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SEB-ChOA: an improved chimp optimization algorithm using spiral exploitation behavior

Neural computing and Applications 2024年第9期36卷 4763-4786页

作者： Qian, Leren Khishe, Mohammad Huang, Yiqian Mirjalili, Seyedali School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Khoury College of Computer Sciences Northeastern University BostonMA02115 United States Centre for Artificial Intelligence Research and Optimization Torrens University Adelaide Australia University Research and Innovation Center Obuda University Budapest1034 Hungary

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees’ individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs’ performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： Particle swarm optimization (PSO)

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Serverless Software Engineering – and How to Get There. 1

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29th European Systems, Software and Services Process Improvement, EuroSPI 2022

作者： McAleese, Stephen McLaughlin, Jordan Conway Detyna, Filip Murashev, Andrey Yilmaz, Murat Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey Lero The Science Foundation Ireland Research Center for Software Dublin Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Serverless computing is on the rise but developing software to exploit this space involves a deep rethink of software architecture, deployment, and operation (perhaps also, software development processes and team structures). Central to this revolution, we find a compelling argument for distributed, services-based software architectures. But converting a large, established monolith architecture system to microservices is non-trivial and fraught with both cost and risk. For the many firms with established software systems, this architectural system conversion might be considered the first stop-off on the journey to serverless computing. In tandem, software deployment and production monitoring also require reinvention. The focus of this paper involves an examination of the advantages of microservices architectures, include techniques for migrating from monolith architectures. Through application of a Multivocal Literature Review (MLR), we find that migrating from a monolith architecture to a microservices architecture is risky and non-trivial, but that there are techniques that can be employed to support the transition. We find also that monoliths have their advantages which might be overlooked to some extent in the race to serverless computing. © 2022, Springer Nature Switzerland AG.

关键词： Software architecture

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Exploring Aspects of Agile Software Development Risk – Results from a MLR 1

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29th European Systems, Software and Services Process Improvement, EuroSPI 2022

作者： Nolan, Aaron Strickland, Ben Quinn, Adam Gallagher, Kyle Yilmaz, Murat Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey Lero the Science Foundation Ireland Research Center for Software Lero Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Agile software development methods are widely used by software organisations, focusing on short developmental life cycles and customer satisfaction through the iterative and incremental development of software products. Despite their popularity, these methods present risks that may be underappreciated. This paper examines certain risks attributed to agile software development, with a focus on the lack of documentation, scope creep, technical debt and job satisfaction. Through the application of a multivocal literature review, we find that agile software development can greatly benefit projects. However, when agile methods are implemented inappropriately or sub-optimally, projects risk over-spending, delayed or defective software, employee turnover, and overall decreased productivity. Understanding the risks associated with agile software development can help practitioners to achieve higher efficiency and success in their software development projects. © 2022, Springer Nature Switzerland AG.

关键词： Job satisfaction

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yiyuan Liang Hong Jia Licai Wang Yangchen Huang Zhe Yang Big data research and development center North China Institute of Computing Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions.

关键词： Seminars Accuracy Correlation Focusing Feature extraction Data mining Task analysis

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Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers

arXiv

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arXiv 2024年

作者： Chen, Jinsong Liu, Hanpeng Hopcroft, John E. He, Kun School of Computer Science and Technology Huazhong University of Science and Technology China Institute of Artificial Intelligence Huazhong University of Science and Technology China Hopcroft Center on Computing Science Huazhong University of Science and Technology China Department of Computer Science Cornell University United States

While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information from other nodes, hindering their ability to fully harness graph information for learning optimal node representations. To address this limitation, we propose a novel graph Transformer called GCFormer. Unlike previous approaches, GCFormer develops a hybrid token generator to create two types of token sequences, positive and negative, to capture diverse graph information. And a tailored Transformer-based backbone is adopted to learn meaningful node representations from these generated token sequences. Additionally, GCFormer introduces contrastive learning to extract valuable information from both positive and negative token sequences, enhancing the quality of learned node representations. Extensive experimental results across various datasets, including homophily and heterophily graphs, demonstrate the superiority of GCFormer in node classification, when compared to representative graph neural networks (GNNs) and graph Transformers. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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The Crafts+Fabrication Workshop: Engaging Students with Intangible Cultural Heritage-Oriented Creative Design 22

The Crafts+Fabrication Workshop: Engaging Students with Inta...

