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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Andi Sama Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

The information technology/Operational technology convergence towards Industry 4.0 opens the opportunity to leverage recent advancements in information technology for Operational technology, such as Cloud, Internet of Things, and Artificial Intelligence. Meanwhile, cyber-attacks are increasing for Operational technology. The security aspect in Operational technology systems has traditionally been a low priority, in contrast with speed. This introduces challenges as Industrial Control Systems such as programmable Logic Controller in Operational technology have been traditionally optimized for speed rather than security due to limited computing power. As computations need to be as efficient as possible, security in Operational technology has yet to be managed as robustly as in information technology. Operational technology communication protocols for securing data transfer, for example, have no or just a few security capabilities, even for basic authentication and encryption. Common security algorithms rely on random numbers. However, Random Number Generator is not usually part of standard functions in programmable Logic Controllers, the core control component in Industrial Control Systems. In Industrial Control Systems, the Random Number Generator is mostly implemented as a software-based Pseudo Random Number Generator. This paper shows how to apply a Pseudo Random Number Generator in a Siemens Compact PLC S7-1200 using a modified lightweight XORshift algorithm. The XORshift algorithm can generate better randomness than the system’s clock-based implementation in the Siemens Library of Generic Functions.

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Identifying Important Usernames in 'Biofarma' Conversation using Twitter Network Analysis 7

Identifying Important Usernames in 'Biofarma' Conversation u...

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7th International Conference on information Management and technology, ICIMTech 2022

作者： Rahutomo, Reza Jariyah, Ainun Trinugroho, Joko Pebrianto Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

This study analyzed interactions between Twitter users in conversations regarding Indonesia's state-owned vaccine manufacturer 'Biofarma' in 2021. The primary objective of this study is to identify Key Opinion Leaders (KOL) due to the need of authentic and trustworthy persona in social media. A technique called network centrality was employed in this research alongside four centrality measurements, namely degree of centrality, eigenvector centrality, betweenness centrality, and closeness centrality. Focused on using a reply network interaction dataset, the research discovered several important usernames with different roles that enlivened the conversation and have a potential for social media marketing optimization. © 2022 IEEE.

关键词： Marketing

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Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

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computers, Materials & Continua 2021年第8期68卷 1575-1593页

作者： Ayat Alrosan Waleed Alomoush Mohammed Alswaitti Khalid Alissa Shahnorbanun Sahran Sharif Naser Makhadmeh Kamal Alieyan Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Computer Department Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia School of Electrical and Computer Engineering Department of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Center for Artificial Intelligence Technology(CAIT) Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiMalaysia School of Computer Sciences Universiti Sains Malaysia11800PenangMalaysia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmmanJordan

Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine *** main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster *** Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of ***,ABC has some weaknesses,such as balancing exploration and *** improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is ***,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of *** experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.

关键词： Artificial bee colony automatic clustering natural images validity index number of clusters

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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The technology behind Genomic Database 4

The Technology behind Genomic Database

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Trinugroho, Joko Pebrianto Perbangsa, Anzaludin Samsinga Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

In this digital era, we are exposed to a large amount of data. This includes biological data, which stores information about living organisms, including Deoxyribonucleic acid (DNA), genes, and proteins. With the development of information technology and information system, most of available biological data are stored in an online public database. Many of the databases are free-access and easily used, which helps the users, especially researchers, to make use of the data. Among the known public biological databases are the University of California Santa Cruz (UCSC) Genome Browser Database and the Rat Genome Database (RGD). These two databases provide access to the biological data from different organisms. This paper aims to describe the technology of public biological databases. Also elucidated in this paper are the differences features between UCSC Genome Browser Database and the RGD. Our results showed that the UCSC contains much more biological data and features than the RGD. However, the genome browser of UCSC has a complex display, while the RGD has a simple display. Overall, both databases give the users the option to choose the most suitable database for them. © 2022 IEEE.

关键词： Rats

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A Deep Learning Driven Feature Based Steganalysis Approach

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Intelligent Automation & Soft Computing 2023年第8期37卷 2213-2225页

作者： Yuchen Li Baohong Ling Donghui Hu Shuli Zheng Guoan Zhang College of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China College of Information Engineering Anhui Broadcasting Movie and Television CollegeHefei230011China Department of Informatics Faculty of Natural&Mathematical SciencesKing’s College LondonLondonWC2R2LSUK

The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic *** traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm,whose detection performance drops rapidly when it is applied to detect another type of steganographic *** phenomenon is called as steganographic algorithm mismatch in *** resolve this pro-blem,we propose a deep learning driven feature-based *** advanced steganalysis neural network is used to extract steganographic features,different pairs of training images embedded with steganographic algorithms can obtain diverse features of each *** a multi-classiﬁer implemented as lightgbm is used to predict the matching *** results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.

