice coefficient, Jaccard index, sensitivity, specificity, Mean Absolute distance, and Housdorff Distance. Based on the above parameters U2-Net (U-Squared-Net) outperformed all other neural network models for all three datasets. In terms of all four criteria (Dice Coefficient: 0.92, 0.89, 0.9, Jaccard Index: 0.81, 0.79, 0.81, Sensitivity: 0.86, 0.84, 0.86, Specificity: 0.97, 0.95, 0.96), the U2-Net (U-Squared-Net) model performed the best. Over the HC18 Challenge dataset, the CUM dataset, and the B-Mode Ultrasound ne

关键词： Deep neural networks

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Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10035-10044页

作者： Nagamani, T. Logeswari, S. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. © 2024 – IOS Press.

关键词： Diseases

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Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production 39

Sign-IDD: Iconicity Disentangled Diffusion for Sign Language...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, Shengeng He, Jiayi Guo, Dan Wei, Yanyan Li, Feng Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)157735897X

Sign Language Production (SLP) aims to generate semantically consistent sign videos from textual statements, where the conversion from textual glosses to sign poses (G2P) is a crucial step. Existing G2P methods typically treat sign poses as discrete three-dimensional coordinates and directly fit them, which overlooks the relative positional relationships among joints. To this end, we provide a new perspective, constraining joint associations and gesture details by modeling the limb bones to improve the accuracy and naturalness of the generated poses. In this work, we propose a pioneering iconicity disentangled diffusion framework, termed Sign-IDD, specifically designed for SLP. Sign-IDD incorporates a novel Iconicity Disentanglement (ID) module to bridge the gap between relative positions among joints. The ID module disentangles the conventional 3D joint representation into a 4D bone representation, comprising the 3D spatial direction vector and 1D spatial distance vector between adjacent joints. Additionally, an Attribute Controllable Diffusion (ACD) module is introduced to further constrain joint associations, in which the attribute separation layer aims to separate the bone direction and length attributes, and the attribute control layer is designed to guide the pose generation by leveraging the above attributes. The ACD module utilizes the gloss embeddings as semantic conditions and finally generates sign poses from noise embeddings. Extensive experiments on PHOENIX14T and USTC-CSL datasets validate the effectiveness of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Forming

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Dehazing using Generative Adversarial Network - A Review

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SN computer science 2025年第1期6卷 1-17页

作者： Khatun, Amina Mostafiz, Rafid Shorif, Sumaita Binte Uddin, Mohammad Shorif Hadi, Md. Abdul Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science: Information Technology University of Nebraska Omaha Omaha United States

Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a viable method for removing haze from photos in recent years. This is because GAN can understand intricate data patterns and provide high-quality outcomes. This paper provides a thorough examination of the most advanced strategies in dehazing utilizing GAN. The study examines the various elements utilized in GAN-based dehazing, including generator and discriminator architectures, loss functions, and training strategies. It also explores the evaluation metrics employed to assess the effectiveness of GAN-based dehazing methods. It also examines the datasets often used to train and evaluate these models. This research concludes by examining prospective avenues for future study in the domain of dehazing, employing GAN to tackle the obstacles of real-time dehazing, managing intricate scenarios with various atmospheric conditions, and enhancing the resilience of GAN-based dehazing models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric haze Dehazing Generative Adversarial Network (GAN) Performance evaluation metrics

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Leaving None Behind: Data-Free Domain Incremental Learning for Major Depressive Disorder Detection

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IEEE Transactions on Affective Computing 2025年第2期16卷 758-770页

作者： Chen, Tao Guo, Yanrong Hao, Shijie Hong, Richang Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei230009 China

While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some efforts have resorted to aggregating data from different domains to expand the data volume. However, their effectiveness is currently limited by the domain gap and data privacy. Additionally, the class imbalance issue is particularly severe in our application, leading to biased classifying performance accordingly. To address these challenges, we propose Data-Free Domain Incremental Learning for the MDD detection (DIL-MDD) task, accommodating multiple feature distributions by only accessing well-trained models from previous domains and the data in the current domain. Specifically, DIL-MDD consists of two key modules: Adaptive Class-tailored Threshold Learning (ACTL) and Data-Free Domain Alignment (DFDA). The first module measures the discrepancy between the outputs of two sequential domains, based on which we learn a class-tailored threshold adaptively. Building on this, we differentiate between samples that either exhibit similarities or dissimilarities with the previous domain, where this similar sample set is identified to investigate the feature distribution of the historical data. The second module imposes an alignment constraint to narrow the gap between these two sample sets, thereby exploring the expertise of the previous domain. To validate the effectiveness of the proposed method, we conduct extensive experiments on the public MDD datasets, i.e., DAIC-WOZ, MODMA, and CMDC. We also apply our method to another mental health condition, Autism Spectrum Disorder (ASD), to further demonstrate its applicability. Finally, the ablation studies validate the superiority of the proposed modules. © 2024 IEEE.

关键词： Adversarial machine learning

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