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6th International Conference on Computing, Electronics and Communications engineering, iCCECE 2023

作者： El Zouka, Hesham A. Arab Academy for Science Technology and Maritime Transport College of Engineering and Technology Computer Eng. Dept Alexandria Egypt

ISBN: (纸本)9798350340921

Nodes represent the core of wireless sensor networks, as being able to connect and exchange information. Despite their limited power and the issue of energy consumption that clearly affects their performance, it has become quite urgent to tackle this issue due to the ongoing need to deploy wireless sensor networks in many fields. To maintain the utmost benefit of nodes regardless their energy consumption problem, clustering has been suggested as the best available solution and this paper introduces a clustering-based routing algorithm to help nodes transmit data to the base station using k-means for selecting clusters. Here, two clusters are chosen, one for collecting data and the other for transmitting it and via a multi-hop approach, the data will be sent to the base station and, hence, the quality and the lifetime of the network will be improved. Energy and security should be considered when choosing the most suitable cost path to achieve the most efficient energy consumption method. The related existing authentication techniques are likely to face many challenges such as the lack of authorization, the limited resources, and the need for a lightweight authentication protocol. Therefore, it is important to provide a security framework and protect the network from attacks with minimum power consumption. This paper proposes a secure and robust three-factor-based authentication protocol, called defense-WSN, which can ensure mutual authentication between nodes and prevent security threats. The simulation results show that the proposed approach can deliver higher performance and lower power consumption compared to other conventional solutions. © 2023 IEEE.

关键词： Energy efficiency

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Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval 24

Not All Pairs are Equal: Hierarchical Learning for Average-P...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Yang Xu, Qianqian Wen, Peisong Dai, Siran Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences BDKM University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The rapid growth of online video resources has significantly promoted the development of video retrieval methods. As a standard evaluation metric for video retrieval, Average Precision (AP) assesses the overall rankings of relevant videos at the top list, making the predicted scores a reliable reference for the users. However, recent video retrieval methods utilize pair-wise losses that treat all sample pairs equally, leading to an evident gap between the training objective and evaluation metric. To effectively bridge this gap, in this work, we aim to address two primary challenges: a) The current similarity measure and AP-based loss are suboptimal for video retrieval;b) The noticeable noise from frame-to-frame matching introduces ambiguity in estimating the AP loss. In response to these challenges, we propose the Hierarchical learning framework for Average-Precision-oriented Video Retrieval (HAP-VR). For the former challenge, we develop the TopK-Chamfer Similarity and QuadLinear-AP loss to measure and optimize video-level similarities in terms of AP. For the latter challenge, we suggest constraining the frame-level similarities to achieve an accurate AP loss estimation. Experimental results present that HAP-VR outperforms existing methods on several benchmark datasets, providing a feasible solution for video retrieval tasks and thus offering potential benefits for the multi-media application. © 2024 Owner/Author.

关键词： Self-supervised learning

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Multi-Source Data with Laplacian Eigenmaps and Denoising Autoencoder for Predicting Microbe-Disease Associations via Convolutional Neural Network

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Journal of computer science & technology 2025年第2期40卷 588-604页

作者： Xiu-Juan Lei Ya-Li Chen Yi Pan School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of ScienceShenzhen 518055China

1 School of computer science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of computer science and Control engineering,Shenzhen Institute of Advanced technology,Chinese academy of sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced technology,Chinese academy of science,Shenzhen 518055,China E-mail:xjlei@***;yalichen@***;***@*** Received December 9,2022;accepted July 29,*** Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of *** this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named ***,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and ***,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease *** the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease ***,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease *** results show that the proposed method is more effective than existing *** addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.

关键词： multi-source data Laplacian eigenmap denoising autoencoder convolutional neural network microbe-disease association

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

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International Journal of Information technology (Singapore) 2024年第3期16卷 1527-1538页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russian Federation Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russian Federation

The widespread dissemination of misinformation and propaganda has become a crucial issue in societal conversations. This study presents an innovative framework to counter propaganda within information warfare using a modified logistic differential equation integrated with a Physics-Informed Neural Network (PINN). Employing the LIAR dataset, which reflects real-world political discourse, our model captures the intricate dynamics of propaganda. The novel approach effectively estimates underlying dynamics and unknown parameters, yielding superior accuracy (97.32%) compared to benchmark models. A parameter sensitivity analysis reveals the significant impact of the spread rate parameter, shedding light on aggressive propaganda dissemination. This research paves the way for robust methods in combating propaganda, demonstrating the effectiveness and superiority of the proposed PINN-based model over established benchmark models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Information warfare Nonlinear differential equations PINN Propaganda Random forest classifier TF-IDF

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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HyResPR: Hybridized Framework for Recommendation of Research Paper Using Semantically Driven Machine Learning Models

HyResPR: Hybridized Framework for Recommendation of Research...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2022

