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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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Urdu Sentiment Analysis Using Deep Learning 18

Urdu Sentiment Analysis Using Deep Learning

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Nasir, Imtisall Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Balochistan University of Information Technology Engineering and Management Science Department of Computer Science Quetta Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Computer Engineering Quetta Pakistan Pakistan Television Corporation Department of Engineering Lahore Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Software Engineering Quetta Pakistan

ISBN: (纸本)9798331508692

Sentiment Analysis is a deep text analysis to determine the expressions from the written text, The Urdu language holds significant importance due to the usage of these social media platforms and content generated at these platform in Urdu. Effective sentiment analysis in Urdu can provide valuable information about social trends, customer feedback, and public opinion on different sets of topics in the Urdu context. This study uses an deep learning (ANN) Artificial Neural Network to predict and classify the sentiments in the Urdu language, labeling them into positive, negative, and neutral sentiments. To achieve the research goals, advanced preprocessing techniques were utilized such as removing stop words, emojis, emails, and characters, removing symbols, performing lemmatization to expand the meaning of text, tokenized each word by using Lexicon-based approach and ngram. The dataset are tweets dependent and have 1,140,021 tweets. These steps ensure that data is standardized and cleaned to be split into training and testing. The datasets were converted into sequence and categorized to be input into the ANN model. The ANN model was trained and achieved an accuracy of 90%, precision of 98%, and f1-score of 97%. These high values indicate that the model accurately predicted the sentiments in the Urdu text. However, during experimentation limitations were discovered due to the complexities of Urdu text, difficult script patterns, and linguistic problems that were not solved by the preprocessing technique. During analysis, the limitation was discovered due to the limited number of preprocessing techniques and tools for Urdu text. However, the study highlights the potential of neural networks, especially ANN in Urdu sentiment analysis. This study contributed significantly to Urdu natural language processing by providing robust techniques and identifying limitations to work on it as a research problem. © 2024 IEEE.

关键词： Natural language processing systems

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Runtime Verification of Neural-Symbolic Systems 10th

Runtime Verification of Neural-Symbolic Systems

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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Deng, Shaojun Liu, Wanwei Zhang, Miaomiao College of Computer Science and Technology National University of Defense Technology Changsha China School of Software Engineering Tongji Uniersity Shanghai China

ISBN: (纸本)9789819606016

Neural-symbolic systems (NSSs), which are typically cyber-physical systems integrated with artificial intelligence modules, have received much attention in both academic and industrial fields. However, thorough verification (such as model checking) of such systems in general leads to prohibitively high costs due to the scale of the state space, and this makes it barely accomplishable within desired time-span. Consequently, light-weight verification techniques are introduced to deal with such cases, e.g., runtime verification (RV, for short). In this paper, we investigate an RV framework for NSSs against signal temporal logic (STL) properties. To guarantee that the on-line monitoring could be accomplished in time, we utilize Euler-prediction to sample the system under scrutiny at discrete time-steps. Our framework supports both qualitative and quantitative on-line monitoring of STL specifications, and thus it terminates whenever a property is satisfied and/or violated by a finite prefix of the trajectory, and it can also provide the bounds of the quantitative satisfaction/violation w.r.t. the remaining execution. Notably, for the so-called piece-wise linear NSSs against affine STL formulas, our approach is exact. On top of it, a prototype tool is implemented, and is experimentally evaluated. The results demonstrate the feasibility of the presented runtime verification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Model checking

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VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models 31

VisualRWKV: Exploring Recurrent Neural Networks for Visual L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hou, Haowen Zeng, Peigen Ma, Fei Yu, Fei Richard Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shool of Information Technology Carleton University Canada

