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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Mohammad Daud Haiderzai Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9798400700408

User contributions is an important part in software development, the pursuit of identifying effective means of user contributions in software development is a crucial area of interest for software companies and their development teams. Determining the most opportune moments, locations, and methods for such contributions presents a significant challenge for organizations, which necessitates the exploration of generic and proven solutions. Applying patterns, established and observed solutions may provide the answers to such challenges and similar ones. This paper offers insights into the practical implementation of seven newly discovered organizational patterns in a real-life environment. The patterns are comprehensively described, accompanied by illustrative examples, and subsequently applied in actual practices.

关键词： organizational patterns software development software engineering

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Classification of Microorganisms from Sparsely Limited Data Using a Proposed Deep Learning Ensemble 7th

Classification of Microorganisms from Sparsely Limited Dat...

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7th International Conference on Inventive systems and Control, ICISC 2023

作者： Chettiar, Gautam Shukla, Amogh Patil, Hemprasad Jindal, Sumit Department of Communication Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Software Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Embedded Technology Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819916238

With the recent advancements in medicine and biotechnology, there is always a growing need for more sophisticated decision systems to aid in certain biological factors, such as detecting a pathogen or microscopic organism and its accurate classification. In such a domain, there is very little tolerance for error, and the highest priority must be given to creating intelligent systems capable of doing so. Advancements in computer vision and other state-of-the-art image processing techniques enable computation systems to extract and analyze principal features from a specimen that even a trained eye may otherwise miss. There has also been an upsurge in the research on the applications of machine learning to aid in specific surgical procedures and diagnostic tests. Classifying organisms at the micro-scale is an essential stepping stone for their long-term success. The study aims to ensembling the best machine learning techniques and models in the computer vision domain, such as transfer learning, top-performing models, and image preprocessing methods, to maximize the capabilities of a typical image classification model for a sparse and limited multi-labeled class dataset containing microscopic images and hence shed light and explore into how more excellent performance can be obtained while facing classical dataset limitations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microorganisms

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of Computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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Dynamic Tip-Over Avoidance Method for Mobile Manipulators Based on the Extended Zero-Moment Point Algorithm for Human–Robot Collaboration

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Journal of Computing and Information Science in engineering 2025年第5期25卷 051003页

作者： Tao, Yong Liu, Haitao Song, Yian Deng, Changyi Wang, Baicun Zheng, Pai School of Mechanical Engineering and Automation Beihang University Beijing 100191 China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing 100040 China School of Mechanical Engineering Zhejiang University Hangzhou 310030 China Research Institute for Advanced Manufacturing Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong

Human–robot collaboration (HRC), as an important role in intelligent manufacturing, is widely utilized in modern factories. Mobile manipulators offer greater flexibility due to the ability to simultaneously move their bases and arms. This provides them an advantage in facing challenging tasks such as flexible production and intelligent manufacturing. The environment of modern factories is complex, with slopes and thresholds at the entrance. Stability plays a fundamental role in complex environments. Therefore, for the future deployment and application of mobile manipulator in HRC, a novel dynamic tip-over avoidance method is proposed for mobile manipulators based on the extended zero-moment point (ZMP) algorithm. This method extends the traditional ZMP stability criterion into three dimensions, deriving a system-wide three-dimensional (3D) zero-moment point. The integration of the extended ZMP with redundant features facilitates adaptive weight matrix adjustments, enabling the robot to adapt its motion based on environmental constraints, thus preventing instability such as tip-over. Simulation verification is carried out withcoppeliasim. Experimental verification is performed using the MR2000 + FR3 mobile manipulator. The results confirm that the extended ZMP algorithm effectively prevents tipping in complex environments.

关键词： tip-over avoidance null-space projection Weight (Mass) mobile manipulator Stability Manipulators Robots human–robot interfaces/interactions velocity controlled manufacturing automation extended ZMP Algorithms

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Systematic review on privacy-preserving machine learning techniques for healthcare data

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Journal of Cyber Security Technology 2025年

作者： Sasirekha, B. Gunavathi, C. Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore India

The rapid growth of healthcare data, powered by advances in Electronic Health Records (EHRs), where sensors, medical imaging, and genetic technologies, has generated significant prospects in Machine Learning (ML) to improve disease diagnosis, treatment methodologies, and precision medicine. The sensitive and personally identifiable characteristics of medical data engender significant concerns over privacy, security, and adherence to regulations such as HIPAA and GDPR. To maintain ethical accountability and public trust, ML in healthcare must be executed with resilient Privacy-Preserving algorithms. In response to this critical necessity, Privacy-Preserving Machine Learning (PPML) has evolved as a viable method for safeguarding patient data while preserving model performance. This review paper focus on a systematic analysis of cryptographic, non-cryptographic, and hybrid models of privacy-preserving technologies in healthcare systems, which integrates several ways to enhance privacy and utility. Our review examines advanced methodologies such as Homomorphic Encryption, Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL). Each technique is assessed according to its technology foundation, benefits, drawbacks, and relevance to particular clinical situations. Ultimately, our paper addresses the significant challenges of using PPML in real-world applications and proposes future research directions to facilitate the development of dependable, scalable, and privacy-aware medical AI systems. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cryptography Healthcare Machine learning PPML techniques Privacy

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Semiconductor Equipment Health Monitoring With Multi-View Data

Semiconductor Equipment Health Monitoring With Multi-View Da...

