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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Peter Atandoh Zhang Fengli Paul Atandoh Hakeem Edward Mensah Acheampong Daniel Addo Michael Appiah-Twum Daniel Adu-Gyamfi Richard Safo Blankson Tohye Tewodros Giza Wi School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Mathematics Mercer University Macon GA USA School of Resources and Environment University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology C. K. Tedam University of Technology and Applied Sciences Navrongo UE/R Ghana Summit Economics LLC Colorado USA

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7 % , 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE.

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Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

arXiv

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arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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Covert Communication in Underlay Multi-Antenna Cognitive Radio Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1493-1507页

作者： Fan, Hongbin Yang, Weiwei Shi, Hui Pan, Gaofeng Si, Jiangbo Wang, Yutao Rong, Chuanzhen Army Engineering University of PLA College of Communications Engineering Nanjing210007 China Xiangnan University College of Computer and Artificial Intelligence Chenzhou423000 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710100 China

In this paper, we investigated a multi-antenna covert communication in underlay cognitive radio networks (UCRNs), by considering both cases that Willie has the perfect and statistical channel state information (CSI) of the link from the secondary transmitter (ST) to the primary receiver (PR), respectively. Specifically, the optimal antenna of the secondary receiver (SR) is selected to receive the private information from ST, and then one of the remaining antennas is randomly selected to transmit jamming signals. The closed-form expressions of Willie's detection error probability, the average minimum detection error probability, the transmission outage probability and the maximum effective covert rate under both cases are derived. The maximum effective covert rate is obtained by optimising the maximum jamming power of SR. The numerical results show that the combined effect of the maximum interference tolerance of PR and the maximum interference power of SR provides the positive effective covert rate between ST and SR. It is further found that the maximum effective covert rate between ST and SR when Willie knows the perfect CSI of the ST-SR link is greater than that when Willie only knows the statistical CSI of the ST-SR link. © 2015 IEEE.

关键词： Jamming

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NLocalSAT: Boosting local search with solution prediction 29

NLocalSAT: Boosting local search with solution prediction

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Wenjie Sun, Zeyu Zhu, Qihao Li, Ge Cai, Shaowei Xiong, Yingfei Zhang, Lu MoE Software Institute Peking University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Computational complexity

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Design and Development of Android Application for Educational Institutes 5

Design and Development of Android Application for Educationa...

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5th International Conference on computer science and Information Engineering, ICCSIE 2020

作者： Ejiyi, Chukwuebuka Joseph Deng, Jianhua Ejiyi, Thomas Ugochukwu Salako, Adetunji A. Ejiyi, Makuachukwu B. Anomihe, Chinonso G. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu State Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Agricultural Extensions University of Nigeria Nsukka Enugu State Nigeria Department of Biochemistry Federal University of Technology Owerri Imo State Nigeria

The growth in the use android applications (Apps) has made it the most popular smart device operating system in use nowadays. Android has over 76% of the mobile operating system from December 2018-January 2020 which is quite significant. Android phones are also becoming the most used electronics globally. Students of higher institutions of learning are also becoming accustomed to the use of applications such that they want everything available for them on their mobile device if possible. This paper deals on an android application that will aid students in planning their timetable and scheduling their classes as well as having full knowledge of days according to the school calendar, get access to academic resources and information about the school right on their smart devices. From the application, one can have access to the detailed and accurate information of the school. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Students

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Temporal-Gated Graph Neural Network with Graph Sampling for Multi-step Attack Detection

Temporal-Gated Graph Neural Network with Graph Sampling for ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Shuyu Chen Dawei Lin Zhenping Xie Hongbo Wang School of Artificial Intelligence and Computer Science Jiangnan University WuXi China Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University Wuxi China Inspur Software Co. Ltd. Taian China TRS Topwalk Information Techololgy Co. Ltd. Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The emergence of new network attacks, especially multi-step attacks which exhibit complex patterns, presents challenges to network security. Intrusion detection in network traffic is one of the core means to ensure network operation security. Traditional intrusion detection methods identify abnormal traffic by modeling the patterns of normal traffic. This strategy focuses on the spatial characteristics of data, ignoring the temporal characteristics of traffic evolution, and may not fully capture the patterns of multi-step network attacks. To address this problem, we propose a Temporal-Gated Graph Neural Network (TGGNN) framework based on graph clustering sampling. Firstly, each network traffic data is viewed as a graph node. We use hierarchical graph clustering methods to sample representative points and further construct local evolutionary structures with their temporally adjacent nodes. Then, we design a novel way of constructing graph data that balances both local and global aspects of network traffic data. Based on the GGNN (Gated Graph Neural Network), we designed a Temporal-Gated Mechanism in the information propagation part, emphasizing the learning of the local evolutionary structure of the graph. Furthermore, a bidirectional LSTM network is used to further enhance the learning of dynamic patterns in network traffic data. Experimental results based on the UNSW-NB15 dataset demonstrate that our approach not only surpasses the performance of four recent baselines but also eliminates the need for feature engineering by adopting an end-to-end approach.

关键词： Privacy Computational modeling Intrusion detection Telecommunication traffic Network security Logic gates Graph neural networks

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From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining

From Misuse to Mastery: Enhancing Code Generation with Knowl...