关键词： Image steganalysis algorithm mismatch convolutional neural network JPEG images

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Protect Your Secrets: Understanding and Measuring Data Exposure in VSCode Extensions

Protect Your Secrets: Understanding and Measuring Data Expos...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Yue Liu Chakkrit Tantithamthavorn Li Li Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Engineering Beihang University China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Recent years have witnessed the emerging trend of extensions in modern Integrated Development Environments (IDEs) like Visual Studio Code (VSCode) that significantly enhance developer productivity. Especially, popular AI coding assistants like GitHub Copilot and Tabnine provide conveniences like automated code completion and debugging. While these extensions offer numerous benefits, they may introduce privacy and security concerns to software developers. However, there is no existing work that systematically analyzes the security and privacy concerns, including the risks of data exposure in VSCode extensions. In this paper, we investigate on the security issues of cross-extension interactions in VSCode and shed light on the vulner-abilities caused by data exposure among different extensions. Our study uncovers high-impact security flaws that could allow adversaries to stealthily acquire or manipulate credential-related data (e.g., passwords, API keys, access tokens) from other extensions if not properly handled by extension vendors. To measure their prevalence, we design a novel automated risk detection framework that leverages program analysis and natural language processing techniques to automatically identify potential risks in VSCode extensions. By applying our tool to 27,261 real-world VSCode extensions, we discover that 8.5 % of them (i.e., 2,325 extensions) are exposed to credential-related data leakage through various vectors, such as commands, user input, and configurations. Our study sheds light on the security challenges and flaws of the extension-in-IDE paradigm and provides suggestions and recommendations for improving the security of VSCode extensions and mitigating the risks of data exposure.

关键词： Productivity Visualization Privacy Codes Passwords Software Vectors Natural language processing Security Software development management

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Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

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computers, Materials & Continua 2022年第5期71卷 3969-3983页

作者： CSS Anupama T.J.Benedict Jose Heba FEid Nojood O Aljehane Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Faculty of Science AL-Azhar UniversityCairo11651Egypt Faculty of Computers and Information Technology University of Tabuk47512Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia&Faculty of Computer and ITSana’a University31220Yemen Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman University11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or *** image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,*** the same time,cervical cancer becomes a major reason for increased women’s mortality *** cervical cancer is an identified at an earlier stage using regular pap smear *** this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)*** BPSIC-CDF technique enables the IoT devices for pap smear image *** addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)***,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear ***,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear *** order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev *** experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

关键词： Biomedical imaging pap smear images internet of things deep learning cervical cancer disease diagnosis

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A Hybrid Deep Learning Method for Forecasting Reservoir Water Level from Sentinel-2 Satellite Images

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computers, Materials & Continua 2025年第6期83卷 4915-4937页

作者： Hoang Thi Minh Chau Tran Thi Ngan Nguyen Long Giang Tran Manh Tuan Tran Kim Chau Graduate University of Science and Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam Faculty of Information Technology University of Economics Technology for IndustriesHanoi100000Vietnam Artificial Intelligence Research Center VNU Information Technology InstituteVietnam National UniversityHanoi100000Vietnam International School Vietnam National UniversityHanoi100000Vietnam Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam Faculty of Computer Science and Engineering Thuyloi UniversityHanoi100000Vietnam Faculty of Water Resources Engineering Thuyloi UniversityHanoi100000Vietnam

Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water *** particular,the reservoir water level is an essential physical indicator for the *** the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management *** recent years,deep learning models have been widely applied to solve forecasting *** this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water *** utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir *** is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during *** implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in *** assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same *** model performances were validated using k-fold cross-validation and ANOVA *** experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared *** has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.

关键词： YOLOv9 ConvLSTM reservoir water level forecasting satellite images

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Exact Algorithms for Multiagent Path Finding with Communication Constraints on Tree-Like Structures 39

Exact Algorithms for Multiagent Path Finding with Communicat...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fioravantes, Foivos Knop, Dušan Křišťan, Jan Matyáš Melissinos, Nikolaos Opler, Michal Department of Theoretical Computer Science Faculty of Information Technology Czech Technical University in Prague Prague Czech Republic

ISBN: (纸本)157735897X

Consider the scenario where multiple agents have to move in an optimal way through a network, each one towards their ending position while avoiding collisions. By optimal, we mean as fast as possible, which is evaluated by a measure known as the makespan of the proposed solution. This is the setting studied in the MULTIAGENT PATH FINDING problem. In this work, we additionally provide the agents with a way to communicate with each other. Due to size constraints, it is reasonable to assume that the range of communication of each agent will be limited. What should be the trajectories of the agents to, additionally, maintain a backbone of communication? In this work, we study the MULTIAGENT PATH FINDING WITH COMMUNICATION CONSTRAINT problem under the parameterized complexity framework. Our main contribution is three exact algorithms that are efficient when considering particular structures for the input network. We provide such algorithms for the case when the communication range and the number of agents (the makespan resp.) are provided in the input and the network has a tree topology, or bounded maximum degree (has a tree-like topology, i.e., bounded treewidth resp.). We complement these results by showing that it is highly unlikely to construct efficient algorithms when considering the number of agents as part of the input, even if the makespan is 3 and the communication range is 1. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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