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SSRN 2024年

作者： Jin, Ran Li, Zhengang Jin, Tao Hou, Tengda Fei, Wang College of Big Data and Software Engineering Zhejiang Wanli University Zhejiang Ningbo China School of Computing National University of Singapore Singapore

As image-text retrieval (a critical task in the field of computer vision) links cross-modal data, it has captured an extensive attention. Most of the existing methods intended for matching images and texts explore the local similarity levels between images and sentences to align images with texts. Even though this fine-grained approach has remarkable gains, how to further mine the deep semantics between data pairs and focus on the essential semantics in data remains to be quested. In this work, a new semantic filtering and adaptive approach (FAAR) was proposed to ease the above problem. To be specific, the Filtered Attention (FA) module selectively focuses on typical alignments with the interference of meaningless comparisons eliminated. Next, the Adaptive Regulator (AR) further adjusts the attention weights of key segments for filtered regions and words. The superiority of our proposed method was validated by a number of qualitative experiments and analyses on the Flickr30K and MSCOCO datasets. © 2024, The Authors. All rights reserved.

关键词： Semantics

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SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

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Digital Communications and Networks 2024年第1期10卷 142-149页

作者： Kui Zhu Yongjun Ren Jian Shen Pandi Vijayakumar Pradip Kumar Sharma School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Peng Cheng Laboratory Shenzhen518000China Department of Computer Science and Engineering University College of Engineering TindivanamTamil Nadu604001India Department of Computing Science University of AberdeenAberdeenAB243UEUK

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep *** Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data ***,FDL can only guarantee data security and privacy among multiple clients during data *** the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively *** this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered ***,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and *** addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data ***,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear *** theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

关键词： Internet of vehicles Federated deep learning Data security Data auditing Data locating and recovery

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Using Accessibility Awareness Interventions to Improve computing Education

Using Accessibility Awareness Interventions to Improve Compu...

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software engineering: software engineering Education and Training (ICSE-SEET), IEEE/ACM International Conference on

作者： Yang Liu Domenic Mangano Krishna Prasad Neupane Samuel Malachowsky Daniel E. Krutz Department of Software Engineering B. Thomas Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798400704987

ISBN: (纸本)9798350352115

Research demonstrates that we continue to be de-ficient in creating inclusive and equitable software, with a lack of empathy among software developers attributed to this issue, even if unintentional. Although increasing empathy has been shown to be part of the solution, unfortunately there is a lack of data that can be analyzed that shows how empathy-building and accessibility awareness interventions can be utilized in computing education, along with a shortage of free, public, and easily adoptable educational material regarding empathy and the creation of accessible software. Through large in-person studies that involve real-world par-ticipants, we are able to contribute to the knowledge regarding the potential benefits that can be used to improve computing education by implementing empathy-building strategies in the curriculum. Two forms of accessibility awareness exercises are utilized to display the effectiveness of empathy-based learning and teach the importance of accessibility in computing, including experiential and expression-based interventions. In these inter-ventions, we expect to find an increase in awareness and empathy in participants, as well as an increase in participant interest in the topic of software inclusivity and accessibility by placing the participant in a situation they may encounter in the real world. We additionally present a web-hosted, easily adoptable ex-periential and expression-based educational lab to support the inclusion of inclusive development and/or empathy-focused topics into a variety of curricula formats. Complete materials are available on the project website: https://***

关键词： Training Project management software Distance measurement Browsers software engineering Business

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Optimizing Edge computing Usability with Random Forest Algorithms

Optimizing Edge Computing Usability with Random Forest Algor...

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computing (ICOCO), IEEE International Conference on

作者： Esraq Humayun Kok Cheng Lim Evelyn Yeap Ali Selamat Nguyet Quang Do Mohd Hazli Mohamed Zabil Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computing Universiti Tenega Nasional Kuala Lumpur Malaysia Department of Computing Universiti Tecnologi Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

