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computers, Materials & Continua 2024年第7期80卷 1203-1233页

作者： Jia Guo JinWang Ke Yan Qiankun Zuo Ruiheng Li Zhou He Dong Wang Yuji Sato Hubei Key Laboratory of Digital Finance Innovation Hubei University of EconomicsWuhan430205China School of Information Engineering Hubei University of EconomicsWuhan430205China China Construction Third Engineering Bureau Installation Engineering Co. Ltd.Wuhan430074China Hubei Internet Finance Information Engineering Technology Research Center Hubei University of EconomicsWuhan430205China College of Informatics Huazhong Agricultural UniversityWuhan430070China Faculty of Computer and Information Sciences Hosei UniversityTokyo184-8584Japan

Complex optimization problems hold broad significance across numerous fields and ***,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also *** address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population *** two strategies:community search and community *** community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm ***,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm *** assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test *** the CEC2017 test suite,WGOA secures first place in 10 *** the CEC2022 benchmark functions,WGOA obtained the first rank in 5 *** ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested *** underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.

关键词： Complex optimization wild gibbon optimization algorithm community search community competition

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins 27

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Diamantopoulos, Georgios Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios School of Computer Science University of Birmingham Birmingham United Kingdom University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350337846

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system. © 2023 IEEE.

关键词： Blockchain

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Improving Norwegian Power Transmission Flexibility Using a New Matheuristic Optimization Framework 5

Improving Norwegian Power Transmission Flexibility Using a N...

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5th International Conference on Clean and Green Energy engineering, CGEE 2024

作者： Meland, Sander Yousefi, Mojtaba Hemmati, Ahmad Bojesen, Troels University of Bergen and Equinor Department of Informatics Stavanger Norway Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway University of Bergen Department of Informatics Bergen Norway

ISBN: (纸本)9798350377491

There is an increasing demand for transmission system operators to expand their participation in the electricity reserve markets, encompassing smaller businesses and house-holds that are currently excluded due to market constraints. These smaller entities require an aggregator's assistance to pool the loads of several minor actors, enabling their participation in the markets through an aggregator that can optimize bids. This paper introduces a matheuristic framework aimed at maximizing revenue in the Norwegian power reserve markets, showcasing the potential effectiveness of aggregators. The framework is utilized to analyze the optimal bidding strategies for an aggregator within the Norwegian Reserve Markets over two distinct time periods, using real market prices, flexible power capacity, and customer consumption data. The heuristics are evaluated and the outcomes are compared with those of a mathematical model. The matheuristic models achieved results ranging from 88-94 % relative to the corresponding mathematical model. © 2024 IEEE.

关键词： Aggregators Demand side flexibility Matheuristics Power reserve market

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Improving SVM Performance in Determining Cost and Kernel Parameter Values Through Grid Search

Improving SVM Performance in Determining Cost and Kernel Par...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Visca Sylvia Maya Silvi Lydia Benny Benyamin Nasution Master of Informatics Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

The purpose of this study is to improve the performance of Support Vector Machine (SVM) algorithm in sentiment analysis of trainee reviews through parameter optimization using Grid Search. Trainee reviews were taken from the Vocational and Productivity Training Center (BBPVP) Medan, which is the context of this study. The method used includes collection and preprocessing of review data, followed by the application of SVM for sentiment analysis. Grid Search is used to optimize the cost and kernel parameters of SVM. The results show that after optimization, the resulting SVM model shows significant performance improvement compared to the model before optimization. Accuracy increased from 68.75% to 71.96%, with improvements in other metrics such as precision, recall, and F1 score. These findings suggest that Grid Search is an effective method to optimize SVM parameters in sentiment analysis, providing a more accurate evaluation of the effectiveness of training programs at BBPVP Medan. This research makes an important contribution by applying hyperparameter optimization to SVM and suggests the application of this method for similar analysis in the future.

关键词： Support vector machines Training Productivity Sentiment analysis Analytical models Accuracy Costs Reviews Kernel Optimization

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Mobile Device Facial Beauty Prediction using Convolutional Neural Network as Makeup Reference

Mobile Device Facial Beauty Prediction using Convolutional N...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Luthfi, Muhammad Rachmadi, Reza Fuad Purnama, I Ketut Eddy Nugroho, Supeno Mardi Susiki Institut Teknologi Sepuluh Nopember Faculty of Electrical and Intellignet Informatics Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Beauty or good looks is one aspect of human attraction. Highly attractive people have an 8 to 12 % higher success in negotiations. One of the things that can be used to increase attractiveness is makeup or cosmetics. Makeup can be used alone or through the services of a Makeup Artist (MUA). MUA services are often used during important and once-in-A-lifetime events such as weddings. The difference in beauty before and after using this makeup cannot be measured but only seen and assessed manually. One application of machine learning in this field is Facial Beauty Predictions (FBP). FBP measures the value of a person's facial beauty or good looks. This paper investigated and implemented FBP on mobile devices, which makes it easier to assess anytime and anywhere. We investigated FBP models using five CNN architectures, including EfficientNetB0, VGG-16, ShuffleNet, MobileNet, and Inception. After evaluation, we choose MobileNetV2 CNN architecture as the primary model for mobile application FBP. Experiments on the FBP dataset show that MobileNetV2 achieves Pearson Correlation 0.7893 with only 10.5 MB for model file size. The model implemented for mobile devices works well in 13 different tests. © 2022 IEEE.

