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GP‐FMLNet:A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 960-972页

作者： Jing Li Dezheng Zhang Yonghong Xie Aziguli Wulamu Yao Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology BeijingBeijingChina University of Alberta EdmontonAlbertaCanada

Sentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified *** methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus cannot fully exploit and utilise sentiment feature information,making their performance less than *** resolve the problem,the authors propose a new method,GP‐FMLNet,that integrates both glyph and phonetic information and design a novel feature matrix learning process for phonetic features with which to model words that have the same pinyin information but different glyph *** method solves the problem of misspelling words influencing sentiment polarity prediction ***,the authors iteratively mine character,glyph,and pinyin features from the input comments ***,the authors use soft attention and matrix compound modules to model the phonetic features,which empowers their model to keep on zeroing in on the dynamic‐setting words in various positions and to dispense with the impacts of the deceptive‐setting ***-periments on six public datasets prove that the proposed model fully utilises the glyph and phonetic information and improves on the performance of existing Chinese senti-ment analysis algorithms.

关键词： aspect‐level sentiment analysis deep learning feature extraction glyph and phonetic feature matrix compound learning

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Adoption of electronic payment platforms by fast moving consumer goods (FMCG) businesses in Ghana: PLS-SEM and FSQCA approaches

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SN Business and Economics 2025年第5期5卷 1-28页

作者： Appiah, Thomas Assefuah, Blessed Agblewornu, Veronica Venyo Forson, Nelly Mac-Arhin Donkoh, Grace Department of Accounting Banking and Finance Ghana Communication Technology University Accra Ghana Ghana Communication Technology University Accra Ghana Department of Computer Science Ghana Communication Technology University Accra Ghana

Over the past decade, the antecedents of electronic payment (e-payment) adoption have been studied extensively in the extant literature. Nonetheless, these studies have often investigated how individual antecedents affect adoption behaviour rather than exploring how combinations of variables influence this process. To address this research gap, this study employs both symmetric and asymmetric techniques to analyse the isolated effects of variables and their synergistic interplay that leads to the desired outcome. Employing a research design that is cross-sectional in nature, the study compiled and analysed data from 241 fast moving consumer goods (FMCG) business owners and managers in the Sekondi-Takoradi municipality of Ghana, utilizing partial least squares-structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (FSQCA) techniques. The findings indicate that effort expectancy, social influence, and facilitation conditions are the key drivers influencing adoption of e-payment systems by FMCG businesses in Ghana. However, electronic transaction levy (e-levy) weakens the link between the intention to adopt e-payment and actual adoption. The FSQCA results indicate that e-payment adoption by FMCG businesses can be explained through three configurational paths, suggesting the existence of multiple equifinal pathways to high e-payment adoption intentions. These findings may assist policymakers and Fintech companies in enhancing the adoption of e-payment platforms among FMCG businesses in Ghana. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adoption E-payment Fast moving consumer goods FSQCA PLS-SEM

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Greenshields Model-Based Fuzzy System for Predicting Traffic Congestion on Highways

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IEEE Access 2024年 12卷 115868-115882页

作者： Hao, Miin-Jong Hsieh, Bo-Yi National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Yanchao Kaohsiung824005 Taiwan

Highways serve as vital connectors between cities, yet they often suffer from traffic congestion as the population continues to grow. Various intelligent frameworks or models for traffic status prediction have been employed in the Intelligent Transport System (ITS) to provide services for convenient and safe traveling, effective traffic management, and smart signal control. Most of these frameworks typically involve learning processes that utilize learning algorithms and requires training data. For highway traffic, the Greenshields model offers a practical relationship among vehicle speeds, traffic flows, and traffic density, which can serve as fundamental knowledge for developing intelligent traffic management systems. This paper proposes a fuzzy logic system based on the Greenshields model as the knowledge base for quickly predicting highway traffic congestion without extensive preparing data. Our system operates in two modes: jam and non-jam modes. In each model, the two inputs of vehicle speed and traffic flow are processed respectively with specified membership functions for effective fuzzification. The set of rules and conditions guided by the Greenshields theory is governed by the inference mechanism, which makes decisions according to the input field. Subsequently, the defuzzification process converts the fuzzy sets obtained by the inference engine into a congestion level as the output. To validate the accuracy of our system, a polynomial regression model utilizing realistic data from roadside equipment on the Sun Yat-Sen Highway in Taiwan is established for comparison. Comparing the observed data points from the polynomial regression model with the outputs obtained from our system using the same inputs, both predicting outputs are found to be consistent, affirming the practical feasibility of the proposed system. Moreover, our proposed scheme is adaptable to suit diverse road conditions without extensive training data and possesses a short memory to perform

关键词： Traffic congestion

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A review on spreading and Forensics Analysis of Windows-Based ransomware

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Annals of Data science 2024年第5期11卷 1503-1524页

作者： Chayal, Narendrakumar Mangilal Saxena, Ankur Khan, Rijwan Department of Computer Science and Technology Faculty of Engineering and Technology Department of Computer Engineering Sankalchand Patel University Visnagar India Department of Computer Science Indus Institute of Information and Communication Technology Dean-ICT & ampDirector-IIICT Indus University Gujarat Ahmedabad India Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad Affiliated to AKTU Lucknow India