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2022 ACM Designing Interactive Systems Conference: Digital Wellbeing, DIS 2022

作者： Lu, Zhicong Tan, Peng Ji, Yi Ma, Xiaojuan Department Of Computer Science City University Of Hong Kong China Center For Human-Engaged Computing Kochi University Of Technology Japan Guangdong University Of Technology China Hong Kong University Of Science And Technology Hong Kong

ISBN: (纸本)9781450393584

Engaging local communities is essential to safeguarding intangible cultural heritage (ICH). To better encourage the participation of local communities, especially younger generations, e.g., students, ICH experience and education workshops are widely adopted by academia, museums, governments, and non-profit organizations. The expected outcomes of these workshops, such as archives, documents, and even creative design solutions, can benefit the promotion and preservation of ICH. However, because of the steep learning curve of using traditional ICH tools and the lack of interactions between students and ICH practitioners, many ICH workshops currently fail to engage students in learning ICH-related knowledge, developing empathy with ICH, or designing novel artifacts with ICH elements. To bridge this gap, we designed a workshop, which integrated ICH in China, digital fabrication, creative technology, and making, to engage Chinese students with ICH and creative design. We conducted empirical studies to collect feedback from students (N = 30) and ICH professionals (N=6). The application of digital fabrication tools successfully piqued students' interest in ICH and enabled them to create interactive ICH artifacts through quick prototyping. However, the ICH professionals pointed out several issues of using digital fabrication, especially regarding tacit knowledge, use of traditional tools, and cultural authenticity. We discuss the importance of these factors in students' acquisition of ICH knowledge and ICH-oriented design, and provide implications for future ICH design workshops. © 2022 ACM.

关键词： Students

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data center

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computer Systems science & Engineering 2024年第3期48卷 571-608页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Abdulatif Alabdulatif Ruba Abu Khurma Maribel Garcia-Arenas Pedro A.Castillo University Institute of Computing Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Uttaranchal UniversityUttarakhand248007India Department of Computer Science College of Computer Qassim UniversityBuraydah52571Saudi Arabia MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

As cloud computing usage grows,cloud data centers play an increasingly important *** maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance *** purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data *** aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management *** review revealed three task allocation research topics and seven performance management *** allocation research areas are resource allocation,load-Balancing,and *** management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network *** study proposes new techniques to enhance cloud computing work allocation and performance ***-comings in each approach can guide future *** research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and *** methodologies can steer future research to fill gaps in the literature.

关键词： Cloud computing data centre task allocation performance management resource utilization

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Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication

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computers, Materials & Continua 2022年第11期73卷 3319-3331页

作者： Muhammad Adnan Khan Ahmed Abu-Khadrah Shahan Yamin Siddiqui Taher M.Ghazal Tauqeer Faiz Munir Ahmad Sang-Woong Lee Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea College of Computing&Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Minhaj University LahoreLahore54000Pakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan

Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network ***,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one *** of the main requirements in a VANET is to provide self-decision capability to the *** memory,which stores all the previous routes,is used by the vehicles to choose the optimal *** networks,communication is *** cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each *** are allocated in a distributed manner,which is known as Mode 4 *** support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this *** k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 *** results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users.

关键词： Mode-4 communication ad-hoc vehicular network CV2X support vector machine

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Applications in Home Improvement Retailer, Koctas 2

Applications in Home Improvement Retailer, Koctas

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Mourad, Nahia Raza, Hammad Amjad, Muhamamd Ullah, Muhammed Ubaid Sabri Elmitwally, Nouh University in Dubai College of Engineering & IT British Dubai United Arab Emirates College of Business Administration and Economics Department of Computer Science National Lahore Pakistan NCBA&E Lahore School of Computer Science Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Minhaj University Lahore Pakistan Birmingham City University School of Computing and Digital Technology BirminghamB4 7XG United Kingdom Cairo University Faculty of Computers and Artifcial Intelligence Department of Computer Science Giza12613 Egypt

ISBN: (纸本)9798350335644

It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possible. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like ***, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective ***, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry. © 2023 IEEE.

关键词： Video cameras

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