This study convincingly demonstrates the immense potential of Random Forest algorithms to significantly enhance usability in edge computing environments by concentrating on critical parameters such as execution time, energy consumption, and scalability. By leveraging the power of the Random Forest technique, we train models using a multitude of decision trees, which not only improves prediction accuracy but also effectively combats overfitting. This approach directly addresses pressing challenges, including limited resources, scalability issues, and the unpredictability of network conditions. Our results are impressive: we achieved a noteworthy reduction in mean execution time by 0.73 seconds and a standard deviation of 1.15 seconds. These metrics highlight the model’s remarkable resilience and reliability in boosting system responsiveness. With these advancements, Random Forest stands out as a formidable solution for the demanding real-time processing needs inherent in edge computing. Our research presents a compelling strategy for establishing robust and effective edge computing infrastructures by closing crucial gaps that currently exist, particularly the lack of scalable solutions for large, heterogeneous edge platforms, adaptability in fluctuating contexts, and real-world validation. In summary, our work lays a solid foundation for further exploration of adaptive Random Forest applications that can seamlessly adjust to dynamic network conditions, reinforcing the significance of edge computing in latency-sensitive, nextgeneration applications.

关键词： Adaptation models Scalability Computational modeling Heuristic algorithms Real-time systems Usability Random forests Standards Optimization Edge computing

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Amplifying the Voice of Blacks in computing

Amplifying the Voice of Blacks in Computing

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Black Issues in computing Education (BICE)

作者： Cheryl Swanier Cheryl Seals Dale-Marie Wilson Manning College of Information and Computing Sciences University of Massachusetts USA Computer Science and Software Engineering Auburn University USA Department of Software and Information Systems University of North Carolina at Charlotte USA

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

This workshop is designed to intentionally share experiences and connect participants to their greatest asset, their voice. There is power in your voice. Engaging interaction, thought-provoking storytelling, and authentic truth telling will open the way for understanding the value of harnessing voice power when navigating professionally and personally. Despite diversity, equity, and inclusion efforts, stated commitments to diversity, the demographic characteristics of the professoriate and industry look remarkably like the faculty and professionals of 50 years ago for Blacks in computing. Blacks in computing constantly face discrimination in the form of microaggressions and white fragility. This discrimination has not translated to greater Black faculty and professional representation in their career advancement. Instead, the health of both faculty and professionals are being adversely impacted, as they are experiencing insomnia, anxiety, depression, emotional distress, and physical exhaustion. This disruption of norms has a negatively impacted the productivity of both Black men and women at work and can lead to their demotion and dismissal. Many times, these struggles are not shared, and their voices are silenced. This workshop primarily addresses how Blacks in computing can use their voice to advance their careers and utilize proven voice positioning strategies to harness their voice power and to maximize professional and personal wellbeing. These voice strategies will facilitate both Black men and women in their career advancement, skill development, and preparation for entry into their next career level. A failure to address these challenges has negative implications for both academia and industry

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Graph-based Modeling and Simulation of Emergency Services Communication Systems

arXiv

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arXiv 2024年

作者： Jordan, Jardi Martinez Stiber, Michael Computing and Software Systems Division School of Science Technology Engineering and Mathematics University of Washington Bothell BothellWA98011 United States

Emergency Services Communication Systems (ESCS) are evolving into Internet Protocol based communication networks, promising enhancements to their function, availability, and resilience. This increase in complexity and cyber-attack surface demands better understanding of these systems’ breakdown dynamics under extreme circumstances. Existing ESCS research largely overlooks simulation and the little work that exists focuses primarily on cybersecurity threats and neglects critical factors such as non-stationarity of call arrivals. This paper introduces a robust, adaptable graph-based simulation framework and essential mathematical models for ESCS simulation. The framework uses a representation of ESCSes where each vertex is a communicating finite-state machine that exchanges messages along edges and whose behavior is governed by a discrete event queuing model. Call arrival burstiness and its connection to emergency incidents is modeled through a cluster point process. Model applicability is demonstrated through simulations of the Seattle Police Department ESCS. Ongoing work is developing GPU implementation and exploring use in cybersecurity tabletop exercises. © 2024, CC BY.

关键词： Queueing theory

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Improved Stereo Matching Algorithm Based on Sparse Window

Improved Stereo Matching Algorithm Based on Sparse Window

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International Conference on Automation and computing (ICAC)

作者： Qi Tang Yuanping Xu Jiliu Zhou Chao Kong Jin Jin Zhijie Xu Chaolong Zhang Yajing Shi School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Hudderseld UK

This study presents a sparse window-based stereo-matching algorithm that enhances the accuracy and efficiency of the semi-global matching algorithm. Unlike traditional methods, this algorithm processes pixel areas based on their texture features, resulting in more efficient encoding. The proposed approach systematically samples pixels within the original encoding window to reduce the number of pixels involved in the process. Additionally, using the FAST feature detection method distinguishes texture areas and applies different encoding processes for each area to obtain the feature encoding of the center pixels. Experimental results show that compared with traditional semi-global stereo matching algorithms, our proposed sparse window-based algorithm improves processing speed by 0.06 seconds and reduces average error by 10.92%.