ISBN: (纸本)9798891761964

Visual Language Models (VLMs) have rapidly progressed with the recent success of large language models. However, there have been few attempts to incorporate efficient linear Recurrent Neural Networks (RNNs) architectures into VLMs. In this study, we introduce VisualRWKV, the first application of a linear RNN model to multimodal learning tasks, leveraging the pre-trained RWKV language model. We propose a data-dependent recurrence and sandwich prompts to enhance our modeling capabilities, along with a 2D image scanning mechanism to enrich the processing of visual sequences. Extensive experiments demonstrate that VisualRWKV achieves competitive performance compared to Transformer-based models like LLaVA-1.5 on various benchmarks. Compared to LLaVA-1.5, VisualRWKV has a speed advantage of 3.98 times and can save 54% of GPU memory when reaching an inference length of 24K tokens. To facilitate further research and analysis, we have made the checkpoints and the associated code publicly accessible at the following GitHub repository: https://***/howard-hou/VisualRWKV. © 2025 Association for Computational Linguistics.

关键词： Recurrent neural networks

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A Review of Colonial Pipeline Ransomware Attack 23

A Review of Colonial Pipeline Ransomware Attack

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23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing Workshops, CCGridW 2023

作者： Beerman, Jack Berent, David Falter, Zach Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH45056 United States

ISBN: (纸本)9798350302080

In April, 2021 a ransomware attack occurred on Colonial Pipeline. The details of this attack point to the hacking group DarkSide taking advantage of the design flaws in the Colonial Pipeline network. After extensive research, the specificity of this attack was related to VPN access through an unused account. In order to regain control of their systems, Colonial Pipeline paid the attackers. This act has only created an incentive for similar attacks across the nation. The events of this attack have impacted both the United States, where the company is located and the world in a very negative way. This paper analyzes the attack with published data and provides a detailed attack methodology. From the attack methodology the focus then shifts into the impacts that an attack of this caliber had, on both the company, the United States, and the world. We then outline possible defense strategies against this type of ransomware attack, analyzing what could have been done to prevent this attack from happening. In addition, we also detail how companies can prevent future attacks of this caliber. Finally we wrap up or findings and detail the key takeaways of the entire attack. © 2023 IEEE.

关键词： Virtual private networks

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TunSLR-25: A New Static Tunisian Sign Language Recognition System 41st

TunSLR-25: A New Static Tunisian Sign Language Recognition S...

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41st computer Graphics International Conference, CGI 2024

作者： Aloui, Sarra Wang, Yu Fan-fan, Zhou College of Computer Science and Software Engineering Hohai University Nanjing China First Affiliated Hospital of Kunming Medical University Kunming China

ISBN: (纸本)9783031818059

There are 466 M deaf across the world according to World Health Organisation. They are deprived of their basic human right which is communicating with others. Many solutions have been developed to help hard-of-hearing people overcome communication barriers. The existing sign language recognition research focuses on sign languages other than Tunisian Sign Language. Consequently, a Tunisian Sign Language dataset, TunSL-D, was developed for this research. The dataset contains static images that are more complex and can generalize the classification tasks. The images represent the most used words and alphabets in Tunisian Sign language. This paper proposes a novel model TunSLR-25 that recognizes and classifies static Tunisian sign language. The model is first trained on the words data, achieving an accuracy of 95.49%, then trained on both words and alphabets, achieving an accuracy of 88%, with a low parameter number of 1,098,901. The TunSL-D has been trained on SLRNet-8 CNN, a 4-layer CNN model, pre-trained models Resnet50, Resnet101, and InceptionV3. The paper compares the performance of these CNN models and the proposed CNN model TunSLR-25. The system will be integrated into a web application designed to be accessible and user-friendly for this community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Classification SLR Tunisian Sign Language

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Research on Visual Slam Technology for UAV Positioning and Navigation 21

Research on Visual Slam Technology for UAV Positioning and N...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wang, Zhou Zhihao, Liu Ying, Tian Yujun, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China College of Computer and Artificial Intelligence Software College Huaihua University Huaihua China

ISBN: (纸本)9798331519254

UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task successfully, the UAV must accurately understand its status, such as position, navigation speed, heading, as well as the starting point and target position. Moreover, it mainly depends on the current environment and location. This article generally focuses on the research on visual SLAM (Simultaneous localization and mapping) for UAV positioning and navigation. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

