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Keyword extraction method based on complex network

International Journal of Information and Communication Technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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User story clustering in agile development:a framework and an empirical study

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Frontiers of computer science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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Identification of software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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RLSM: A Strategy Matrix based Microservice Splitting Method for Reinforcement Learning

RLSM: A Strategy Matrix based Microservice Splitting Method ...

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2024 International Conference on Intelligent Systems and Robotics, CISR 2024

作者： Liu, Yuqiang Yu, Jianyong Han, Xue Liu, Yuqi The School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China The School of Hunan Software Vocational and Technical University Xiangtan China

We introduce a novel microservice splitting approach, the Policy Matrix-based Reinforcement Learning Splitting Method (RLSM), designed to overcome the limitations of traditional service splitting schemes by providing a solution that is both fine-grained and efficient, with a strong emphasis on automation. This method utilizes dynamic link tracking and static code analysis techniques to analyze business modules, and extract entities using data flow graphs. We use these entities as agents in reinforcement learning to construct a reinforcement learning environment model, and optimize and update the value of entities by constructing a policy matrix, replacing the Q table in traditional Q-learning algorithms. Finally, we obtained a set of entity categories and used the K-means algorithm to cluster the entities of these categories, with each clustered cluster being a split individual microservice. The experimental results show that this splitting scheme not only achieves automated splitting, but also maintains a stable compliance rate of about 97% for microservices. This is enough to demonstrate that RLSM is more efficient and flexible in completing service design and evaluation. © Published under licence by IOP Publishing Ltd.

关键词： Data flow graphs

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APT Detector: Detect and Identify APT Malware Based on Deep Learning Framework 23

APT Detector: Detect and Identify APT Malware Based on Deep ...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Tang, Binhui Yang, Jialin Li, Xin Cao, Yang Wang, Jie School of Computer and software Chengdu Jincheng College School of Cyber Science and Engineering Sichuan University China School of Computer and software Chengdu Jincheng College China

ISBN: (纸本)9781450399029

Advanced persistent threat (APT) attacks use sophisticated attack techniques and covert command and control (C&C) channels to conduct long-term sustained cyber attacks on specific targets as unobtrusively as possible. Over the past ten years, APT attacks have become increasingly frequent, gradually taking on a highly organized, nationalized, and militarized nature. Because APT malware is highly covert, has a long latency period, and presents characteristics such as diversification, high frequency, and sophistication. In the face of existing detection methods, it is easy for attackers to bypass detection by customizing malware and adapting and configuring tools to the target network. The continuous improvement of network technologies and tools and the complex and diverse attack process make it very difficult to detect unknown APT malware. To address the problems, this paper takes APT malware as the detection object and collects 6777 malware from 20 types of APT attack actors. Through the improved Convolutional Neural network (CNN) model combined with an attention mechanism to complete automatic extraction and weighting of static malware features, which can detect and discover APT malware. Meanwhile, the recognition effect for APT malware is better than other existing methods, which realize the attribution of APT attack groups. © 2023 Copyright held by the owner/author(s).

关键词： Malware

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A Self-Distillation Assisted ResNet-KL Image Classification Network

A Self-Distillation Assisted ResNet-KL Image Classification ...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Wang, Yuanyuan Tian, Haiyang Yan, Shaofeng Zhu, Junxun Song, Zhaoyu Shen, Yu Huaiyin Institute of Technolog School of Computer Science and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

Traditional ResNet models suffer from large model size and high computational complexity. In this study, we propose a self-distillation assisted ResNet-KL image classification method to address the low accuracy and efficiency issues in image classification ***,we introduce depthwise separable convolutions to the ResNet network and enhance the model's classification performance by improving the design of activation functions, using TReLU instead of traditional ReLU. Secondly,we enhance the model's perception of features at different scales by incorporating multi-scale convolutions for the fusion of residual layers and attention mechanism layers. To reduce the model's parameter count, we combine feature distillation with logic distillation and optimize the model layer by layer through selfdistillation, while applying pruning techniques multiple times to reduce its size. Finally, To assess the efficacy of our methodology, we conduct experimental evaluations on public datasets CIFAR-10, CIFAR-100, and STL-10. The results show that the improved ResNet-KL network achieves an accuracy improvement of 1.65%, 2.72%, and 0.36% compared to traditional ResNet models on these datasets, respectively. Our method obtains better classification performance with the same computational resources, making it promising for applications in tasks such as object classification. © 2023 IEEE.