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IEEE International Conference on Automated software Engineering (ASE)

作者： Xiaoxue Ren Xinyuan Ye Dehai Zhao Zhenchang Xing Xiaohu Yang School of Software Technology Zhejiang University Hangzhou China School of Computing Australian National University Canberra Australia CSIRO's Data61 Sydney Australia CSIRO's Data61 & Australian National University Sydney Australia College of Computer Science and Technology Zhejiang University Hangzhou China

Large Language Models (LLMs) have shown promising results in automatic code generation by improving coding efficiency to a certain extent. However, generating high-quality and reliable code remains a formidable task because of LLMs' lack of good programming practice, especially in exception handling. In this paper, we first conduct an empirical study and summarize three crucial challenges of LLMs in exception handling, i.e., incomplete exception handling, incorrect exception handling and abuse of try-catch. We then try prompts with different granularities to address such challenges, finding fine-grained knowledge-driven prompts works best. Based on our empirical study, we propose a novel Knowledge-driven Prompt Chaining-based code generation approach, name KPC, which decomposes code generation into an AI chain with iterative check-rewrite steps and chains fine-grained knowledge-driven prompts to assist LLMs in considering exception-handling specifications. We evaluate our KPC-based approach with 3,079 code generation tasks extracted from the Java official API documentation. Extensive experimental results demonstrate that the KPC-based approach has considerable potential to ameliorate the quality of code generated by LLMs. It achieves this through proficiently managing exceptions and obtaining remarkable enhancements of 109.86% and 578.57% with static evaluation methods, as well as a reduction of 18 runtime bugs in the sampled dataset with dynamic validation.

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From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining

arXiv

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arXiv 2023年

作者： Ren, Xiaoxue Ye, Xinyuan Zhao, Dehai Xing, Zhenchang Yang, Xiaohu School of Software Technology Zhejiang University Hangzhou China School of Computing Australian National University Canberra Australia CSIRO’s Data61 Sydney Australia CSIRO’s Data61 Australian National University Sydney Australia College of Computer Science and Technology Zhejiang University Hangzhou China

Large Language Models (LLMs) have shown promising results in automatic code generation by improving coding efficiency to a certain extent. However, generating high-quality and reliable code remains a formidable task because of LLMs’ lack of good programming practice, especially in exception handling. In this paper, we first conduct an empirical study and summarize three crucial challenges of LLMs in exception handling, i.e., incomplete exception handling, incorrect exception handling and abuse of try-catch. We then try prompts with different granularities to address such challenges, finding fine-grained knowledge-driven prompts works best. Based on our empirical study, we propose a novel Knowledge-driven Prompt Chaining-based code generation approach, name KPC, which decomposes code generation into an AI chain with iterative check-rewrite steps and chains fine-grained knowledge-driven prompts to assist LLMs in considering exception-handling specifications. We evaluate our KPC-based approach with 3,079 code generation tasks extracted from the Java official API documentation. Extensive experimental results demonstrate that the KPC-based approach has considerable potential to ameliorate the quality of code generated by LLMs. It achieves this through proficiently managing exceptions and obtaining remarkable enhancements of 109.86% and 578.57% with static evaluation methods, as well as a reduction of 18 runtime bugs in the sampled dataset with dynamic validation. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Heterogeneous Graph Contrastive Multi-view Learning

arXiv

引用

arXiv 2022年

作者： Wang, Zehong Li, Qi Yu, Donghua Han, Xiaolong Gao, Xiao-Zhi Shen, Shigen School of Mathematics University of Leeds Leeds United Kingdom Department of Computer Science and Engineering Shaoxing University Shaoxing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Computing University of Eastern Finland Kuopio Finland School of Information Engineering Huzhou University Huzhou China

Inspired by the success of Contrastive Learning (CL) in computer vision and natural language processing, Graph Contrastive Learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant stage. For example, it is unclear how to augment the HINs without substantially altering the underlying semantics, and how to design the contrastive objective to fully capture the rich semantics. Moreover, early investigations demonstrate that CL suffers from sampling bias, whereas conventional debiasing techniques are empirically shown to be inadequate for GCL. How to mitigate the sampling bias for heterogeneous GCL is another important problem. To address the aforementioned challenges, we propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In particular, we use metapaths as the augmentation to generate multiple subgraphs as multi-views, and propose a contrastive objective to maximize the mutual information between any pairs of metapath-induced views. To alleviate the sampling bias, we further propose a positive sampling strategy to explicitly select positives for each node via jointly considering semantic and structural information preserved on each metapath view. Extensive experiments demonstrate HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets. To enhance the reproducibility of our work, we make all the code publicly available at https://***/Zehong-Wang/HGCML. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Structure Enhanced Prototypical Alignment for Unsupervised Cross-Domain Node Classification

SSRN

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SSRN 2023年

作者： Liu, Meihan Zhang, Zhen Ma, Ning Gu, Ming Wang, Haishuai Zhou, Sheng Bu, Jiajun College of Computer Science Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University Hangzhou310027 China School of Software Technology Zhejiang University Ningbo315048 China School of Computing National University of Singapore 117417 Singapore

Graph Neural Networks (GNNs) have demonstrated remarkable success in graph node classification task. However, their performance heavily relies on the availability of high-quality labeled data, which can be time-consuming and labor-intensive to acquire for graph-structured data. Therefore, the task of transferring knowledge from a label-rich graph (source domain) to a completely unlabeled graph (target domain) becomes crucial. In this paper, we propose a novel unsupervised graph domain adaptation framework called Structure Enhanced Prototypical Alignment (SEPA), which aims to learn domain-invariant representations on non-IID (non-independent and identically distributed) data. Specifically, SEPA captures class-wise semantics by constructing a prototype-based graph and introduces an explicit domain discrepancy metric to align the source and target domains. The proposed SPEA framework is optimized in an end-to-end manner, which could be incorporated into various GNN architectures. Experimental results on several real-world datasets demonstrate that our proposed framework outperforms recent state-of-the-art baselines with different gains. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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