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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shi, Chenglong Geng, Haibin Jiang, Wenfeng Li, Jinbao Wu, Chao Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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Boosting Graph Convolution with Disparity-induced Structural Refinement 25

Boosting Graph Convolution with Disparity-induced Structural...

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34th ACM Web Conference, WWW 2025

作者： Huang, Sujia Pi, Yueyang Zhang, Tong Liu, Wenzhe Cui, Zhen Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Fuzhou University College of Computer and Data Science Fuzhou China Huzhou University School of Information Engineering Huzhou China Beijing Normal University School of Artificial Intelligence Beijing China

ISBN: (纸本)9798400712746

Graph Neural Networks (GNNs) have expressed remarkable capability in processing graph-structured data. Recent studies have found that most GNNs rely on the homophily assumption of graphs, leading to unsatisfactory performance on heterophilous graphs. While certain methods have been developed to address heterophilous links, they lack more precise estimation of high-order relationships between nodes. This could result in the aggregation of excessive interference information during message propagation, thus degrading the representation ability of learned features. In this work, we propose a Disparity-induced Structural Refinement (DSR) method that enables adaptive and selective message propagation in GNN, to enhance representation learning in heterophilous graphs. We theoretically analyze the necessity of structural refinement during message passing grounded in the derivation of error bound for node classification. To this end, we design a disparity score that combines both features and structural information at the node level, reflecting the connectivity degree of hopping neighbor nodes. Based on the disparity score, we can adjust the aggregation of neighbor nodes, thereby mitigating the impact of irrelevant information during message passing. Experimental results demonstrate that our method achieves competitive performance, mostly outperforming advanced methods on both homophilous and heterophilous datasets. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers 6

An Advanced Intrusion Detection Solution for Networks based ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Veena, K. Meena, K. Mercy Theresa, M. Hemalatha, C. Rajalakshmi, D. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India Department of Computer Science and Engineering GITAM School of Technology GITAM Bangalore561203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Computer Science and Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (纸本)9798350398496

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds. © 2023 IEEE.

关键词： Network security

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A Multi-Dimensional Deep Learning Approach for Enhanced Cardiovascular Disease Diagnosis using ECG Signals 8

A Multi-Dimensional Deep Learning Approach for Enhanced Card...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Evangelin Sonia, S.V. Nedunchezhian, R. Rajalakshmi, M. Kavya, S.P. Parveen, H. Summia Jerusha Miraclin Dulcie, B. Karunya Institute of Technology and Sciences Division of Cse Coimbatore India Coimbatore Institute of Technology Computer Science and Engineering Coimbatore India Coimbatore Institute of Technology Information Technology Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar college of Engineering Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

The diagnosis of cardiovascular disease is essential for timely clinical intervention and patient prognose. Although electrocardiogram is performed frequently for cardiovascular disease diagnosis, its interpretation requires experts. This study first introduces a Multi-Dimensional Deep Neural Network aimed to improve the accuracy and efficiency for cardiovascular disease diagnosis from electrocardiogram data. The Multi-Dimensional Deep Neural Network architecture of our electrocardiogram signal model, which coalesces convolutional and recurrent neural networks (layers) to learn spatial/temporal features. We developed the model on a large annotated dataset of electrocardiogram recordings using training and validation datasets, yielding personal performance compared to baseline methods as well as known deep learning models. We summarize the various statistics-sensitivity, specificity;overall accuracies-that are calculated for our Multi-Dimensional Deep Neural Network indicating that our model is both robust and reliable in terms of diagnostics. This research could lead to more advanced neural networks in medical diagnostics, which may provide clinicians with an effective tool for the clinical diagnosis of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Causal discovery with endogenous context variables 38

Causal discovery with endogenous context variables

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Günther, Wiebke Popescu, Oana-Iuliana Rabel, Martin Ninad, Urmi Gerhardus, Andreas Runge, Jakob Institute of Data Science Jena Germany Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany Dresden Leipzig Germany Technische Universität Dresden Faculty of Computer Science Dresden Germany

Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variables as those variables that indicate this change in causal mechanisms. An example are the causal relations in soil moisture-temperature interactions and their dependence on soil moisture regimes: Dry soil triggers a dependence of soil moisture on latent heat, while environments with wet soil do not feature such a feedback, making it a context-specific property. Crucially, a regime or context variable such as soil moisture need not be exogenous and can be influenced by the dynamical system variables - precipitation can make a dry soil wet - leading to joint systems with endogenous context variables. In this work we investigate the assumptions for constraint-based causal discovery of context-specific information in systems with endogenous context variables. We show that naive approaches such as learning different regime graphs on masked data, or pooling all data, can lead to uninformative results. We propose an adaptive constraint-based discovery algorithm and give a detailed discussion on the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. Numerical experiments demonstrate the performance of the proposed method over alternative baselines, but they also unveil current limitations of our method. © 2024 Neural information processing systems foundation. All rights reserved.

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Consistency-aware unsupervised label learning for cross-domain person re-identification

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Multimedia Tools and Applications 2024年 1-20页

作者： Geng, Yanbing Lian, Yongjian Cui, Fangshu Zhang, Xiaowei Zhou, Mingliang Zhang, Geao School of Data Science and Technology North University of China Shanxi Taiyuan030051 China Shandong Key Laboratory of Intelligent Information Processing School of Computer Science and Technology Qingdao University Qingdao266071 China School of Computer Science Chongqing University 174 Shazheng Street Shapingba District Chongqing400044 China College of Information Science and Engineering Northeastern University 11 Lane 3 Wenhua Road Heping District Liaoning Shenyang110819 China

Un-refined pseudo labels always disturb the cross-domain Re-ID performance in unsupervised clustering methods. In this paper, we propose a consistency-aware unsupervised label learning network to refine noisy labels for promising domain adaptation. Specifically, we use a hetero-generated label consistency constraint to mine possible label information, keep a reference consistency of the target pairs on the relative comparative characteristics. We then use a cross-granularity attention consistency constraint to refine the pseudo labels meanwhile learning multi-granularity discriminative representation, keeping a self-similarity consistency of the corresponding parts on the discriminative regions. Our model is fine-tuned by the refined pseudo-labels to reduce the domain gap while coping with the intra-domain variation. Experimental results demonstrate that the proposed method can achieve superior performance on several benchmark datasets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Benchmarking

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A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks 2

A Novel Approach for Driver Drowsiness Detection using Deep ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sharon, K.V. Natarajan, B. Madhumitha, P. Dhanamathi, A. Prabu, S. Saranya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Dept. of Artificial Intell. and Data Science Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India K. Ramakrishnan College of Engineering Dept. of Cse Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver's eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions. © 2024 IEEE.

关键词： computer vision

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Adapting Contextual Embedding to Identify Sentiment of E-commerce Consumer Reviews with Addressing Class Imbalance Issues

Adapting Contextual Embedding to Identify Sentiment of E-com...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Rakib Mollah, Md. Abdur Jahangir Kabir, Mir Md. Reza, Md. Sazid Kabir, Monika Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh Td School University of Technology Sydney Data Science and Innovation Sydney Australia Murdoch University Information Technology Perth Australia

ISBN: (纸本)9798350350289

Understanding consumer attitudes toward specific products is crucial for boosting sales in the e-commerce industry. To effectively target customers with popular products based on reviews, the classification of consumer feedback becomes imperative. However, classifying product reviews can be challenging, particularly when dealing with imbalanced data labels, which often result in suboptimal classification performance. This study builds upon previous efforts that utilized the Amazon Fine Food Reviews dataset for classification tasks. While these prior attempts showed promise, they were hindered by either poor embeddings or the prevalent class imbalance issue. In response, this research tries to solve these problems by using word embeddings with RoBERTa, a pre-trained transformer-based language model, to classify reviews. Additionally, the XGBoost classifier was implemented, along with embeddings from the language model. Losses were first calculated with equal weights for all class labels, and a re-weighted loss was subsequently adopted to balance the impact of each class on the loss function during training. The incorporation of RoBERTa and XGBoost, along with the class label re-weighting, contributed to improved capturing of intricate word relationships within reviews. As a result, this approach achieves significantly improved accuracy in both binary and multiclass classifications compared to earlier endeavors. Notably, it attained an impressive accuracy of 83.84% in multiclass classification and 93.29% in binary classification tasks, marking a substantial advancement in the field of consumer review analysis. © 2024 IEEE.

关键词： Embeddings

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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