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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Zhou, Pengfei Pan, Yijie Zhang, Xun Shou, Jibin Guo, Jiayang Xu, Meiyan Gu, Peipei College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China Zhejiang Moorgen Group Co. Ltd. Zhejiang310000 China Department of Hematology School of Medicine Xiamen University Xiamen361000 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361000 China College of Computer Science Minnan Normal University Zhangzhou363000 China

ISBN: (纸本)9789819619061

Epilepsy, characterized by abnormal neuronal discharges, is a widespread neurological disorder. Traditional detection methods primarily focus on extracting time-frequency features from raw electroencephalogram (EEG) signals, often overlooking the complex interactions between brain regions. To address this limitation, we propose an automated approach for epileptic seizure detection. We first construct a fused brain network (TFPP) by integrating the Pearson Correlation Coefficient (PCC) and Phase Lag Index (PLV) across both temporal and frequency domains, enhancing EEG signals by capturing synchronized activities across different brain regions. Next, we introduce the CNN_sk_BiLSTM model, which leverages Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to sequentially extract spatial and temporal features. The SKAttention mechanism further refines feature extraction across multiple scales. Using the CHB-MIT scalp EEG dataset, the proposed method achieved an average sensitivity of 90.86%, specificity of 98.79%, and accuracy of 97.88%. These results confirm that fused brain functional networks effectively integrate the intra-channel characteristics of EEG signals, demonstrating the superior performance of the proposed approach in epileptic seizure detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Ransomware Classification and Detection With Machine Learning Algorithms

arXiv

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arXiv 2022年

作者： Masum, Mohammad Faruk, Md Jobair Hossain Shahriar, Hossain Qian, Kai Lo, Dan Adnan, Muhaiminul Islam School of Data Science Kennesaw State University United States Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department Computer Science Kennesaw State University United States Institute of Natural Sciences United International University Bangladesh

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F-beta, and precision scores. © 2022, CC BY.

关键词： Malware

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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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Extended Abstract of Graph4Web: A Relation-Aware Graph Attention Network for Web Service Classification

Extended Abstract of Graph4Web: A Relation-Aware Graph Atten...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Kunsong Zhao Jin Liu Zhou Xu Xiao Liu Lei Xue Zhiwen Xie Yuxuan Zhou Xin Wang School of Computer Science Wuhan University Wuhan China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) China School of Big Data and Software Engineering Chongqing University Chongqing China School of Information Technology Deakin University Geelong Australia Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Engineering and Computer Science Syracuse University Syracuse USA

software reuse, as a means to develop new software products with similar functions by virtue of existing software components, has become a popular way during the software development process. In particular, as the service-oriented architecture became popular, web services turned into an indispensable part in modem software development Web services provide a basic composition with high cohesion and loose coupling to support responses among heterogeneous software components, which is the valuable resources for software reuse. The popular web service repositories, such as Programmable Web, contain a mass of web services for beginners and developers to choose from. Nevertheless, the large number of web services also makes it difficult to select the suitable services. Thus, the key to reuse software components lies in how to find appropriate web services from repositories to meet developers requirements in specific application scenarios.

关键词： Couplings Web services Modems Service-oriented architecture software reusability

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Joint Scheduling and Beamforming Design in Traffic-Aware RIS Aided MEC Network

Joint Scheduling and Beamforming Design in Traffic-Aware RIS...

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IEEE Globecom Workshops

作者： Aichen Li Yang Liu Qingqing Wu Qingjiang Shi Jun Zhao School of Information and Communication Engineering Dalian University of Technology Department of Electrical and Computer Engineering University of Macau Macau China School of Software Eng. Tongji Univ. Shanghai Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Nanyang Technological University

ISBN: (纸本)9781665459761

This paper considers exploiting reconfigurable intelligent surface (RIS) in a multi-user multi-access mobile computing (MEC) system. To precisely model the traffic behaviors of the tasks, we consider queuing procedures at both mobile users and the cloud server. This consideration leads to concatenation of queues and makes our problem much more difficult than existing literature since analytic expression of the objective is impossible. Our goal is to jointly optimize the task scheduling, RIS configuration and computation resource allocation to improve the MEC network’s overall computation rate in the long run while maintaining the stability of the queues. To tackle this highly challenging stochastic problem, we develop a Lyapunov guided deep reinforcement learning (DRL) solution, which borrows the Lyapunov optimization theory to stabilize queues and at the same time can effectively optimize the intractable objective in an online manner. Numerical results will be presented to verify the effectiveness of our solution and the performance boost due to the deployment of RIS.

