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Multi-Topic Belief Formation Through Bifurcations Over Signed Social Networks

IEEE Transactions on Automatic Control 2025年

作者： Bizyaeva, Anastasia Franci, Alessio Leonard, Naomi E. University of Washington NSF AI Institute in Dynamic Systems Dept. of Mechanical Engineering SeattleWA98195 United States University of Liège Dept. of Electrical Engineering and Computer Science Belgium WEL Research Institute Wavre Belgium Princeton University Dept. of Mechanical and Aerospace Engineering PrincetonNJ08544 United States

We propose and analyze a nonlinear dynamic model of continuous-time multi-dimensional belief formation over signed social networks. Our model accounts for the effects of a structured belief system, self-appraisal, internal biases, and various sources of cognitive dissonance posited by recent theories in social psychology. We prove that agents become opinionated as a consequence of a bifurcation. We analyze how the balance of social network effects in the model controls the nature of the bifurcation and, therefore, the belief-forming limit-set solutions. Our analysis provides constructive conditions on how multi-stable network belief equilibria and belief oscillations emerging at a belief-forming bifurcation depend on the communication network graph and belief system network graph. Our model and analysis provide new theoretical insights on the dynamics of social systems and a new principled framework for designing decentralized decision-making on engineered networks in the presence of structured relationships among alternatives. © 1963-2012 IEEE.

关键词： Continuous time systems

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Secure IoT Entrance Using Mobile Application

Secure IoT Entrance Using Mobile Application

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International Electromagnetic Compatibility Conference (EMC Turkiye)

作者： Marwa Issam Abdulkareem Osama Ibraheem Ashour Yucel Batu Salman dept. software engineering Bahcesehir University Istanbul Turkey ddept. computer engineering Cankaya University Tikrit Iraq

The impact of cyber security attacks increased globally, implementing proper security standards to protect data in the cyber environment has been prioritized, while assets are also at risk of being exploited through physical security breaches. Considering the potential risk of Radio Frequency Identification (RFID) cards being lost or stolen, third parties can effortlessly gain the privileges of an authorized person. In this paper, we propose a design for a secure access control system that operates on an Android phone with multi-factor authentication to enable secure access control systems. For this purpose, the authorized users are registered in a database that can be accessed by the access control system when the user authenticates his$\backslash$her credentials with a login/password as well as biometric data from his$\backslash$her smartphone. This system works when the user is 10 meters away from the access control system, after that the user will be asked to submit his$\backslash$her biometric credentials. The database then compares the user’s keys and grants access if authentication is successful. This design is intended to reduce the number of cases in which unauthorized access to a restricted area. The observed results clearly meet the required security of physical access control systems.

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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A Blockchain-Powered Loan Management System Enhanced with Smart Contract

A Blockchain-Powered Loan Management System Enhanced with Sm...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Muhammad Saleem Muhammad Haris Mehwish Naeem Abdul Basit Abro Maaz Ahmad Abdul Ahad Dept. of Software Engineering Iqra University Main Campus Karachi Pakistan University Libre de Bruxelles Brussels Belgium Dept. of Computer Science Indus University Karachi Pakistan Dept. of Computer Science Iqra University Main Campus Karachi Pakistan

Most financial lending systems in use today are based on a single service platform, transactions are opaque, and the majority of system participants can be tracked back to Mechanisms for protecting data privacy are not strong enough to withstand cyberattacks. We suggest lending over the block chain (LoC), a cutting-edge finance and credit facility built on smart contracts on a block chain hyper ledger fabric, as a solution to these problems. We use the poverty level for Chinese loans as an example. We create a digital accounting model to transfer assets between decentralized and centralized ledgers and suggest smart contract closing and unlocking methods. To safeguard the privacy of data, we established digital signatures and oracles. The opening rule's and the chain rule's performance evaluation demonstrates the applicability of our system in actual loan situations.

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Innovative Poverty Estimation through Machine Learning Approaches 18

Innovative Poverty Estimation through Machine Learning Appro...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mashhood, Ahmad Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim Khan, Ammad Aslam Khan, Bahram BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences M.A. Road Satellite Town Rawalpindi Pakistan

ISBN: (纸本)9798350328172

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges. © 2023 IEEE.

