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IEEE International Conference on Multi Topic

作者： Rawish Butt Noshina Tariq Mamoona Humayun Ammara Ishaq Department of Avionics Engineering Air University Islamabad Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Arts Humanities and Social Sciences University of Roehampton London United Kingdom

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The expansion of Internet of Things (IoT) places an immediate need for strong security measures, particularly in malware detection. This research was done by using IoT-23 dataset to implement machine learning techniques for malware detection in an IoT network traffic. Random forest and gradient descent were selected and applied to pre-processed data to evaluate its effectiveness in differentiating benign and malicious traffic. Feature reduction was performed using principal component analysis (PCA) and both models were evaluated and tested using cross-validation. Random forest have scored an accuracy of 99.78%. with perfect precision Recall and F1 scores for both classes While gradient boosting achieved 99.45% accuracy and slightly lower precision and recall for non-hazardous traffic. The results indicate that random forest is more suitable for dataset which shows higher accuracy and consistency. The study highlights the efficacy of ensemble learning in malware detection within an IoT environments and emphasizes the importance of proper pre-processing and feature reduction to improve model performance.

关键词： Accuracy Telecommunication traffic Feature extraction Boosting Malware Internet of Things Security Ensemble learning Random forests Principal component analysis

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A Study on Sentiment Analysis of Online Course Review Information Based on XLNet-BiGRU

A Study on Sentiment Analysis of Online Course Review Inform...

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

作者： Jianxin Xiao Zengzhen Shao Shuai Han Zhuangzhuang Li School of Information Science and Engineering Shandong Normal University Jinan China School of Data Science and Computer Science Shandong Women’s University Jinan China

Texts from course comments play a crucial role in the online learning environment on the Internet. In a positive way, the sentiment data contributes significantly to classroom engagement, enhances the effectiveness of instruction, and fosters the long-term growth of online courses. How to effectively assess the emotional qualities of Chinese course review information and adjust the course quality properly is one of the hot topics of the current study. In this study, we propose a sentiment analysis model based on XLNet-BiGRU for categorizing the sentiment polarity of course review text in Chinese online courses. First, the text corpus data is learned using the XLNet pre-training model. The major sentiment characteristics and contextual features are then extracted from the comments using the BiGRU model, and the attention mechanism is used as a weighting method to increase the weight of the sentiment words in the sentences. The Softmax layer ultimately classifies the weighted features. According to the experimental findings, the XLNet-BiGRU model, which incorporates the BiGRU model, can fully extract the text’s deep semantic information and, to a certain extent, increase the task’s effectiveness in classifying sentiments.

关键词： Sentiment analysis Analytical models Dictionaries Semantics Education Feature extraction data models

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Gradient-Guided Test Case Generation for Image Classification Software

Gradient-Guided Test Case Generation for Image Classificatio...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Shunhui Ji Yunhe Li Hai Dong Mingxuan Xiao Pengcheng Zhang Mengyuan Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The widespread use of deep neural networks (DNNs) in image classification sofwares underlines the importance of the robustness. Researchers have proposed sparse adversarial attack methods for generating test cases, which add pixel-level perturbations to construct the test case to mislead the target model. However, the existing methods have certain limitations, such as high time cost, poor flexibility, and poor quality of the test cases. To address these issues, we propose a gradient-guided test case generation method (GGTM) to evaluate the robustness of image classification software. The method firstly identifies the key region in the image based on the gradient-weighted class activation mapping (Grad-CAM) and the prediction confidence of the target model on the input image. In the key region, it selects a set of pixels as candidate perturbation pixels according to the gradient value and the change of loss function. Then perturbations are added to the candidate perturbation pixels after applying a random dropout strategy to reduce some candidate perturbation pixels which is used to avoid local optimum. For the initially constructed test case which can mislead the target model, after removing redundant and unimportant perturbations, perturbations are re-added to optimize the test case. Experiments show the effectiveness of GGTM, which achieves 100% attack success rate. And the test cases generated by GGTM have the best perturbation sparsity. Furthermore, compared with the baseline method SparseAG which achieves optimal perturbation sparsity among the baseline methods, GGTM significantly improves the efficiency.