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2023 Winter Simulation Conference, WSC 2023

作者： Ahn, Jeongsun Kim, Hong-Yeon Cho, Sang-Hyun Kim, Hyun-Jung Kim, Hongyeon Choi, Hyeonjeong Ham, Dain Korea Advanced Institute of Science and Technology Department of Industrial and Systems Engineering 291 Daehak Street Daejeon34141 Korea Republic of Wonik Ips Software Team 75 Jinwisandan Street Pyeongtaek17709 Korea Republic of

ISBN: (纸本)9798350369663

Monitoring the state of semiconductor equipment is crucial for ensuring optimal performance and preventing downtime. In previous studies, researchers have attempted to derive a health index that represents the overall condition of the equipment as a single index. However, these studies have often relied solely on time-series data from each sensor, neglecting other important viewpoints engineers consider when monitoring the equipment. To address this limitation, we propose a multi-view data set specifically designed for semiconductor equipment, which incorporates process, trend, and spatial data. In addition, we present a framework for deriving a hierarchical health index based on a multi-view data set. The hierarchical structure is derived using a hierarchical spectral clustering method, and an autoencoder-based health index is used. We have verified the effectiveness of our approach with real data sets, demonstrating its potential as a valuable tool for monitoring the condition of semiconductor equipment. © 2023 IEEE.

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Optimizing Existing SHM systems: Retasking as a Self-Healing Solution for Improved Fault Tolerance

Optimizing Existing SHM Systems: Retasking as a Self-Healing...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Aaron Mano Cherian D. Ajitha Avanish Gouraha Dipanshu Mandal School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper focuses on self-healing algorithms in structural health monitoring (SHM) systems centered around the enhancement of resilience and adaptability of the systems. In this study, imports from existing methods (clustering, Fault Tolerant Multiple Redundancy (FTMR) and reinforcement learning) are analyzed against the choice of creating a novel retasking algorithm designed for dynamic resource redistribution and optimal monitoring coverage. Unlike conventional methods, retasking will allow adapting the coverage in real time, whereby system down time will be reduced, with less computational load achieved through task redistribution through functional sensors. Findings showed that retasking improved reliability and scalability of the SHM systems drastically, providing a simple yet powerful resolution towards modern infrastructure monitoring. This study stresses the retasking capability to redefine self-healing in the SHM systems for future directions in infrastructure safety.

关键词： Training Heuristic algorithms Fault tolerant systems Redundancy Clustering algorithms Reinforcement learning Solids Real-time systems Monitoring Stress

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Unified Modeling Method of Heterogeneous Resources for Multi-platform Avionics System Based on AADL 3

Unified Modeling Method of Heterogeneous Resources for Multi...

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3rd International Conference on Computer, Control and Robotics, ICCCR 2023

作者： Huang, Hui Liu, Chang Yang, Xunli An, Dong Guo, Yingxian Zhu, Wenjing China Aeronautical Radio Electronics Research Institute Key Laboratory of Avionics System Integration Shanghai China School of Reliability and Systems Engineering Beihang University Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9781665492126

Currently, single-platform avionics system modeling often uses service-oriented modeling methods to describe service-oriented architecture (SOA). However, for the multi-platform avionics system, the traditional modeling method can not represent the heterogeneous resources uniformly;and the description method of the relationship between services and resources is lacking. To solve these problems, this paper proposes a modeling method based on architecture analysis and design language (AADL). The heterogeneous resource pool model is constructed by unified description of heterogeneous resources internally and externally. In addition, a service-oriented approach is used to match function servitization with resources. This method is used to model a multi-platform avionics system. The example shows that this method can abstract heterogeneous resources in the same way, support multiple types of resources, and describe the relationship between services and resources in the task scenario. © 2023 IEEE.

关键词： Information services

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Astronomical Image Processing by the Lemur software 24

Astronomical Image Processing by the Lemur Software

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6th Asia Conference on Machine Learning and Computing, ACMLC 2024

作者： Khlamov, Sergii Savanevych, Vadym Briukhovetskyi, Oleksandr Vlasenko, Vladimir Dikov, Yevhen Dmytrenko, Artem Department of Media Systems and Technologies Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of systems engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Western Center of Radio Engineering Surveillance National Space Facilities Control and Test Center Kyiv Ukraine Space Research and Communication Center National Space Facilities Control and Test Center Kyiv Ukraine Department of Software Engineering GlobalLogic Incorporation Kharkiv Ukraine Institute of Astronomy V. N. Karazin Kharkiv National University Kharkiv Ukraine

ISBN: (纸本)9798400710018

The astronomical images are made by cameras with the charge-coupled device (CCD). They can be received from the different sources, like servers, clusters, predefined series, archives or "live" (online) data streams. The astronomical images processing is focused on but not limited to the following tasks: data mining, knowledge discovery, big astronomical data processing, filtering, background alignment, brightness equalization, segmentation, classification, image recognition, object's image detection, object's astrometry and photometry, moving object detection, parameters determination of the object's image and apparent motion, reference objects selection and others. The modern Lemur software of the Collection Light Technology (CoLiTec) project (https://***) was developed using the described above technologies and approaches. The Lemur software is designed to perform a sequence of the following main steps: pre-processing (astronomical information collection -> worst data rejection -> useful data extraction -> data mining -> classification -> background alignment -> brightness equalization), image processing (segmentation -> typical form analysis -> recognition patterns applying -> detection of the object's image -> astrometry -> photometry -> objects identification -> tracks detection), knowledge discovery (Solar System objects or artificial satellites to be discovered, tracks parameters for the investigation, light curves of the variable stars, scientific reports in the international formats). The paper describes the modern features for the astronomical image processing implemented in the Lemur software. It has assisted in making over 1700 discoveries of asteroids, including 5 NEOs, 21 Trojan asteroids of Jupiter, 1 Centaur. In total it has been used in about 800 000 observations, during which five comets were discovered. All such observations and discoveries have official observational circulares from the Minor Planet Center (MPC) of the International Ast

关键词： Asteroids

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