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Parallel software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

Journal of Computer Science & technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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ALBIG: a Chinese medical text entity recognition model based on Global Pointer 3

ALBIG: a Chinese medical text entity recognition model based...

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3rd International Conference on Electronic information engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Zhang, Yijie Gao, Yan Zeng, Qiong School of Software Engineering Chengdu University of Information Technology Chengdu610225 China Sichuan Prov. Eng. Technology Research Center of Support Software of Informatization Application Chengdu610225 China

ISBN: (纸本)9781510680449

Current work on Chinese medical name entity recognition focuses on extracting flattened entities, and when there are nested entities in the entities, the entity recognition is prone to errors and cannot accurately identify all the entities in the statement. In this paper, we construct an ALBIG model based on multilayer neural network and adversarial learning method. The model uses a pre-trained model RoBERTa-wwm stablish word embedding and connects IDCNN model to extract text features. Adds adversarial learning method to increase the robustness of the model and uses Global Pointer method based on Rotary Position Embedding as the output layer to obtain the final result. On the CMeEE dataset, the F1 values of the ALBIG model obtained higher scores compared to other experimental models, proving the validity of the model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Embeddings

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Knowledge Graph and Knowledge Reasoning:A Systematic Review

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Journal of Electronic Science and technology 2022年第2期20卷 159-186页

作者： Ling Tian Xue Zhou Yan-Ping Wu Wang-Tao Zhou Jin-Hao Zhang Tian-Shu Zhang Kashi Institute of Electronics and Information Industry Kashi 844099 Shenzhen Institute of Information Technology Shenzhen 518172 the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

关键词： methods. representation reasoning

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MEDICINE RECOMMENDER SYSTEM BASED ON SEMANTIC AND MULTI-CRITERIA FILTERING

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Interdisciplinary Journal of information, Knowledge, and Management 2023年 18卷 435-457页

作者： Shambour, Qusai Yousef Al-Zyoud, Mahran Abu-Shareha, Ahmad Adel Abualhaj, Mosleh Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Networks and Cybersecurity Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan

Aim/Purpose: This study aims to devise a personalized solution for online healthcare platforms that can alleviate problems arising from information overload and data sparsity by providing personalized healthcare services to patients. The primary focus of this paper is to develop an effective medicine recommendation approach for recommending suitable medications to patients based on their specific medical conditions. Background: With a growing number of people becoming more conscious about their health, there has been a notable increase in the use of online healthcare platforms and e-services as a means of diagnosis. As the internet continues to evolve, these platforms and e-services are expected to play an even more significant role in the future of healthcare. For instance, WebMD and similar platforms offer valuable tools and information to help manage patients' health, such as searching for medicines based on their medical conditions. Nonetheless, patients often find it arduous and time-consuming to sort through all the available medications to find the ones that match their specific medical conditions. To address this problem, personalized recommender systems have emerged as a practical solution for mitigating the burden of information overload and data sparsity-related issues that are frequently encountered on online healthcare platforms. Methodology: The study utilized a dataset of MC ratings obtained from WebMD, a popular healthcare website. Patients on this website can rate medications based on three criteria, including medication effectiveness, ease of use, and satisfaction, using a scale of 1 to 5. The WebMD MC rating dataset used in this study contains a total of 32,054 ratings provided by 2,136 patients for 845 different medicines. The proposed HSMCCF approach consists of two primary modules: a semantic filtering module and a multi-criteria filtering module. The semantic filtering module is designed to address the issues of data sparsity and new item prob

关键词： Diagnosis

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Service Function Chain Deployment Algorithm Based on Multi-Agent Deep Reinforcement Learning

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Computers, Materials & Continua 2024年第9期80卷 4875-4893页

作者： Wanwei Huang Qiancheng Zhang Tao Liu YaoliXu Dalei Zhang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Henan Jiuyu Tenglong Information engineering Co. Ltd.Zhengzhou450052China Henan Xin’an Communication Technology Co. Ltd.Zhengzhou450007China

Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(SFC)under 5G networks,this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment(MADDPG-SD).Initially,an optimization model is devised to enhance the request acceptance rate,minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained ***,we model the dynamic problem as a Markov decision process(MDP),facilitating adaptation to the evolving states of network ***,by allocating SFCs to different agents and adopting a collaborative deployment strategy,each agent aims to maximize the request acceptance rate or minimize latency and *** agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection,and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed *** simulation results indicate that the proposed method,while simultaneously meeting performance requirements and resource capacity constraints,has effectively increased the acceptance rate of requests compared to the comparative algorithms,reducing the end-to-end latency by 4.942%and the deployment cost by 8.045%.

