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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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Machine Learning Algorithms in Gene Editing

Machine Learning Algorithms in Gene Editing

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Sanyour, Kaluad Kim, Wooyoung University of Washington Bothell Computing and Software Systems School of Stem BothellWA United States University of Tabuk Alwajh College Computer Science Department Tabuk Saudi Arabia

ISBN: (纸本)9781665468190

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), as an adaptive immunity mechanism in prokaryotes, is a programmable system that can be used for editing genes of various species. Possibly it can be used to edit genes causing some rare diseases of humans, such as Ichthyosis. CRISPR is associated with the Cas9 protein that cuts a DNA sequence. Cas9 binds to a gRNA that guides Cas9 to an editable site. Although versatile and easy to design, a misguiding gRNA could result wrong edition of genes. Hence, the CRISPR/Cas9 process needs to find ideal gRNAs that can guide Cas9 to on-targets, and avoid off-targets. Various machine learning algorithms play important roles in finding effective gRNAs for the CRISPR/Cas9 by predicting the cleavage efficiency of gRNA. Here, we aim to provide an overview and comprehensive analysis of various machine and deep learning algorithms that are effective in predicting CRISPR gRNA sequences' on-target activities. Comparison results show that the hybrid approaches combining deep learning and other machine learning algorithms present excellent outcomes. We applied the models to predict the cleavage efficiency of gRNAs from the FLG gene causing Ichthyosis, and the results suggest to use multiple models to obtain consistent and trustworthy results. Although our work does not provide final solutions, it will open opportunities for computational tools to diagnose and treat rare diseases. The datasets and codes are available at https://***/ksanyour/MLforGeneEditing. © 2022 IEEE.

关键词： Genes

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Hybrid Cybersecurity Research and Education Environment for Maritime Sector

Hybrid Cybersecurity Research and Education Environment for ...

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Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Gabor Visky Aleksei Šiganov Muaan ur Rehman Risto Vaarandi Hayretdin Bahşi Hayretdin Bahsi Leonidas Tsiopoulos Department of Software Science Tallinn University of Technology Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

Digitalization is a global trend affecting many crit-ical infrastructures, including maritime transportation. Incorpo-rating information and operational technologies into transportation processes significantly enhances efficiency but also introduces substantial risks originating from cyberspace. It is a significant endeavour to develop lab environments to research offensive and defensive aspects of cybersecurity and facilitate hands-on experience in educational settings. This paper introduces a hybrid cybersecurity research and education lab environment for the maritime domain. It enables real-world data to be fed into a controlled setting equipped with various emulation system components. The lab, which combines open-source and low-cost commercial software, also benefits from virtualization technologies and real hardware devices. Its scala-bility and affordability make it a viable solution for educational purposes. Although the environment is defended against external access, it can be provided for third parties on demand. We conducted a series of cyberattacks on the network and an open-source navigation application to demonstrate the effective-ness of our lab setting. The recorded and shared network traffic from these attacks not only serves as a fertile ground for our own research but also invites other researchers to develop and test their solutions, fostering a collaborative environment for the advancement of cybersecurity in the sector.

关键词： Navigation Emulation Transportation Telecommunication traffic Market research software Hardware

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract)

A Survey on Modern Deep Neural Network for Traffic Predictio...

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International Conference on Data Engineering

作者： David Alexander Tedjopurnomo Zhifeng Bao Baihua Zheng Farhana Murtaza Choudhury A. K. Qin School of Computing Technologies RMIT University Australia School of Computing and Information Systems Singapore Management University Singapore School of Computing and Information Systems The University of Melbourne Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare; making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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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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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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Delay-aware Dual-Interface Gateway Activation for Energy-Efficient Data Collection in IoT

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Qin, Hua Chen, Hao Li, Ni Deng, Yaqi Yang, Gelan Peng, Yang Hunan City University College of Information and Electronic Engineering Yiyang413000 China University of Washington Bothell Division of Computing and Software Systems BothellWA98011 United States

The proliferation of the Internet of Things (IoT) has elevated the demand for energy-efficient gateway solutions, particularly for battery-powered Wi-Fi devices functioning as gateways in IoT data collection. Traditional approaches either neglect gateway energy consumption or struggle to balance energy efficiency with delay requirements. To address this, we introduce the Dual-interface Gateway Activation (DGA) scheme, a significant advancement enhancing IoT gateway energy management via interactive cooperation between ZigBee and Wi-Fi. DGA's core innovation lies in its dual-interface design and heterogeneity-aware activation strategy. By leveraging coexisting low-power ZigBee communications, DGA dynamically activates the gateway's high-power Wi-Fi interface only when necessary for data reception, effectively decoupling power consumption from continuous Wi-Fi operation. Meanwhile, DGA conservatively schedules high-reliability Wi-Fi sessions to augment unreliable ZigBee communications, thereby ensuring prompt gateway activation for delay-bounded data delivery under varying network conditions. This innovative approach not only guarantees high energy efficiency but also low latency, overcoming the limitations of single-interface gateways. Experimental validation of our prototype system demonstrates DGA's superior performance, achieving up to 62.1% and 45.9% reductions in gateway energy consumption compared to state-of-the-art single-interface and dual-interface solutions, respectively. Additionally, DGA attains lower node energy consumption, further highlighting its potential to enable more energy-efficient IoT networks. © 2017 IEEE.

关键词： Wireless local area networks (WLAN)

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Automatic Deployment to Kubernetes Cluster by Applying a New Learning Tool and Learning Processes 10

Automatic Deployment to Kubernetes Cluster by Applying a New...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Golis, Tomáš Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia

The rapid growth and widespread adoption of containerization technologies, such as Docker, and the increasing popularity of Kubernetes as a container orchestration platform have significantly shaped the landscape of modern software development and deployment. Containers have created a paradigm shift in application packaging, offering a consistent and portable environment across diverse infrastructure setups. Meanwhile, Kubernetes has emerged as the dominant choice for managing containerized applications at scale, providing features such as automated scaling, load balancing, and fault tolerance. However, despite the numerous advantages containers and Kubernetes offer, deploying applications to a Kubernetes cluster often poses challenges due to the steep learning curve and complex configuration requirements. To address these challenges, this paper aims to introduce a comprehensive learning tool that automates and simplifies the deployment process on Kubernetes clusters. © 2023 Copyright for this paper by its authors.

关键词： CI/CD Container orchestration Docker image Kubernetes

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Financial Sustainability of Automotive software Compliance and Industry Quality Standards 8th

Financial Sustainability of Automotive Software Compliance ...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Dakić, Pavle Todorović, Vladimir Vranić, Valentino Faculty of Informatics and Computing Singidunum University Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia Faculty of Business Studies and Law MB University Belgrade Serbia

ISBN: (纸本)9789819930906

Achieving a high level of quality in the AV industry involves understanding the complexity of the production cycle with the constant challenges of applying appropriate standards. The need for compliance based on relevant certificates implies the creation of an environment of trust between manufacturers, component suppliers, and end customers. Research should sublimate the most current standards from various fields related to data protection, security, system sustainability, environmental management, financial sustainability, and business transparency. The success of a complex parts supply system is impossible without a stable approach to IT security. The control of production processes sometimes involves using non-standardized techniques, and one of the applicable ones is work-in-progress (WIP), which describes the use of partially finished raw materials in the stages of obtaining final products. Key points and the contribution of the current study are in getting a better overview of the situation within this area related to standards and the financial problems that can arise due to a lack of understanding of standards compliance. The main limitations within the study itself refer to the consideration of the theoretical foundations that should be used during future research related to the practical part during implementation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automotive industry

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