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PathActMarker:an R package for inferring pathway activity of complex diseases

Frontiers of computer science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Intelligent Segment Routing: Toward Load Balancing with Limited Control Overheads

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Big Data Mining and Analytics 2023年第1期6卷 55-71页

作者： Shu Yang Ruiyu Chen Laizhong Cui Xiaolei Chang College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518000China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518071China

Segment routing has been a novel architecture for traffic engineering in recent ***,segment routing brings control overheads,i.e.,additional packets headers should be *** overheads can greatly reduce the forwarding efficiency for a large network,when segment headers become too *** achieve the best of two targets,we propose the intelligent routing scheme for traffic engineering(IRTE),which can achieve load balancing with limited control *** achieve optimal performance,we first formulate the problem as a mapping problem that maps different flows to key diversion ***,we prove the problem is nondeterministic polynomial(NP)-hard by reducing it to a k-dense subgraph *** solve this problem,we develop an ant colony optimization algorithm as improved ant colony optimization(IACO),which is widely used in network optimization *** also design the load balancing algorithm with diversion routing(LBA-DR),and analyze its theoretical ***,we evaluate the IRTE in different real-world topologies,and the results show that the IRTE outperforms traditional algorithms,e.g.,the maximum bandwidth is 24.6% lower than that of traditional algorithms when evaluating on BellCanada topology.

关键词： traffic engineering segment routing bandwidth load balancing ant colony optimization

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Keyword extraction method based on complex network

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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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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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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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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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