关键词： Forecasting

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19

Investigating Online Searching Behavior Based on Google Tren...

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Frontiers in Education (FIE) Conference

作者： Abdelkader Nasreddine Belkacem Nuraini Jamil Saed Alrabaee Dept. of Computer and Network Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Computer Science and Software Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Information Systems and Security College of IT UAE University Al Ain United Arab Emirates

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning.

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Comparing Actual Measurement and PVsyst Simulation Results for Energy Generation of Microgrid-Connected PV systems

Comparing Actual Measurement and PVsyst Simulation Results f...

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Smart Grid Conference (SGC)

作者： Mohammad Nazififard Hamed Hashemi-Dezaki Kia Nazififard Dept. of Energy Systems Engineering School of Mechanical Engineering Kashan Iran Dept. of Electrical and Computer Engineering Kashan Iran Independent Researcher Tehran Iran

This paper evaluates the uncertainty in energy generation of a 12 kW p microgrid (MG)-connected solar photovoltaic (PV) system located at the University of Kashan campus. The study compares long-term recorded measurements and PVsyst simulations for energy generation of PV systems over the course of a year. It is focused on specific yield and the deviation between the measured net energy generated by the PV facility and the calculated useful produced energy by PVsyst software. The results indicate a significant difference between the output power and the simulation results from PVsyst during colder months. However, in warmer months, the disparity between recorded measurements and simulated results is less than 10%. The largest deviation occurs in November, where the difference exceeds 35%. Generally, PVsyst software estimates the energy output from the PV system with an uncertainty of less than 10%. These findings contribute to understanding the variability in PV power generation and developing effective energy management strategies. Such strategies are crucial for ensuring a reliable power supply within an MG.

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Sinhala Sign Language Learning System for Hearing Impaired Community

Sinhala Sign Language Learning System for Hearing Impaired C...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： Idushan K. A. S Dilshan P. G. T Jayasundera P. S Madhushan K. L. B Jenny Krishara Dinuka Wijendra Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-to-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users.

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Mobile Gateways for LoRa End-Devices: Support Connectivity on the Move

Mobile Gateways for LoRa End-Devices: Support Connectivity o...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Vasileios Moysiadis Dimitrios N. Skoutas Charalabos Skianis Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Computer Science International Hellenic University Greece Dept. of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

This paper proposes a method enabling mobile LoRa gateways to support mobile end devices. The approach utilizes trilateration and similar approaches based on RSSI to estimate the position of end devices and specify the movement of the gateways to maintain contact. The evaluation of the proposed method tests various parameters such as speed and number of end devices, and it shows that it is able to retain contact even with speeds up to 12 mps. Moreover, the evaluation shows that the ADR-MIN algorithm performs better in this scenario than the default ADR.

关键词： Performance evaluation Network servers Conferences Computational modeling LoRa Logic gates Low-power wide area networks

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Movie genres and beyond: An analytical survey of classification techniques 5

Movie genres and beyond: An analytical survey of classificat...

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5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Mehal, Avleen Singh Meena, Kalpana Singh, Rijul Bir Shambharkar, Prashant Giridhar Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Dept. of Computer Engineering New Delhi India

ISBN: (纸本)9781665415712

In today's world with a plethora of options available on movie content, it is very important to have models which could classify these large contents based on their genres. Movies are the most widely entertaining source all over the world. Every person has his own taste of movies, some feel action movies are better, some would say romantic. So the online platforms must have the collection of movies according to a particular category i.e. based on genres. This paper has proposed an extensive and exhaustive survey for various models and techniques, which could be applied to solve the challenges involved in performing the genre based classification. This paper summarizes and compares all those techniques by classifying them into four broad domains -through text mining, poster detection, audio and video features of the movies. These domains included various classifying techniques: Bi-directional LSTM, gram layer, scene categorization and CNN respectively. With such an extensive amount of work going in the field in the last not many years, this review paper will help specialists in the field of movie genre classification. © 2021 IEEE.

关键词： Surveys

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