作者： Maddineni, Saketh Deepak, Gerard Praveen, S.V. Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh Tadepalligudem India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9789819946334

The obligation for research paper recommendation is high as scientific document recommendations like research paper recommendation frameworks are not many in number. The knowledge-centric semantically inclined framework for research paper recommendation HyResPR has been proposed. HyResPR is a hybridized research paper recommendation model which is implemented on the RARD II dataset. A hybridized research paper recommendation framework (HyResPR) is proposed. It uses user queries as input to obtain the query words after preprocessing, later these query words are induvial integrated with the domain ontologies. The dataset is subjected to preprocessing to create a synthesized knowledge map with the help of category term mapping and static domain ontology alignment. Features are extracted from the synthesized knowledge map and classified using logistic regression, the extracted query words are processed along with the top 75% of the instances classified. The experimentations are conducted on the RARD II dataset which is classified using the logistic regression classifier. The SemantoSim similarity measure and Cosine similarity measures are used to compute the similarity among the extracted instances from RARD II dataset. The query words obtained from input, and relevant research papers are recommended back to user depending on the value of the semantic similarity. Auxiliary knowledge is incorporated by using static domain ontology, topic modeling has been used experimentations have been computed for 1416 queries on the RARD II dataset. The proposed HyResPR framework achieved the highest average accuracy of 96.43%, recall of 97.05%, with a least FDR value of 0.05 observed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Joint user profiling with hierarchical attention networks

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Frontiers of computer science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

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ASocTweetPred: Mining and Prediction of Anti-social and Abusive Tweets for Anti-social Behavior Detection Using Selective Preferential Learning 13th

ASocTweetPred: Mining and Prediction of Anti-social and Abus...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Bhaveeasheshwar, E. Deepak, Gerard Mala, C. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru India Manipal Academy of Higher Education Manipal Udupi India

ISBN: (纸本)9783031274985

In the present times, owing to a large number of social interactions via Online Social Networking platforms. Because the content is not moderated on the World Wide Web, especially on the Social Web, due to lack of moderation, there are many derogatory, Anti-Social, and racist comments. Specifically, Twitter, YouTube, Instagram, Facebook comments, and similar social network platforms. In this paper, ASocTweetPred for Mining and Prediction of Anti-Social and Abusive Tweets for Anti-Social Behavior Detection has been put forth by encompassing Ontologies of different phases, Knowledge Graph, and the classification of the dataset using the LSTM to generate and yield matching ontology snippets by computing the Semantic Similarity using the Bose Einstein’s Index and the APMI measure. Subsequently, the matched ontology snippets and the formulated Knowledge Graph are used in order to extract features by preferential selection using Shannon’s Entropy and Horn’s Index with differential step deviation measures. A triadic hybrid model of the LSTM, Bagging with Random Forest and SVC, and XGBoost classifiers are made use for Anti-Social Tweets prediction. An overall Precision of 97.93%, Recall of 98.09%, Accuracy of 98.01%, F-Measure of 98.00%, and with the lowest FNR of 0.02 has been achieved by the proposed ASocTweetPred framework. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Enhance Intrusion Detection by Analyzing the Behavior of Labeled and Unlabeled Classification to Obtain Better Accuracy 2

Enhance Intrusion Detection by Analyzing the Behavior of Lab...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Suriya Prakash, J. Srinidhi, N. Latha, A. Chaithra Kiran, S. Department of Computer Science and Engineering Karnataka Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Sri Krishna Institute Of Technology Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Bengaluru India KSIT Department of Computer Science and Design Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519056

Intrusion Detection System has accelerated globally as a result of the need to identify intrusions that occur in network data flow. The two IDS methods that are primarily used by machine learning and network pattern detection are anomaly-based and signature-based methods. A network can be made foolproof by keeping it safe by keeping an eye out for any hazardous activity. In network dataflow systems, network traffic can be produced via simulators. Simulators are a useful tool for detecting harmful behavior. Intrusions that are host-based, protocol-based, application protocol-based, network-based, and hybrid-based are some of the most prevalent kinds that have been discovered. In this paper,open source datasets ISP datasets and WIDE datasets are employed for experimentation. The crux of this paper is to carry out network classification by using minimal training sets and classifying maximal test sets for procuring accurate results. Lastly, the Labelled and Unlabelled technique is used to compare various accuracy outcomes. The suggested scheme's effectiveness is confirmed by the empirical investigation conducted on ISP and WIDE data. In this paper, the labeled and unlabeled classification proposed here identifies the intruder application with minimal labeled dataset. Most importantly automatic labeling of large test sets with small training set is accomplished. The analysis process for classification methods involving ISP and WIDE datasets by an experimental way has produced efficient classification results. The Labeled classification algorithm produces 93% accuracy and the unlabeled classification algorithm produces 84% accuracy. © 2024 IEEE.

关键词： Network intrusion

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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