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Multi-objective parameter-less population pyramid in solving the real-world and theoretical problems 21

Multi-objective parameter-less population pyramid in solving...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Przewozniczek, Michal W. Dziurzanski, Piotr Zhao, Shuai Indrusiak, Leandro S. Dep. of Computational Intelligence Wroclaw University of Science and Technology Wroclaw Poland Fac. of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland University of York Department of Computer Science York United Kingdom

ISBN: (纸本)9781450383516

Many real-world problems are notoriously multi-objective and NP-hard. Hence, there is a constant striving for optimizers capable of solving such problems effectively. In this paper, we examine the Multi-Objective Parameter-less Population Pyramid (MO-P3). MO-P3 is based on the Parameter-less Population Pyramid (P3) that was dedicated to solving single-objective problems. P3 employs linkage learning to decompose the problem and uses this information during its run. P3 maintains many different linkage information sets, which is the key to effectively solve the problems of the overlapping nature, i.e., the problems whose variables form a large and complicated network of dep.ndencies rather than additively separable blocks. MO-P3 inherits the features of its predecessor and employs both linkage learning and linkage diversity maintenance to effectively solve hard multi-objective problems, which includes both: well-known test problems and NP-hard real-world problems. © 2021 Owner/Author.

关键词： Genetic algorithms

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The Correlation Between architecture Smells, Package Stability and Reusability: A Case Study

The Correlation Between Architecture Smells, Package Stabili...

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Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mrwan BenIdris Ehab A. Omar Elfallah Marwa Aslam Cheema Mohammed Salah Addin Alsma Tareq Ziad Kadiki Abdelfattah Elbarsha Dep. of Computer Science Faculty of Information Technology University of Benghazi Dept. of Software Engineering Faculty of Information Technology Libyan International Medical University Health Informatics Department Faculty of Public Health University of Benghazi

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dep.h research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic dep.ndency and Unstable dep.ndency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable dep.ndency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture Software systems Software development management

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Khean-Thye Bea Shih-Shin Hu Wei-Hsuan Lin Da-Zheng Lin Hsin-Hui Hu Xiu-Zhi Chen Ting-Ru Lin Yen-Lin Chen Kun-Ming Chen Wai Khuen Cheng Dep. of Electronic Engineering National Taipei University of Technology Taipei Taiwan Dep. of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Taiwan Semiconductor Research Institute Hsinchu Taiwan Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation.

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Graph Attention Networks and Deep Q-Learning for Service Mesh Optimization: A Digital Twinning Approach

Graph Attention Networks and Deep Q-Learning for Service Mes...

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IEEE International Conference on Communications (ICC)

作者： Michel Gokan Khan Javid Taheri Andreas Kassler Arsineh Boodaghian Asl Dep. of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Electronics Electrical Eng. and Computer Science Queen's University Belfast Belfast United Kingdom Faculty of Computer Science Deggendorf Institute of Technology Deggendorf Germany School of Eng. Sciences in Chemistry Biotech. and Health KTH Royal Inst. of Technology Stockholm Sweden

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In the realm of cloud native environments, Ku-bernetes has emerged as the de facto orchestration system for containers, and the service mesh architecture, with its interconnected microservices, has become increasingly prominent. Efficient scheduling and resource allocation for these microservices play a pivotal role in achieving high performance and maintaining system reliability. In this paper, we introduce a novel approach for container scheduling within Kubernetes clusters, leveraging Graph Attention Networks (GATs) for representation learning. Our proposed method captures the intricate dep.ndencies among containers and services by constructing a representation graph. The deep Q-learning algorithm is then employed to optimize scheduling decisions, focusing on container-to-node placements, CPU request-response allocation, and adherence to node affinity and anti-affinity rules. Our experiments demonstrate that our GATs-based method outperforms traditional scheduling strategies, leading to enhanced resource utilization, reduced service latency, and improved overall system throughput. The insights gleaned from this study pave the way for a new frontier in cloud native performance optimization and offer tangible benefits to industries adopting microservice-based architectures.

关键词： Industries Cloud computing Job shop scheduling Q-learning Microservice architectures computer architecture Containers

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Sorting Red and Green Chilies by Digital Image Processing

Sorting Red and Green Chilies by Digital Image Processing

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Cybernetics and Innovations (ICCI), International Conference on

作者： Narumol Chumuang Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Jiratha Chongsri Worawut Yimyam Dep. of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand Dep. of Information Technology Management King Mongkut's University of Technology North Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Dep. of Business Information Management Phetchaburi Rajabhat University Thailand

The purpose of this research is to help save electricity within organizations and workplaces that use computers. Due to the behavior of general users today, they do not give importance to saving electricity. This work has developed an automatic computer shutdown system. with image processing via a webcam by using image processing techniques (image processing) applied to a webcam by using a webcam to capture the user on the computer screen. Then the images are analyzed and examined with the principles of image processing with the face detection technique using the algorithm of Haar -like features to detect human faces and ROI. define a specific area of interest. The system will then check the amount of inactivity on the screen. or does not show the face image within 10 minutes, the system will automatically turn off the computer. with image processing. This work can make it easier to turn off computers when not in use. and helps to save electricity for the organization or agencies as well.