关键词： Distillation

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Temperature-Constrained Computation Time Minimization for Integrated Tasks in UAV-Assisted MEC Networks 24

Temperature-Constrained Computation Time Minimization for In...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Hao, Yuyu Bao, Lingyan Li, Hai Zhao, Mingxiong Macau University of Science and Technology School of Computer Science and Engineering Macau China Yunnan University National Pilot School of Software Kunming China

ISBN: (纸本)9798350363760

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) leverages UAVs' mobility and cost-effectiveness to handle computation-intensive tasks at the network edge. However, limited energy supply and hardware costs can cause high CPU temperatures due to excessive heat dissipation when processing demanding tasks. To address this, we study a UAV-assisted MEC network where the UAV provides computing services during flight, aiming to minimize users' average computation time under CPU temperature constraints. We jointly optimize the UAV's trajectory, resource allocation, and task scheduling, breaking the problem into four subproblems solved iteratively. Simulations confirm that the proposed algorithm meets thermal constraints while reducing average computation time. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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M-GhostNet: A Lightweight CNN Model Combined with Coordinate Attention Mechanism for Identifying Pests and Diseases 12

M-GhostNet: A Lightweight CNN Model Combined with Coordinate...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Ma, Chi Xiao, Yu Hu, Hui Li, Jingyan Wu, Jie The School of Computer Science and Engineering Huizhou University Huizhou516007 China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9798400707988

Plant pests and diseases have always been an important factor affecting agricultural production, and how to automatically identify them is one of the current research hotspots. Although traditional deep learning models can achieve good results, they are difficult to deploy into small devices due to their large number of parameters. In this paper, we design a lightweight convolutional neural network M-GhostNet based on the coordinate attention mechanism, which improves the bottleneck structure in the original GhostNet and and effectively extracts the spatial information of the image. We compare and ablate the M-GhostNet model on the IP102 dataset and the Embrapa dataset. Compared with the existing lightweight neural networks, M-GhostNet achieves the best results of 67.15%, 65.04%, and 71.41% on the IP102 dataset, respectively, and 92.67%, 95.65%, 93.74%, and 96.52% on the Embrapa dataset in terms of Mrec, Mpre, MF1, and Acc. The final results show that M-GhostNet can achieve good performance in both pest and disease identification. © 2023 ACM.

关键词： Convolutional neural networks

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Robust copy-move detection and localization of digital audio based CFCC feature

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Multimedia Tools and Applications 2025年第11期84卷 9573-9589页

作者： Wang, Dongyu Li, Xiaojie Shi, Canghong Niu, Xianhua Xiong, Ling Wu, Hanzhou Qian, Qing Qi, Chao School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer Science Chengdu University of Information Technology Chengdu610225 China School of Information Shanghai University Shanghai200444 China School of Information Guizhou University of Finance and Economics Guiyang550000 China

Copy-move forgery is a common audio tampering technique in which users copy the contents of one speech and paste them into another region of the same speech signal, thus achieving the effect of tampering with the semantics. To verify the authenticity of the audio, this paper proposes a method to detect and localize audio copy-move forgery based on the cochlear filter of cochlear filter cepstral coefficients (CFCC) feature. The pitch tracking algorithm is used to distinguish the voiced and unvoiced segments in the audio, and then the CFCC coefficients are extracted for each voiced segment. The CFCC feature simulates the entire transmission process of signals in the cochlear basilar membrane using wavelet transformation. Finally, we use Pearson correlation coefficients (PCCs) and dynamic time warping (DTW) in combination to compare the similarity of voiced segments, accurately determining the tampered locations in the audio through threshold judgment. Through extensive experiments on relevant datasets, this algorithm achieves a precision rate of 98.39% and a recall rate of 98.00% in detecting audio without post-processing. Even when detecting audio that has undergone different post-processing, the precision and recall rates remain above 90%. Compared to existing methods, this approach not only achieves precise localization of replicated segments but also demonstrates superior experimental results. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization 31

SparseCoder: Identifier-Aware Sparse Transformer for File-Le...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350330663

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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