Ransomware is one of the most advanced malware which uses high computer resources and services to encrypt system data once it infects a system and causes large financial data losses to the organization and individuals. There are certain automatic ransomware detection and analysis strategies available nowadays. File system analysis reveals some essential patterns and artifacts that can be very useful to understand its behavior spreading mechanism, taxonomy for malware forensics experts. Current trend explores Ransomware as a service (RaaS) and Malware as a service (MaaS) on Darknet. This paper reveals a theory of digital forensic methodology to identify the spreading/infection mechanism and attack path, the cryptographic methodology, windows services, process, APIs, persistence mechanism, and system lockdown strategies and malware analysis methodology. This review could be helpful to learn and understand malware forensic analysis for threat researchers, students, cyber experts, etc. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Malware

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Data Secure Storage Mechanism for IIoT Based on Blockchain

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computers, Materials & Continua 2024年第3期78卷 4029-4048页

作者： Jin Wang Guoshu Huang R.Simon Sherratt Ding Huang Jia Ni School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Nanjing University of Information Science and TechnologyNanjing210044China

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of *** technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the *** the traditional blockchain,data is stored in a Merkle *** data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based ***,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of *** solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC ***,this paper uses PVC instead of the Merkle tree to store big data generated by *** can improve the efficiency of traditional VC in the process of commitment and ***,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of *** mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

关键词： Blockchain IIoT data storage cryptographic commitment

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Decomposition-based learning in drone-assisted wireless-powered mobile edge computing networks

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Digital communications and Networks 2024年第6期10卷 1769-1781页

作者： Xiaoyi Zhou Liang Huang Tong Ye Weiqiang Sun State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong UniversityShanghai 200240China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310058China

This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile *** aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all *** action space of the system is extremely large and grows exponentially with the number of *** this case,single-agent learning will require an overlarge neural network,resulting in insufficient ***,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for *** thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale *** the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this *** the second tier,UAVs utilize multiple agents to plan their *** agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint *** results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific *** particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal ***,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.

关键词： Mobile-edge computing Multi-agent reinforcement learning Offloading decision Trajectory planning Unmanned aerial vehicle Wireless power transfer

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of computer science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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AIO: Automating I/O Optimization Pipeline for Data-Intensive Applications in HPC 22

AIO: Automating I/O Optimization Pipeline for Data-Intensive...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Wanxin Wu, Huijun Yang, Lihua Liu, Zhangyu Zheng, Bin Changsha University of Science and Technology School of Computer and Communication Engineering China National University of Defense Technology College of Computer Science and Technology China

ISBN: (纸本)9798331509712

High-performance computing (HPC) systems has entered the exascale era, but I/O performance has lagged behind due to storage hardware limitations, creating a "storage wall effect"that hinders HPC systems full potential. Modern HPC storage systems employ a layered storage architecture with local node storage, burst buffers, and global parallel file systems to improve I/O performance. However, application workloads vary, and fast storage layers may not always outperform global parallel file systems, while a static storage software stack configuration may not achieve optimal performance across different applications. Traditional I/O optimization is time-consuming, laborintensive, and prone to human error. To address this issue, this paper proposes AIO, a data-driven I/O optimization method. AIO automatically selects the optimal storage hierarchy and software stack based on the task's I/O patterns, and further enhances performance by identifying and applying the most efficient configuration settings. We evaluated AIO with eight applications in a cluster that features shared burst buffers, local burst buffers, and a global shared file system. The experiments demonstrate that AIO effectively optimizes I/O performance, achieving an overall speedup of 2.74. © 2024 IEEE.

关键词： Buffer storage

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SiamS3C: spatial-channel cross-correlation for visual tracking with centerness-guided regression

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Multimedia Systems 2024年第5期30卷 1-14页

作者： Zhang, Jianming Chen, Wentao He, Yufan Kuang, Li-Dan Sangaiah, Arun Kumar School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410076 China International Graduate School of AI National Yunlin University of Science and Technology Douliu64002 Taiwan

Visual object tracking can be divided into the object classification and bounding-box regression tasks, but only one sharing correlation map leads to inaccuracy. Siamese trackers compute correlation map by cross-correlation operation with high computational cost, and this operation performed either on channels or in spatial domain results in weak perception of the global information. In addition, some Siamese trackers with a centerness branch ignore the associations between the centerness branch and the bounding-box regression branch. To alleviate these problems, we propose a visual object tracker based on Spatial-Channel Cross-Correlation and Centerness-Guided Regression. Firstly, we propose a spatial-channel cross-correlation module (SC3M) that combines the search region feature and the template feature both on channels and in spatial domain, which suppresses the interference of distractors. As a lightweight module, SC3M can compute dual independent correlation maps inputted to different subnetworks. Secondly, we propose a centerness-guided regression subnetwork consisting of the centerness branch and the bounding-box regression branch. The centerness guides the whole regression subnetwork to enhance the association of two branches and further suppress the low-quality predicted bounding boxes. Thirdly, we have conducted extensive experiments on five challenging benchmarks, including GOT-10k, VOT2018, TrackingNet, OTB100 and UAV123. The results show the excellent performance of our tracker and our tracker achieves real-time requirement at 48.52 fps. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object tracking

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Computation of Graph Fourier Transform Centrality Using Graph Filter

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of Circuits and Systems 2024年 5卷 69-80页

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung824005 Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan711301 Taiwan

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method;the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter. © 2020 IEEE.

关键词： Complex networks

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