关键词： Network function virtualization service function chain Markov decision process multi-agent reinforcement learning

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An Air Quality Oriented Convolutional and Interaction Network for Traffic Flow Prediction in VANET 16

An Air Quality Oriented Convolutional and Interaction Networ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Cheng, Hanlin Li, Xuan Zhou, Tianqing Yao, Yu Wang, Kan Zhou, Momiao School of Information and software Engineering East China Jiaotong University Nanchang China School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Engineering Xi'an University of Technology Xi'an China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350390643

To accurately predict traffic flow, it is necessary to take into account not only the volume of road traffic, but also external urban factors. Particularly, the correlations between urban air quality and traffic flow is particularly strong, However, due to the complexity of air composition and its nonlinear characteristics in spatial and temporal sequences, it remains underexplored in current research efforts. In this paper, we propose an air quality oriented convolutional and interaction network for traffic flow prediction in Vehicular ad-hoc network, AQCInet, which captures feature information from both types of data using convolutional block, integrates this feature information with interaction networks, and effectively represents the long-term and short-term dependencies in the sequential data, ulti-mately outputting short-term traffic flow prediction results. The experimental results show that AQCInet can effectively model traffic flow sequences with complex time dynamics by aggregating rich features from multiple resolutions. Compared with existing model, our proposed model significantly improves RMSE, MSE, and MAE through jointly considerations of the relationship between air quality, various air component concentrations, and traffic flow. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of information technology (Singapore) 2024年 1-18页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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A Vulnerability Accessibility Analysis Framework for software Supply Chain 10

A Vulnerability Accessibility Analysis Framework for Softwar...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Xiao, Lei Gao, Zhenyu Xu, Qizhen Chang, Le Zhu, Shunzhi Xiamen University of Technology College of Computer and Information Engineering Xiamen361024 China Software Security Company Chengdu610041 China

ISBN: (纸本)9798350362930

The security issues brought by third-party components in software are becoming increasingly prominent. However, current component analysis tools only analyze the existence of the vulnerabilities introduced by components, resulting in much false positives. In this paper, we present a vulnerability accessibility analysis framework to analyze whether these vulnerabilities would be actually called by the project. © 2024 IEEE.

关键词： Supply chains

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of information technology (Singapore) 2024年 1-21页

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Joint Spatial Similarity-based Attention for Retina Vessel Segmentation with Super Resolution Encoding 4

Joint Spatial Similarity-based Attention for Retina Vessel S...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Ekong, Favour Yu, Yongbin Patamia, Rutherford Agbeshi Sarpong, Kwabena Ukwuoma, Chiagoziem C. Cai, Jingye University of Electronic Science and Technology of China Information and Software Engineering Chengdu China Chengdu University of Technology Nuclear Technology and Automation Engineering Chengdu China

ISBN: (纸本)9798350324303

Machine learning methods based on fully convolutional networks have emerged as a viable choice for retinal vessel segmentation. However, when input samples significantly deviate from the training data distribution, these deep networks fail silently, posing serious challenges for automatic data processing pipelines. In our study, we question the effectiveness of randomly selecting unlabeled training target images for annotation and inclusion in the support set, as it may not facilitate an efficient fine-Tuning process. We propose a novel method to optimize the image selection procedure to address this. By utilizing our method, we can identify informative unlabeled target images by comparing the consistency of model predictions on these images under specific data augmentations. However, it should be noted that the model developed using the source datasets cannot accurately evaluate consistency on the target images. To overcome this issue, we suggest employing self-supervised pretext tasks to compute scores for unlabeled target images, resolving this limitation. The top few images with the lowest consistency scores can then be added to the support set and annotated by experts to fine-Tune the model for the target images. We introduce an upgraded encoder-decoder model which outperforms multi-scale prediction ensembling to address insufficient single-scale base learners. This paper proposes a novel module that uses joint spatial and channel attention and an auxiliary prototype similarity operation to enhance border localization and super-resolution in the encoder-decoder architecture. To evaluate the performance of our approach, we conducted experiments on two distinct datasets, integrating our joint spatial/channel attention-based module with auxiliary prototype similarity operation for super-resolution encoding. Our empirical results demonstrate the superiority of our proposed model over state-of-The-Art attention mechanisms used in super-resolution. © 2023 IEEE.

关键词： Decoding

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