关键词： Costs Perturbation methods Artificial neural networks Predictive models Software Robustness Image classification

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning

arXiv

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arXiv 2025年

作者： Zhuo, Shengda Fang, Jiwang Lin, Hongguang Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang College of Cyber Security Jinan University Guangzhou510632 China School of Management Jinan University Guangzhou China School of Computer Sun Yat-sen University Guangzhou510275 China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Cyber Security of Jinan University Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou510632 China

Graph Neural Networks (GNNs) have significant advantages in handling non-Euclidean data and have been widely applied across various areas, thus receiving increasing attention in recent years. The framework of GNN models mainly includes the information propagation phase and the aggregation phase, treating nodes and edges as information entities and propagation channels, respectively. However, most existing GNN models face the challenge of disconnection between node and edge feature information, as these models typically treat the learning of edge and node features as independent tasks. To address this limitation, we aim to develop an edge-empowered graph feature preference learning framework that can capture edge embeddings to assist node embeddings. By leveraging the learned multidimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features, thereby obtaining the non-local structural characteristics and fine-grained high-order node features. Specifically, the inclusion of multidimensional edge information enhances the functionality and flexibility of the GNN model, enabling it to handle complex and diverse graph data more effectively. Additionally, integrating relational representation learning into the message passing framework allows graph nodes to receive more useful information, thereby facilitating node representation learning. Finally, experiments on four real-world heterogeneous graphs demonstrate the effectiveness of the proposed model. © 2025, CC BY.

关键词： Graph embeddings

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ML Based Phenotype Analysis Using Differential Gene Expression data in Schizophrenia

ML Based Phenotype Analysis Using Differential Gene Expressi...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Aadhithyan D Shabarirajan KJ Varun Maniappan Shivanesh B Divya S Praghaadeesh R Kalpana Raja I R Oviya Department of Computer Science and Engineering (Artificial Intelligence) Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350384628

ISBN: (纸本)9798350384635

In this paper, differential gene expression (DGE) data in schizophrenia is analysed using machine learning approaches with the objective of predicting cell phenotypes related to the condition. The dataset contains information on cis eQTNs, gene orientation, probe information, gene expression profiles, and SNP information. When first investigating well-known machine learning methods, a hybrid model combining Random Forests and XGBoost is suggested, however it only offers a little increase. Increases in predicted accuracy of more than 99.12% are made possible by the H2O AutoML package. The results demonstrate the capability of ML-based analysis to unravel the underlying biological mechanisms of schizophrenia and contribute to the design of individualised therapeutic approaches. The paper represents the capability of machine learning to comprehend the relationship between gene expression patterns and complicated illnesses like schizophrenia.

关键词： Water Training Accuracy Phenotypes Mental disorders Predictive models data models

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Understanding the Bad Development Practices of Android Custom Permissions in the Wild

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiaohan Yu, Zhiyuan Li, Xinghua Zhang, Cen Sun, Cong Zhang, Ning Deng, Robert H. Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Ministry of Education Engineering Research Center of Big data Security China Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States Nanyang Technological University 639798 Singapore Singapore Management University 188065 Singapore

Android system provides application developers with the ability to define custom permissions, which serve to regulate the sharing of resources and functionalities with other applications. However, developers' improper development practices can render the permission mechanism ineffective, facilitating easy exploitation by attackers. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, and even component hijacking. To automatically identify these patterns of bad practices, we devised PERMEAGRE, a static analysis tool. Employing PERMEAGRE, we performed a large-scale analysis of 83,085 applications obtained from seven major app markets, aiming to detect instances of BPCP issues. The results revealed that more than 26% of the analyzed apps contained at least one issue, and a significant number of apps had garnered millions of downloads. Our analysis delved into the underlying causes of these issues. Consequently, this analysis sheds light on the potential threat landscape associated with bad practices in custom permissions, emphasizing the urgent requirement for effective mitigation strategies. © 2004-2012 IEEE.

关键词： Android custom permission empirical study static analysis

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Scribble-supervised semantic segmentation with prototype-based feature augmentation 24

Scribble-supervised semantic segmentation with prototype-bas...

