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International Conference on Intelligent Systems: Theories and Applications (SITA)

作者： Jamal Eddine Rafiq Mohammed Amraouy Abdelwahed Namir Abdellah Bennane Abdelali Zakrani Laboratory of Information Technology and Modeling Ben M'sik Faculty of Science Hassan II University Casablanca Morocco National Institute of Posts and Telecommunications Rabat Morocco Inspectors Training Center for Education Mohammed V University Rabat Morocco Laboratory of Artificial Intelligence & Complex Systems Engineering National School of Arts and Crafts of Casablanca Hassan II University Casablanca Morocco

As online learning evolves rapidly and discussion forums become increasingly important in educational environments, this study analyzes the impact of participation in discussion forums on the learning performance of future computer science teachers. data were collected to assess their level of engagement and academic outcomes. The methodology included the use of natural language processing techniques, such as the Bidirectional Encoder Representations from Transformers (BERT) model, and Latent Semantic Analysis (LSA) to analyze the emotional state and relevance of their contributions. Preliminary results indicate a positive correlation between active participation and their learning performance. Additionally, this study makes a significant contribution by analyzing the correlation between various participation factors and learner performance. These findings highlight the importance of discussion forums as interactive educational tools to enhance learning and promote learner engagement. Important implications are also presented in relation to the obtained results.

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JDAT: Joint-dimension-aware transformer with strong flexibility for EEG emotion recognition

TechRxiv

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TechRxiv 2021年

作者： Wang, Zeyu Zhou, Ziqun Shen, Haibin Xu, Qi Huang, Kejie College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China School of Artificial Intelligence Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian16024 China

Electroencephalography (EEG) emotion recognition, an important task in Human-Computer Interaction (HCI), has made a great breakthrough with the help of deep learning algorithms. Although the application of attention mechanism on conventional models has improved its performance, most previous research rarely focused on multiplex EEG features jointly, lacking a compact model with unified attention modules. This study proposes Joint-Dimension-Aware Transformer (JDAT), a robust model based on squeezed Multi-head Self-Attention (MSA) mechanism for EEG emotion recognition. The adaptive squeezed MSA applied on multidimensional features enables JDAT to focus on diverse EEG information, including space, frequency, and time. Under the joint attention, JDAT is sensitive to the complicated brain activities, such as signal activation, phase-intensity couplings, and resonance. Moreover, its gradually compressed structure contains no recurrent or parallel modules, greatly reducing the memory and complexity, and accelerating the inference process. The proposed JDAT is evaluated on DEAP, DREAMER, and SEED datasets, and experimental results show that it outperforms state-of-the-art methods along with stronger flexibility. © 2021, CC BY.

关键词： Emotion Recognition

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AI4S Based on DeSci: Reference Model and Research Issues

AI4S Based on DeSci: Reference Model and Research Issues

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wenwen Ding Juanjuan Li Rui Qin Sangtian Guan Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The rise of artificial intelligence for science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems.

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Prior-Guided Adversarial Initialization for Fast Adversarial Training

arXiv

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arXiv 2022年

作者： Jia, Xiaojun Zhang, Yong Wei, Xingxing Wu, Baoyuan Ma, Ke Wang, Jue Cao, Xiaochun SKLOIS Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Tencent AI Lab Shenzhen China Institute of Artificial Intelligence Beihang University Beijing China School of Data Science Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Computer Science and Technology UCAS Beijing China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China

Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial training (SAT). However, initial FAT encounters catastrophic overfitting, i.e., the robust accuracy against adversarial attacks suddenly and dramatically decreases. Though several FAT variants spare no effort to prevent overfitting, they sacrifice much calculation cost. In this paper, we explore the difference between the training processes of SAT and FAT and observe that the attack success rate of adversarial examples (AEs) of FAT gets worse gradually in the late training stage, resulting in overfitting. The AEs are generated by the fast gradient sign method (FGSM) with a zero or random initialization. Based on the observation, we propose a prior-guided FGSM initialization method to avoid overfitting after investigating several initialization strategies, improving the quality of the AEs during the whole training process. The initialization is formed by leveraging historically generated AEs without additional calculation cost. We further provide a theoretical analysis for the proposed initialization method. We also propose a simple yet effective regularizer based on the prior-guided initialization, i.e., the currently generated perturbation should not deviate too much from the prior-guided initialization. The regularizer adopts both historical and current adversarial perturbations to guide the model learning. Evaluations on four datasets demonstrate that the proposed method can prevent catastrophic overfitting and outperform state-of-the-art FAT methods. The code is released at https://***/jiaxiaojunQAQ/FGSM-PGI. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Generative Federated Learning with Small and Large Models In Consumer Electronics for Privacy preserving data Fusion in Healthcare Internet of Things

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IEEE Transactions on Consumer Electronics 2025年

作者： Ghazal, Taher M. Islam, Shayla Hasan, Mohammad Kamrul Abu-Shareha, Ahmad A. Mokhtar, Umi A. Khan, M. Attique Baili, Jamel Saeed, Ali Q. Bhattt, Mohammed Wasim Ahmad, Munir Al-Ahliyya Amman University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Amman Jordan UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur 56000 Malaysia Universiti Kebangsaan Malaysia (UKM) Center for Cyber Security Faculty of Information Science and Technology Malaysia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Data Science and Artificial Intelligence Amman Jordan Prince Mohammad bin Fahd University Saudi Arabia King Khalid University College of Computer Science Department of Computer Engineering Abha 61413 Saudi Arabia Northern Technical University Computer Center Ninevah Iraq Korea University College of Informatics Seoul 02841 South Korea

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences. © 1975-2011 IEEE.