关键词： Forecasting

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Improving Information Freshness in Edge-Assisted Smart Grids: An AoI-Aware Routing Strategy for Neighborhood Area Networks 22

Improving Information Freshness in Edge-Assisted Smart Grids...

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Farag, Hossam Kotb, Mostafa Stefanović, Čedomir Gidlund, Mikael Department of Electronic Systems Aalborg University Denmark Department of Informatics Universität Hamburg Germany Department of Electrical Engineering Aswan University Egypt Mathematics Department Aswan University Egypt Department of Computer and Electrical Engineering Mid Sweden University Sweden

ISBN: (纸本)9798331527471

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high responsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL. © 2024 IEEE.

关键词： Edge computing

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On the Difficulty of NOT being Unique: Fingerprinting Users from Wi-Fi Data in Mobile Devices 25

On the Difficulty of NOT being Unique: Fingerprinting Users ...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Cunha, Mariana Mendes, Ricardo De Montjoye, Yves-Alexandre Vilela, João P. CRACS/INESC TEC CISUC Department of Computer Science Faculty of Sciences University of Porto Porto Portugal CISUC and Department of Informatics Engineering University of Coimbra Coimbra Portugal Imperial College London London United Kingdom

ISBN: (纸本)9798400706295

The pervasiveness of mobile devices has fostered a multitude of services and applications, but also raised serious privacy concerns. In order to avoid users' tracking and/or users' fingerprinting, smartphones have been tightening the access to unique identifiers. Nevertheless, smartphone applications can still collect diverse data from available sensors and smartphone resources. Using real-world data from a field study we performed, this paper demonstrates the possibility of fingerprinting users from Wi-Fi data in mobile devices and the consequent privacy impact. From the performed analysis, we concluded that a single snapshot of a set of scanned Wi-Fi BSSIDs (MAC addresses) per user is enough to uniquely identify about 99% of the users. In addition, the most frequent Wi-Fi BSSID is sufficient to re-identify more than 90% of the users, a percentage that goes up to 97% of the users with the top-2 scanned BSSIDs. The Wi-Fi SSID (network name) also leads to a re-identification risk of about 83% and 97% with 1 and 2 of the strongest Wi-Fi Access Points (APs), respectively. Copyright © 2025 held by the owner/author(s).

关键词： privacy

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An Attention-Based Deep Network for Plant Disease Classification

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Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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Designing Secured Cafe Network with Security Awareness Domain and Resource (SADAR) by Simulation using Cisco Packet Tracer 22

Designing Secured Cafe Network with Security Awareness Domai...

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10th International Conference on computer and Communications Management, ICCCM 2022

作者： Lubis, Muharman Lubis, Arif Ridho Department of Information System Telkom University Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Indonesia

ISBN: (纸本)9781450396349

computer networking has a huge part of life today, which its participation not only in one side of life but in almost all aspects of life, especially in the fields of education, organization and business. The purpose of a computer network related to connecting every device for providing information and data services through the borderless concept of anytime, anywhere and anyone. On the other hand, the use of a network in the business sector is very critical and fundamental to assist the implementation in the respect of accelerate growth and reducing cost. In this case, the network in the cafe not only become the secondary service to the customer but also enable the business to be sustained, which many customers browse and utilize Internet during their visit, also cafe's effort to broaden the range of its business. Despite, the accessibility become widespread to the network, but at some point, the demand for Internet access in cafe such as for gaming, accompanying and streaming remain huge. Thus, this study want to design a computer network using the Cisco Packet Tracer software through the concept of SADAR (security awareness domain and resource) in order to increase security compliance. This study also utilized and configure VLAN and DHCP connected to gigabit Ethernet as standard, while the testing will used PING and TRACEROUTE in order for mapping as desired. © 2022 ACM.

关键词： Network management

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Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Process (HDP) Models on News Data 5

Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Pr...

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5th IEEE International Conference on computer and informatics engineering, IC2IE 2022

作者： Lubis, Arif Ridho Prayudani, Santi Fatmi, Yulia Nugroho, Okvi Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Sma Muhammadiyah 2 Medan Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

News has become a very important need in modern society. Almost every level of society needs information such as news. Online news gets the attention of writers because there are a lot of characteristics of the data available and can be used for analysis so as to get insights and current trends, such as on 4 news portals that are most frequently accessed by the public such as ***, ***, ***, and ***. Related research in this field about Trend Analysis and Topic modeling on news media data in Indonesia then discusses news data collection methods and data collection techniques news pre-processing text, and 2 Topic Modeling algorithms applied in this research, namely Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Process (HDP). In this study, the author has succeeded in extracting topics from news data from media in Indonesia. Various topics show the effectiveness of a learning model producing topics from the LSI and HDP models. This research has implemented natural language preprocessing (NLP) to process news data so that it can be processed and used as data from modeling a topic. This study finds topics based on the model of coherence and LSI and HDP contained in the news data with a total of 29,437. The accuracy obtained is 87% compared to the model from LDA-KNN which can determine the topic with an accuracy of 72%. Hence, the Latent Semantic Indexing (LSI) model and Hierarchical Dirichlet Process (HDP) have better accuracy than the LDA-KNN model. © 2022 IEEE.

关键词： Semantics

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