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Congress on Evolutionary Computation

作者： Andrea Tangherloni Vasco Coelho Francesca M. Buffa Paolo Cazzaniga Department of Computing Sciences Bocconi Institute for Data Science and Analytics Bocconi University Milan Italy Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Computing Sciences Bocconi University Milan Italy Bocconi Institute for Data Science and Analytics Department of Human and Social Sciences University of Bergamo Bergamo Italy

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Evolutionary algorithms are effective techniques for optimizing non-linear and complex high-dimensional problems. However, most of them require a precise fine-tuning of their functioning settings to achieve satisfactory results. In this work, we propose a modified version of an evolutionary approach called the Evolutionary Algorithm for COmplex-process oPtimization (EACOP), designed to have a limited number of hyper-parameters. The base version of EACOP (bEACOP) combines different strategies, including the scatter search methodology, local searches, and a novel combination method based on path relinking to balance the exploration and exploitation phases. Our improved version (iEACOP) intensifies the exploration phase to escape from suboptimal search space areas where, on the contrary, bEACOP gets stuck. Our results show that iEACOP outperforms bEACOP on 27 out of 29 CEC 2017 test suite benchmark functions, exhibiting comparable performance against the three best algorithms of the CEC 2017 competition on single-objective bound-constrained real-parameter numerical optimization. The source code of bEACOP and iEACOP will be made publicly available on GitHub upon acceptance.

关键词： Parameter estimation Limiting Source coding systems biology Evolutionary computation Benchmark testing Search problems

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A Review of the Effectiveness of Management Information System in Decision Making 1

A Review of the Effectiveness of Management Information Syst...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Zeidan Al-Kaseasbeh, Hazem Mohammad Wahsheh, Firas Rashed Mohamad, Fatma Susilawati Jusoh, Julaily Aida Farea, Mazen Mohammed Archana, V.K. Irbid National University Faculty of Business and Financial Science MIS Bi and Data Analysis Department Irbid Jordan Ajloun National University Faculty of Business Department of Management Information Systems Ajloun Jordan UniSZA Faculty of Informatics and Computing Malaysia Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The rapid advancement of technology has revolutionized the way organizations operate, resulting in the widespread adoption of Management Information systems (MIS) as a crucial tool in decision-making processes. This research paper presents a comprehensive review of the effectiveness of Management Information systems in facilitating decision-making within various organizational contexts. The paper begins with an exploration of the fundamental role of MIS in collecting, processing, and presenting relevant data to decision-makers. Emphasis is placed on understanding how MIS enables the integration of data from diverse sources and transforms it into actionable insights, empowering managers to make informed and timely decisions. Furthermore, this review delves into the different types of Management Information systems, including Executive Information systems (EIS), Decision Support systems (DSS), and Transaction Processing systems (TPS). By analyzing case studies and real-world examples, the paper highlights the specific applications and benefits of each system type in enhancing decision-making efficiency and accuracy. To assess the effectiveness of MIS in decision-making, the review examines key performance indicators, such as the speed and accuracy of decision-making processes, resource optimization, and overall organizational performance. The role of MIS in improving strategic planning, risk management, and competitive advantage is also thoroughly investigated. Additionally, the review explores potential challenges and limitations associated with the implementation and utilization of MIS in decision-making. Factors such as data security, system reliability, user training, and resistance to change are addressed, offering insights into strategies for mitigating these obstacles. The paper concludes with a synthesized analysis of the existing literature and identifies gaps for future research in the field of Management Information systems and decision-making effectiveness

关键词： Decision support systems

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Bibliometric Analysis of Trend in Metaverse Research

Bibliometric Analysis of Trend in Metaverse Research

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International Conference on Information Management and Technology (ICIMTech)

作者： Cadelina Cassandra Mohamad Noorman Masrek Fadhilah Aman Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia School of Information Science College of Computing Informatics and Media University Teknologi MARA Shah Alam Selangor MALAYSIA

Metaverse is currently going to be a popular topic. This study aims to investigate the research trends in metaverse topics to provide a new point of view for future research direction. This study performed a bibliometric analysis systematic literature review by reviewing the specific term in the Scopus database. The data were obtained from publish or perish app and analyzed using the VOSviewer Software. This research article was limited from 2018 - 2022, and 119 were analyzed using the VOSViewer Software. The result shows that the metaverse topic is increasing very fast in 2022. The most popular term in the title and abstract combination are Experience, Study, Application, and Education, which indicates the opportunity for future research, trend, and topic that the public and researchers are interested in. Meanwhile, the keyword of a metaverse in education is still limited, which opens new opportunities to be discussed in the future.