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Deep Learning Object Detection 7th

Deep Learning Object Detection

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Liu, Jingnian Huang, Weihong Xiao, Lijun Huo, Yingzi Xiong, Huixuan Li, Xiong Xiao, Weidong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Xiangtan411201 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

Object detection techniques are a major part of computer vision research, with large-scale applications in industrial, scientific and other scenarios. Technologies such as face detection, medical image detection, autonomous driving, and traffic detection have played a significant role in people’s lives. With the rapid development of deep learning, many application areas, such as image classification, text classification, machine translation, etc., have achieved breakthrough success in combination with deep learning. R-CNN brings object detection into the era of deep learning, and its advantage compared with traditional methods is that the former requires personnel to extract features manually, while the latter uses deep learning to extract features automatically, which greatly improves efficiency, simplifies operation, and opens a new era of object detection research. First, this paper provides an overview of deep learning-based object detection backbone networks, reviews and analyzes milestone object detection algorithms, compares commonly used datasets, summarizes applications, and finally concludes the paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Prediction of Sprint Delivery Capability in Agile software Development Using Machine Learning

Prediction of Sprint Delivery Capability in Agile Software D...

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International Research Conference on Smart computing and Systems engineering (SCSE)

作者： Narayanasamy Lorshan P. M. A. K. Wijerathna B. T. G. S. Kumara Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

Scrum is one of the most prominent agile development frameworks as it delivers the project in many sprints in an incremental manner. Sprint delivery capability is a crucial concept in the scrum projects as it assesses whether sprint can capable of delivering its issues on its length. It is difficult to consider forecasting sprint delivery capability in large scrum projects as multiple sprints are processed simultaneously and agile dynamics. Machine Learning (ML) models were applied in the existing studies to forecast sprint delivery but, had limitations in finding non-linear and hidden patterns. To address these problems, this research exploited the untapped ensemble learning approach to bridge the gap to improve the prediction of sprint delivery capability in different project progression levels. The study gathered the data from one of the large open-source scrum projects, JIRA which included 1873 iterations and 10853 issues in table format spanning at 30%, 50%, and 80% project progression levels. Data preprocessing enabled to refinement of the data for analysis. The author enhanced the feature aggregation by using both statistical and automatic feature learning to derive the new features from the issue table and combine them with the iteration table. The key features affecting sprint delivery capability were identified using hybrid feature selection approach. The study developed the untapped ensemble models by choosing the different combinations of ML, Deep Learning (DL), and ensemble algorithms using appropriate ensemble methods and combination rules. The author examined the models and concluded that Categorical Boost (CatBoost) gained the highest accuracy of 96.8% at the 80% project progression level. This prediction assists in improving sprint planning, delivering the software on time, and getting more insights about sprint performance.

关键词： Representation learning software algorithms Predictive models Feature extraction Prediction algorithms software Planning Ensemble learning Modeling Forecasting

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Optimization of Interactive Videos Empowered the Experience of Learning Management System

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Computer Systems Science & engineering 2023年第7期46卷 1021-1038页

作者： Muhammad Akram Muhammad Waseem Iqbal M.Usman Ashraf Erssa Arif Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

The Learning management system(LMS)is now being used for uploading educational content in both distance and blended *** platform has two types of users:the educators who upload the content,and the students who have to access the *** students,usually rely on text notes or books and video tutorials while their exams are conducted with formal *** assessments and examination criteria are ineffective with restricted learning space which makes the student tend only to read the educational contents and videos instead of interactive *** aim is to design an interactive LMS and examination video-based interface to cater the issues of educators and *** is designed according to Human-computer interaction(HCI)principles to make the interactive User interface(UI)through User experience(UX).The interactive lectures in the form of annotated videos increase user engagement and improve the self-study context of users involved in *** interface design defines how the design will interact with users and how the interface exchanges *** findings show that interactive videos for LMS allow the users to have a more personalized learning experience by engaging in the educational *** result shows a highly personalized learning experience due to the interactive video and quiz within the video.

关键词： User interface user experience learning management system linear nonlinear video interactive video visual design

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