关键词： data Fusion Federated Learning Healthcare IoT Privacy-Preserving Triple DES

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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

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IEEE Internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media Internet of Things Collaboration Visual analytics Feature extraction

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1−ρǫ-approximate pure Nash equilibria algorithms for weighted congestion games and their runtimes

arXiv

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arXiv 2022年

作者： Ren, Chunying Wu, Zijun Xu, Dachuan Yang, Xiaoguang Department of Operations Research and Information Engineering Beijing University of Technology Pingleyuan 100 Beijing100124 China Department of Artificial Intelligence and Big Data Hefei University Jinxiu 99 Anhui Hefei230601 China Academy of Mathematics and System Science University of Chinese Academy of Sciences Beijing100190 China

This paper concerns computing approximate pure Nash equilibria in weighted congestion games, which has been shown to be PLS-complete. With the help of Ψ-game and approximate potential functions, we propose two algorithms based on best response dynamics, and prove that they efficiently compute 1−ρǫ-approximate pure Nash equilibria for ρ = d! and ρ = 22··WW·+(dd+1)+1 ≤ d+1, respectively, when the weighted congestion game has polynomial latency functions of degree at most d ≥ 1 and players’ weights are bounded from above by a constant W ≥ 1. This improves the recent work of Feldotto et al. [2017] and Giannakopoulos et al. [2022] that showed efficient algorithms for computing dd+o(d)approximate pure Nash equilibria. © 2022, CC BY.

关键词： Computer games

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Feature selection based on min-redundancy and max-consistency

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Advances in Computational intelligence 2021年第1期2卷 1-11页

作者： Guo, Yanting Hu, Meng Tsang, Eric C. C. Chen, Degang Xu, Weihua College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Information Technology Macau University of Science and Technology Taipa China Department of Mathematics and Physics North China Electric Power University Beijing China College of Artificial Intelligence Southwest University Chongqing China

Feature selection can effectively eliminate irrelevant or redundant features without changing features semantics, so as to improve the performance of learning and reduce the training time. In most of the existing feature selection methods based on rough sets, eliminating the redundant features between features and decisions, and deleting the redundant features between features are performed separately. This will greatly increase the search time of feature subset. To quickly remove redundant features, we define a series of feature evaluation functions that consider both the consistency between features and decisions, and redundancy between features, then propose a novel feature selection method based on min-redundancy and max-consistency. Firstly, we define the consistency of features with respect to decisions and the redundancy between features from neighborhood information granules. Then we propose a combined criterion to measure the importance of features and design a feature selection algorithm based on minimal-redundancy-maximal-consistency (mRMC). Finally, on UCI data sets, mRMC is compared with three other popular feature selection algorithms based on neighborhood idea, from classification accuracy, the number of selected features and running time. The experimental comparison shows that mRMC can quickly delete redundant features and select useful features while ensuring classification accuracy.

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Application of IoT to Monitor the Shipping of Goods in Maritime Transportation System

Application of IoT to Monitor the Shipping of Goods in Marit...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Naim Shaikh Kishori Kasat Vishal Moyal N. Dilip Kumar Sivakumar Ponnusamy K. Pavithra Global Business School and Research Centre Dr. D. Y. Patil Vidyapeeth (Deemed to be University) Pune India Symbiosis School for Liberal Arts Symbiosis International Deemed University Pune India Electrical Engineering SVKM's Institute of Technology Dhule India Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Tirupati India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India

Surprising occurrences like temperature variations, humidity shifts, and vibrations might devalue the products being carried. ICT must be used to stop unwelcome occurrences from happening and fix some problems with the delivery of products. Making it possible to monitor the conditions of storage and transportation of products is one of the main issues. The IoT offers opportunities for tracking and locating goods from point of origin to point of destination and for monitoring traffic conditions. IoT-based solutions make it possible to identify the circumstances around certain delivery procedure abnormalities, including their timing, location, and cause. Additionally, these systems offer the ability to inform the client, transportation providers, or other participants to request urgent help. Businesses may enhance their product portfolio, improve the competitiveness of their business, and enhance route travel times and transportation circumstances. As a result, IoT technology can be advantageous to all parties involved in the logistics chain. The potential of IoT applications for supply chain and logistics management applications, such as vehicle fleet surveillance, commodities control, monitoring location-based assistance, etc., are presented in this article.

关键词： Temperature sensors Temperature measurement Vibrations Wireless sensor networks Surveillance Containers Real-time systems

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Unsupervised Time Series Anomaly Detection under data Contamination

Unsupervised Time Series Anomaly Detection under Data Contam...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiaohui Lin Zuoyong Li Xunhua Huang Xinwei Chen Haoyi Fan College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China Fujian Engineering and Research Center of New Chinese Lacquer Materials Minjiang University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE.

关键词： Training Dimensionality reduction Time series analysis Training data Mean square error methods Gaussian distribution Robustness

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