关键词： computers Technological innovation Webcams Digital images Employment Organizations Feature extraction

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Deep Learning technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Bheemalingaiah, M. Srinivasan, R. Nathan, V. Vijey Kolluri, Raju Gupta, Amit J.B Institute of Engineering and Technology Department of CSE Telangana Hyderabad India School of Computer Science and Engineering Galgotia University Department of CSE U.P India SRM TRP Engineering College Department of CSE Tamilnadu Tiruchirappalli India Narasaraopet Engineering College Department of ECE Andhra Pradesh Narasaraopet India J.B Institute of Engineering and Technology Dep. of AI & ML Telangana Hyderabad India

ISBN: (纸本)9798350378092

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology. © 2024 IEEE.

关键词： Convolutional neural networks

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Turksent: A sentiment annotation tool for social media 7th

Turksent: A sentiment annotation tool for social media

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7th Linguistic Annotation and Interoperability with Discourse Workshop, LAW 2013 and ID 2013

作者： Eryiǧit, Gülşen Temel, Tanel Çetin, Fatih Samet Yanık, Meltem Çiçekli, Ilyas Dep. of Computer Eng. Istanbul Technical University Turkey Dep. of Information Technology Turkcell Global Bilgi Turkey Dep. of Computer Eng. Hacettepe University Turkey

ISBN: (纸本)9781937284589

In this paper, we present an annotation tool developed specifically for manual sentiment analysis of social media posts. The tool provides facilities for general and target based opinion marking on different type of posts (i.e. comparative, ironic, conditional) with a web based UI which supports synchronous annotation. It is also designed as a SaaS (Software as a Service). The tool's outstanding features are easy and fast annotation interface, detailed sentiment levels, multi-client support, easy to manage administrative modules and linguistic annotation capabilities. © 2013 Association for Computational Linguistics

关键词： Sentiment analysis

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An Enhanced Method for Detecting Attack in Collaborative Recommender System

An Enhanced Method for Detecting Attack in Collaborative Rec...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Reda A. Zayed Hesham A. Hefny Lamiaa F. Ibrahim Hesham A. Salman Computer Scinces Dep. Faculty of Graduate Studies for Statistical Research Cairo University Cairo Egypt Dean of College of Informatics Everyone’s Smart University Management Information system Dep. Higher Institute for Computers & Information Technology ElShorouk Cairo Egypt

In recent decades, the advent of digital information services by YouTube, Amazon, Netflix, and many other web services of this kind have made recommendation systems more and more ubiquitous in our lives. rice field. The system suggests and recommends articles to the user that may interest the user in online advertising (recommending and suggesting appropriate content to the user that matches the user’s tastes and activities). Recommendation systems have become an integral part of our daily online journeys. The quality of predictions is degraded by the attackers by injection of fake profiles. therefore, the shilling attacks detection are necessary. thus, various shilling attacks detection techniques proposed. In this paper, we introduce an enhanced technique for detecting shilling attacks in collaborative recommender system using supervised learning techniques. The proposed method results show that getting better accuracy when we employee ensemble learning algorithm.

关键词： Technological innovation Video on demand Web services Smart cities Supervised learning Collaboration Information services

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Automatic computer Shutdown with Image Processing via Webcam to Save Energy

Automatic Computer Shutdown with Image Processing via Webcam...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Narumol Chumuang Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Worawut Yimyam Chuamoo Dep. of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand Dep. of Information Technology Management King Mongkut's University of Technology North Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Dep. of Business Information Management Phetchaburi Rajabhat University Thailand

The purpose of this research is to help save electricity within organizations and workplaces that use computers. Due to the behavior of general users today, they do not give importance to saving electricity. The researcher has developed an automatic computer shutdown system. with image processing via a webcam By using image processing techniques (Image Processing) applied to a webcam. By using a webcam to capture the user on the computer screen. Then the images are analyzed and examined with the principles of image processing with the Face Detection technique using the algorithm of Haar-like Features to detect human faces and ROI. Define a specific area of interest The system will then check the amount of inactivity on the screen. or does not show the face image within 10 minutes, the system will automatically turn off the computer. with image processing This research can make it easier to turn off computers when not in use. and helps to save electricity for the organization or agencies as well.

关键词： computers Performance evaluation Technological innovation Webcams Organizations Inspection Feature extraction

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The search of the path MTU with QUIC

The search of the path MTU with QUIC

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2021 Workshop on the Evolution, Performance and Interoperability of QUIC, EPIQ 2021 - Part of CoNEXT 2021

作者： Völker, Timo Tüxen, Michael Rathgeb, Erwin P. FH Münster Uni. of Appl. Sciences Dep. of Electrical Engineering and Computer Science NRW Steinfurt Germany University of Duisburg-Essen Computer Networking Technology Group NRW Essen Germany

ISBN: (纸本)9781450391351

A data sender in an IP based network is only capable to efficiently use a network path if it knows the packet size limit of the path, i.e., the Path Maximum Transmission Unit (PMTU). The IETF recently specified a PMTU discovery framework for transport protocols like QUIC. This paper complements this specification by presenting a search algorithm. In addition, it defines several metrics and shows results of analyses for the algorithm with various PMTU candidate sequences using these metrics. We integrated the PMTU discovery with our algorithm into a QUIC simulation model. This paper describes the integration and presents measurements obtained by simulations. © 2021 ACM.

关键词： Learning algorithms

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