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Active Learning-Based Mobile Malware Detection Utilizing Auto-Labeling and Data Drift Detection

Active Learning-Based Mobile Malware Detection Utilizing Aut...

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

作者： Zhe Deng Arthur Hubert Sadok Ben Yahia Hayretdin Bahsi Department of Software Science Tallinn University of Technology Estonia Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg School of Informatics Computing and Cyber Systems Northern Arizona University USA

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

Machine learning-based detection methods have demonstrated high performance in mobile malware detection. However, changes in mobile malware over time induce a sig-nificant challenge for malware detection systems running in operational environments. The development of non-stationary models to address concept drift in the threat landscape has garnered significant research interest. Auto-labeling is using automated algorithms to assign labels to data without manual intervention. This study explores the method for introducing auto-labeling in active learning with data drift detection. It achieves satisfying results for the lowest possible cost while succeeding in adapting to changes in the data for a long period.

关键词： Training Adaptation models Costs Training data Prediction methods Manuals Prediction algorithms

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Multiomics Reveals Induction of Neuroblastoma SK-N-BE(2)C Cell Death by Mitochondrial Division Inhibitor 1 through Multiple Effects

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Journal of Proteome Research 2024年第1期23卷 301-315页

作者： Wang, Wei-Hsuan Kao, Yi-Chun Hsieh, Chiao-Hui Tsai, Shin-Yu Cheung, Chantal Hoi Yin Huang, Hsuan-Cheng Juan, Hsueh-Fen Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei 106 Taiwan Department of Life Science National Taiwan University Taipei 106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei 106 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 112 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106 Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei 106 Taiwan

Mitochondrial division inhibitor 1 (Mdivi-1) is a well-known synthetic compound aimed at inhibiting dynamin-related protein 1 (Drp1) to suppress mitochondrial fission, making it a valuable tool for studying mitochondrial dynamics. However, its specific effects beyond Drp1 inhibition remain to be confirmed. In this study, we employed integrative proteomics and phosphoproteomics to delve into the molecular responses induced by Mdivi-1 in SK-N-BE(2)C cells. A total of 3070 proteins and 1945 phosphorylation sites were identified, with 880 of them represented as phosphoproteins. Among these, 266 proteins and 97 phosphorylation sites were found to be sensitive to the Mdivi-1 treatment. Functional enrichment analysis unveiled their involvement in serine biosynthesis and extrinsic apoptotic signaling pathways. Through targeted metabolomics, we observed that Mdivi-1 enhanced intracellular serine biosynthesis while reducing the production of C24:1-ceramide. Within these regulated phosphoproteins, dynamic dephosphorylation of proteasome subunit alpha type 3 serine 250 (PSMA3-S250) occurred after Mdivi-1 treatment. Further site-directed mutagenesis experiments revealed that the dephosphorylation-deficient mutant PSMA3-S250A exhibited a decreased cell survival. This research confirms that Mdivi-1’s inhibition of mitochondrial division leads to various side effects, ultimately influencing cell survival, rather than solely targeting Drp1 inhibition. © 2023 American Chemical Society.

关键词： Mdivi-1 multiomics neuroblastoma phosphoproteome proteome targeted metabolome

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A Composite T60 Regression and Classification Approach for Speech Dereverberation

arXiv

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arXiv 2023年

作者： Li, Yuying Liu, Yuchen Williamson, Donald S. The Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States The Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States The Department of Computer Science and Engineering The Ohio State University ColumbusOH43065 United States

Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time, T60, however, is an essential acoustic factor that reflects how reverberation may impact a signal. In this work, we propose to perform dereverberation while leveraging key acoustic information from the environment. More specifically, we develop a joint learning approach that uses a composite T60module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation. The reverberation time module provides key features to the dereverberation module during fine tuning. We evaluate our approach in simulated and real environments, and compare against several approaches. The results show that this composite framework improves performance in environments. © 2023, CC BY.

关键词： Deep neural networks

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Analyzing Deep Learning Techniques in Natural Scene Image Classification

Analyzing Deep Learning Techniques in Natural Scene Image Cl...