关键词： Processor scheduling Stochastic processes Reinforcement learning Reconfigurable intelligent surfaces Stability analysis Servers Resource management

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An Efficient Searchable Encryption Framework with Weighted Keywords for the Internet of Medical Things (IoMT)

TechRxiv

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TechRxiv 2022年

作者： Wang, Haiyan Li, Yuan He, Ying Luo, Fucai Yu, F. Richard Ming, Zhong Wang, Shulan The College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Department of New Networks Pengcheng Laboratory Shenzhen China The Department of Systems and Computer Engineer Carleton University OttawaON Canada The College of Big Data and Internet Shenzhen Technology University Shenzhen China

With the increasing popularity of smart terminals, the Internet of Things (IoT) vastly expands the influence of information technology and creates great values to our society. The Internet of Medical Things (IoMT) is a typical application of the IoT specifically for medical and healthcare related purposes. In IoMT, personal health records (PHRs) generated by intelligent equipment are usually stored in a cloud sever managed by a semi-trusted cloud provider. However, there is still a possibility of the exposure of private information to unauthorized parties. In order to protect a patient’s private information, and ensure that doctors can search a patient’s PHRs without decrypting all ciphertexts, an efficient searchable encryption framework with weighted keywords is proposed in this paper. In this framework, we implement a weighted keyword model to raise the accuracy of search results, and reduce the computational and storage overhead. Besides, compared with the existing schemes, the framework is supportable of multi-search mechanism, which is adaptable to more application scenarios. Finally, the efficiency and security of this framework is proved through security and performance analysis in this paper. © 2022, CC BY.

关键词： Cryptography

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Determination of Subscribers Coordinates using Flying Network for Emergencies 24

Determination of Subscribers Coordinates using Flying Networ...

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24th International Conference on Advanced Communication Technology, ICACT 2022

作者： DInh, Truong Duy Vishnevsky, Vladimir Pham, Van Dai Le, Duc Tran Kirichek, Ruslan Koucheryavy, Andrey Bonch-Bruevich Saint-Petersburg State University of Telecommunications Prospekt Bolshevikov Department of Software Engineering and Computer Technology St. Petersburg Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya Street Moscow Russia The University of Danang-University of Science and Technology Information Technology Faculty 54 Nguyen Luong Bang Danang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia

ISBN: (纸本)9791188428090

In the last decade, unmanned aerial systems have emerged in different fields, such as entertainment, military, Internet of Things, emergencies. In these systems, unmanned aerial vehicles (UAVs) communicate with each other based on a flying network to provide services to customers such as live streaming, high-speed access point, etc. The mobility of UAVs can bring numerous advantages in emergency cases, for example, rescuing and searching victims in areas where the network infrastructure is not available. However, the crucial task needed to be done first is to locate the victims' coordinates. In this paper, we propose a method for detecting the coordinates of subscribers with the Wi-Fi signals generated from victims' phones in the absence of network infrastructure of communication operators using a flying network for emergencies based on UAV-swarms. We develop a new protocol for communication between UAVs and UAV-swarms. A structure of UAV-swarms is considered to optimize the searching time. The results of the proposed method are achieved by simulation. © 2022 Global IT Research Institute-GiRI.

关键词： Wi-Fi

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Connecting latent relationships over heterogeneous attributed network for recommendation

arXiv

引用

arXiv 2021年

作者： Duan, Ziheng Wang, Yueyang Ye, Weihao Feng, Zixuan Fan, Qilin Li, Xiuhua School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Control Science and Technology Zhejiang University Hangzhou310027 China

Recently, deep neural network models for graph-structured data have been demonstrating to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most existing GNN models mainly focus on the homogeneous graph. They cannot characterize heterogeneous and complex data in the recommendation system. Meanwhile, it is challenging to develop effective methods to mine the heterogeneity and latent correlations in the graph. In this paper, we adopt Heterogeneous Attributed Network (HAN), which involves different node types as well as rich node attributes, to model data in the recommendation system. Furthermore, we propose a novel graph neural network-based model to deal with HAN for Recommendation, called HANRec. In particular, we design a component connecting potential neighbors to explore the influence between neighbors and provide two different strategies with the attention mechanism to aggregate neighbors’ information. The experimental results on two real-world datasets prove that HANRec outperforms other state-of-the-arts methods1 Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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A Switch Architecture for Time-Triggered Transmission with Best-Effort Delivery

arXiv

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arXiv 2023年

作者： Li, Zonghui Zhu, Wenlin Shin, Kang G. Wan, Hai Song, Xiaoyu Yang, Dong Ai, Bo The Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2121 United States The Software School Tsinghua University Beijing100084 China The Department of Electrical and Computer Engineering Portland State University PortlandOR United States The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

In Time-Triggered (TT) or time-sensitive networks, the transmission of a TT frame is required to be scheduled at a precise time instant for industrial distributed real-time control systems. Other (or best-effort (BE)) frames are forwarded in a BE manner. Under this scheduling strategy, the transmission of a TT frame must wait until its scheduled instant even if it could have been transmitted sooner. On the other hand, BE frames are transmitted whenever possible but may miss deadlines or may even be dropped due to congestion. As a result, TT transmission and BE delivery are incompatible with each other. To remedy this incompatibility, we propose a synergistic switch architecture (SWA) for TT transmission with BE delivery to dynamically improve the end-to-end (e2e) latency of TT frames by opportunistically exploiting BE delivery. Given a TT frame, the SWA generates and transmits a cloned copy with BE delivery. The first frame arriving at the receiver device is delivered with a configured jitter and the other copy ignored. So, the SWA achieves shorter latency and controllable jitter, the best of both worlds. We have implemented SWA using FPGAs in an industry-strength TT switches and used four test scenarios to demonstrate SWA's improvements of e2e latency and controllable jitter over the state-of-the-art TT transmission scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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