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Proceedings of the 41st International Conference on Machine Learning

作者： Guiyang Chan Pengcheng Zhang Hai Dong Shunhui Ji Bainian Chen Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China and College of Computer Science and Software Engineering Hohai University Nanjing China School of Computing Technologies RMIT University Melbourne Australia

Scribble-supervised semantic segmentation presents a cost-effective training method that utilizes annotations generated through scribbling. It is valued in attaining high performance while minimizing annotation costs, which has made it highly regarded among researchers. Scribble supervision propagates information from labeled pixels to the surrounding unlabeled pixels, enabling semantic segmentation for the entire image. However, existing methods often ignore the features of classified pixels during feature propagation. To address these limitations, this paper proposes a prototype-based feature augmentation method that leverages feature prototypes to augment scribble supervision. Experimental results demonstrate that our approach achieves state-of-the-art performance on the PASCAL VOC 2012 dataset in scribble-supervised semantic segmentation tasks. The code is available at https://***/TranquilChan/PFA.

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PM2.5 Forecasting Model based on Linear and Non-linear Hybrid Algorithm

PM2.5 Forecasting Model based on Linear and Non-linear Hybri...

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International Conference on Knowledge and Smart Technology (KST)

作者： Anupong Banjongkan Nittaya Kerdprasop Anusara Hirunyawanakul Kittisak Kerdprasop School of Computer Engineering Suranaree University of Technology Thailand Data Science and Computation Faculty of Science Energy and Environment King Mongkut’s University of Technology North Bangkok Rayong Campus Thailand

Air pollution is one of the harmful problems that the world has focused on and needs to be solved urgently because air pollution has a direct impact on humans leading to premature death caused by various diseases such as asthma inflammatory respiratory disease, lung cancer, and so on. The air pollutants, especially tiny particulate matter (PM), are currently receiving attention because they are a major problem in many large and populated cities around the world. This paper proposed a time-series model for forecasting PM 2.5 in advance through a machine learning process with a linear and non-linear hybrid algorithm. A hybrid algorithm that brings together the capabilities of autoregressive integrated moving average (ARIMA) and the adaptive-neuro fuzzy inference system (ANFIS) is used to find the linear and nonlinear correlation of the PM 2.5 time-series data. The proposed model is called ARIMA-FIS which uses the gradient descent (GD) method in the learning process. The dataset used in this research is the daily recorded of PM 2.5 values in Rayong province, which is the industrial city in Thailand. The results showed that the ARIMA-FIS model had the best performance in forecasting PM 2.5 in advance with the least error at 3.46 of mean absolute error (MAE) and 5.11 of root mean square error (RMSE). The proposed model gave the percentage of RMSE almost 3% better than the other standard time-series models.

关键词： Adaptation models Machine learning algorithms Urban areas Machine learning Predictive models Air pollution Inference algorithms

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TSC: A Simple Two-Sided Constraint against Over-Smoothing

arXiv

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arXiv 2024年

作者： Peng, Furong Liu, Kang Lu, Xuan Qian, Yuhua Yan, Hongren Ma, Chao Institute of Big Data Science and Industry School of Computer and Information Technology Shanxi University Shanxi Taiyuan China College of Physics and Electronic Engineering Shanxi University Shanxi Taiyuan China HOPERUN Information Technology Jiangsu Nanjing China

Graph Convolutional Neural Network (GCN), a widely adopted method for analyzing relational data, enhances node discriminability through the aggregation of neighboring information. Usually, stacking multiple layers can improve the performance of GCN by leveraging information from high-order neighbors. However, the increase of the network depth will induce the over-smoothing problem, which can be attributed to the quality and quantity of neighbors changing: (a) neighbor quality, node’s neighbors become overlapping in high order, leading to aggregated information becoming indistinguishable, (b) neighbor quantity, the exponentially growing aggregated neighbors submerges the node’s initial feature by recursively aggregating operations. Current solutions mainly focus on one of the above causes and seldom consider both at once. Aiming at tackling both causes of over-smoothing in one shot, we introduce a simple Two-Sided Constraint (TSC) for GCNs, comprising two straightforward yet potent techniques: random masking and contrastive constraint. The random masking acts on the representation matrix’s columns to regulate the degree of information aggregation from neighbors, thus preventing the convergence of node representations. Meanwhile, the contrastive constraint, applied to the representation matrix’s rows, enhances the discriminability of the nodes. Designed as a plug-in module, TSC can be easily coupled with GCN or SGC architectures. Experimental analyses on diverse real-world graph datasets verify that our approach markedly reduces the convergence of node’s representation and the performance degradation in deeper GCN. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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