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IEEE International Conference on Big Data

作者： Athanasios Kanavos Orestis Papadimitriou Khalil Al-Hussaeni Ioannis Karamitsos Manolis Maragoudakis Department of Information and Communication Systems Engineering University of the Aegean Samos Greece Computing Sciences Department Rochester Institute of Technology Dubai UAE Graduate and Research Department Rochester Institute of Technology Dubai UAE Department of Informatics Ionian University Corfu Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Image classification is a fundamental task in computer vision, with wide applications including autonomous navigation, content recommendation, and environmental monitoring. This paper offers a comprehensive comparative analysis of deep learning techniques using the Intel Natural Scenes Image dataset, which features diverse scenes such as forests, mountains, seas, streets, buildings, and glaciers. Our study evaluates the performance of various Convolutional Neural Network (CNN) architectures, focusing on aspects such as network design, hyperparameters, data augmentation, and transfer learning strategies. We describe our experimental setup in detail, including the CNN models used, preprocessing techniques applied, and evaluation metrics employed. The results and discussions present key findings, highlighting the strengths and limitations of the approaches studied and providing guidance for future research and practical applications. Our systematic analysis yields valuable insights into effective strategies for recognizing natural scenes.

关键词： Deep learning Measurement Systematics Image recognition Forests Transfer learning Focusing Glaciers Convolutional neural networks Image classification

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A Cross-Organizational Identity Proofing System for Seamless Online ID Re-Binding Leveraging Individual Number Cards

A Cross-Organizational Identity Proofing System for Seamless...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Sayako Shimizu Hiroyuki Sato Motonori Nakamura Hukofumi Suzuki Yasuo Okabe Information Systems Architecture Science Research National Institute of Informatics Tokyo Japan Institute for Information Management and Communication Kyoto University Kyoto Japan Cyber Science Infrastructure Development Department Academic Authentication Systems Office National Institute of Informatics Tokyo Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This study focuses on the issue of ID management when researchers change their organizational affiliations, aiming at the effective utilization of research data in educational and research organizations. Although research data management systems are being developed at each organization, the continuous use of research data remains an issue for researchers. For example, when a researcher moves from one organization to another, the research data that was previously available to him or her becomes unavailable. In response, there is a need for a system that allows researchers to continue using their research data smoothly even if they move from one organization to another. To solve this problem, the authors propose a cross-organizational identity proofing system that ensures the continuity of IAL2/ AAL2 and completes the ID transfer online. The idea is to use researcher ID numbers, ORCID, and public personal authentication to verify the user's identity when linking the IDs before and after the transfer. To facilitate this, the study performs identity verification using an Individual Number Card (a Japanese governmental system) to enable easy ID linkage between IdPs with high identity confirmation and authentication strength. The implementation has been carried out in the development environment of Orthros, an ID linkage service provided by the National Institute of informatics (NIl), utilizing the API service of xID Co., which is a solution for linking with the Individual Number Card. This approach is expected to overcome ID management issues associated with researcher transfers and improve the efficiency of research data use.

关键词： Couplings Digital transformation Employment Education Authentication Organizations Software

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Dynamic Programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors 1

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7th International Conference on Computational Science and Technology, ICCST 2020

作者： Lee, Wing Shum Ng, Hu Yap, Timothy Tzen Vun Ho, Chiung Ching Goh, Vik Tor Tong, Hau Lee Faculty of Computing & Informatics Multimedia University Cyberjaya63100 Malaysia Department of Computing and Information Systems Sunway University Petaling Jaya47500 Malaysia Faculty of Engineering Multimedia University Cyberjaya63100 Malaysia

ISBN: (数字)9789813340695

ISBN: (纸本)9789813340688

In this research, we look at the notions of objectivity and subjectivity and create word embeddings from them for the purpose of sentiment analysis. We created word vectors from two datasets, the Wikipedia English Dataset for objectivity and the Amazon Product Reviews Data dataset for subjectivity. A model incorporating an Attention Mechanism was proposed. The proposed Attention model was compared to Logistic Regression, Linear Support Vector Classification models, and the former was able to achieve the highest accuracy with large enough data through augmentation. In the case of objectivity and subjectivity, models trained with the objectivity word embeddings performed worse than their counterpart. However, when compared to the BERT model, a model also with Attention Mechanism but has its own word embedding technique, the BERT model achieved higher accuracy even though model training was performed with only transfer learning. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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