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computers, Materials & Continua 2024年第4期79卷 161-182页

作者： Yu Zhou Bosong Lin Siqi Hu Dandan Yu College of Computer Science and Software Engineering Shenzhen UniversityShenzhen518060China College of Management Shenzhen UniversityShenzhen518060China Information Center The First Affiliated Hospital of Dalian Medical UniversityDalian116011China

This paper addresses the problem of predicting population density leveraging cellular station *** wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban ***,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population *** address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant *** preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population ***,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population *** experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction *** performance of this model in tasks pertaining to population prediction exhibits commendable results.

关键词： Population density estimation smart city transformer multiview learning

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Do Bad Smells Lead to Defects? 13

Do Bad Smells Lead to Defects?

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13th International Conference on software Technology and engineering, ICSTE 2023

作者： Topuz, Fatma Neda Umphress, David A. Osmaniye Korkut Ata University Computer Engineering Osmaniye Turkey Auburn University Computer Science and Software Engineering AuburnAL United States

ISBN: (纸本)9798350371475

Code that disregards fundamental sound design practices is said to 'smell.' While a code smell is not normally a bug, conventional wisdom suggests that it could lead to bugs when the software is later modified. This makes logical sense, but is there empirical evidence that supports this argument? If code smells can be shown to metastasize over time, are there some code smells that more virulent than others? We set out to address these questions by examining five popular Python open-source software projects to determine if recorded defects traced back to segments of code containing a smell. Our study focused on eight code smells: Large Class, Long Parameter List, Feature Envy, Parallel Inheritance Hierarchy, Data Class, Lazy Class, Refused Bequest, and Long Method. We analyzed GitHub change and defect logs for defects, then identified how the code as repaired. A smell was assumed to be the cause of the defect if changes resulted in removal of the smell. We counted the number of code smells in the project and used the ratio of the count of smells to the count of defects to quantify the influence of smells on defects. Our results indicate that the Long Parameter List code smell has the highest positive correlation, the Long Method and Large Class smells have a moderate correlation, and the Parallel Inheritance Hierarchy, Data Class, Lazy Class, Feature Envy and Refused Bequest smells have no significant correlation on the presence of defects. © 2023 IEEE.

关键词： Open source software

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BiKD-Yolo: A Small Object Detection Algorithm from UAV Perspective Based on BiFormer Attention and Knowledge Distillation 20

BiKD-Yolo: A Small Object Detection Algorithm from UAV Persp...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Hua, Xiang Cai, Zeju Zhu, Xiangyuan Aijia, Ouyang School of Software Engineering Sun Yat-sen University Guangdong China School of Computer Science and Software Zhaoqing University Guangdong China School of Information Engineering Zunyi Normal University Guizhou China

ISBN: (纸本)9798350356328

Object detection is crucial for the real-time operations of Unmanned Aerial Vehicles (UAVs), particularly in identifying small objects within UAV imagery. While existing computer vision techniques have shown success in UAV applications, detecting small objects remains challenging due to limited information, small size, and low resolution. This study presents the BiKD-YOLO approach, which combines BiFormer attention and knowledge distillation to enhance both the precision and efficiency of small object detection. We employ SAHI (Slice-Assisted Hyper-Inference) cropping to enlarge small objects in images, followed by knowledge distillation for model compression, facilitating knowledge transfer from larger to smaller models for streamlined operation. Through the incorporation of the BiFormer attention mechanism, the detection process is further refined by focusing on critical regions of interest, thereby improving overall accuracy. Experimental Results from tests on the VisDrone dataset show that BiKD-Yolo greatly surpasses the baseline, delivering superior performance in detection accuracy, mAP, and AP metrics. In summary, the BiKD-Yolo method presents a novel and effective solution for improving small object detection in UAV images, offering enhanced accuracy and speed. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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