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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Burak Dindar Can Berk Saner Dogukan Yigit Polat Hüseyin Kemâl Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore School of Computing National University of Singapore Singapore

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time.

关键词： Data privacy Computational modeling Europe Machine learning computer architecture Generators Data models Standards Interoperability Load flow

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Development of a Website-Based Financial Transaction Management System Using Agile with the Scrum Framework 10

Development of a Website-Based Financial Transaction Managem...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Latifah, Ayu Ramdan, Galih Muhammad Sidik, Muhamad Lutfhi Farid Faturrohman, Nadhif Nursyaban, Dzikri Ramelan, Agus Rahman, Fachrur Razy School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Institut Teknologi Garut Department of Computer Science Garut Indonesia Universitas Sebelas Maret Electrical Engineering Departement Solo Indonesia Metal Industry Polytechnic of Morowali Department of Installation and Electrical Engineering Central Sulawesi Indonesia

ISBN: (纸本)9798350339543

The process of managing financial transactions, especially in the education sector, is still mostly carried out conventionally, so it has the potential to cause many risks, such as loss or damage to documents related to difficulties in searching for data as needed, transaction processes that take quite a long time, financial records that are not properly recorded. Well, it is prone to discrepancies between the nominal filing and the nominal disbursed conventional representations of financial statements that make the stakeholder evaluation process uncomfortable, and many other things. Therefore this research was conducted to build a website-based information system that can prevent this from happening in the future. The method used in the development of this information system is the Agile method with the Scrum framework. The Scrum framework consists of five stages, starting from Initiate, Plan, and Estimate, Implement, Review and Retrospect, and Release. This website was built using the Javascript programming language using the Vue JS framework for the Front-end. With this website-based application, it is hoped that transaction activities can be carried out better and correctly without being limited by space and time. © 2023 IEEE.

关键词： Websites

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Smart Road Damage Survey System Using Machine Vision and IoT technology

Smart Road Damage Survey System Using Machine Vision and IoT...

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

作者： Kusprasapta Mutijarsa Figo Agil Alunjati Fadhil Hidayat Ranna Kurnia Zulkifli Arsyad School of Electrical Engineering and Informatics Bandung Institute of Technology (ITB) Bandung Indonesia Department of Civil Engineering National Institute of Technology Bandung Indonesia Department of Computer and Informatics Engineering Bandung State Polytechnic Bandung Indonesia

Road is an essential transportation infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the road surface. A good-condition road ensures the land traffic runs well. This research is a collaboration between the Bandung institute of technology and the department of Public Works and Spatial Planning of West Java Province to develop a smart system for road damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the performance of the road survey activities. The previous road damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect road defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer architecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the road condition image automatically, and a dashboard application to reveal the road condition on map. The performance resulted in accuracy, precision, recall, and F-score of the road damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively.

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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Probing Large Language Models from A Human Behavioral Perspective

arXiv

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arXiv 2023年

作者： Wang, Xintong Li, Xiaoyu Li, Xingshan Biemann, Chris Department of Informatics Universität Hamburg Germany School of Computer Science and Technology Beijing Institute of Technology China Institute of Psychology Chinese Academy of Sciences China

Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. However, the understanding of their prediction processes and internal mechanisms, such as feed-forward networks (FFN) and multi-head self-attention (MHSA), remains largely unexplored. In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely recognized as meaningful indicators of human reading patterns. Our findings reveal that LLMs exhibit a similar prediction pattern with humans but distinct from that of Shallow Language Models (SLMs). Moreover, with the escalation of LLM layers from the middle layers, the correlation coefficients also increase in FFN and MHSA, indicating that the logits within FFN increasingly encapsulate word semantics suitable for predicting tokens from the vocabulary. © 2023, CC BY.

关键词： Eye tracking

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NCC: Neural concept compression for multilingual document recommendation[Formula presented]

NCC: Neural concept compression for multilingual document re...

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作者： Tashu, Tsegaye Misikir Lenz, Marc Horváth, Tomáš ELTE – Eötvös Loránd University Faculty of Informatics Institute of Academia-Industry Cooperation Department of Data Science and Engineering Pázmány Péter sétány 1/C 1117 Budapest Hungary Pavol Jozef Šafárik University in Košice Faculty of Science Institute of Computer Science Jesenná 5 Košice040 01 Slovakia Department of Artificial Intelligence Bernoulli Institute of Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Wollo University Kombolcha Institute of Technology College of Informatics Kombocha Ethiopia

In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to improve the quality of content-based multilingual document recommendation and information retrieval systems by creating a language-independent representation. The main idea is to use mappings to align monolingual representation spaces, using concept compression, to create inter-lingual representations. The proposed approach outperforms traditional cross-lingual retrieval and recommendations methods in experiments conducted on JRC-Acquis and EU bookshop multilingual corpora. Our dataset and code are publicly available at https://***/Tsegaye-misikir/NCC. © 2023

关键词： Information retrieval systems

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On the Existence of Highly Organized Communities in Networks of Locally Interacting Agents 7th

On the Existence of Highly Organized Communities in Netwo...

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7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023

作者： Liagkou, Vasiliki Nastou, Panagiotis E. Spirakis, Paul Stamatiou, Yannis C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus Patras26504 Greece Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Business Administration University of Patras Patras26504 Greece Department of Informatics and Telecommunications University of Ioannina Kostakioi Arta47100 Greece Department of Mathematics Applied Mathematics and Mathematical Modeling Laboratory University of the Aegean Samos Greece Center for Applied Optimization University of Florida Gainesville United States

ISBN: (纸本)9783031346705

In this paper we investigate phenomena of emergence of highly organized structures in networks. Our approach is based on Kolmogorov complexity of networks viewed as finite size strings. We apply this approach to study the emergence of simple organized, hierarchical, structures based on Sierpinski Graphs and we prove a Ramsey type theorem that bounds the number of vertices in Kolmogorov random graphs that contain Sierpinski Graphs as subgraphs. Moreover, we show that Sierpinski Graphs encompass close-knit relationships among their vertices that facilitate fast spread and learning of information. In the context of our work we can investigate questions related to the emergence or formation of suitably large groups of malicious individuals in a networks of interacting agents, which are able to take control of the networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Security Revisited: Honeytokens meet Google Authenticator 7

Security Revisited: Honeytokens meet Google Authenticator

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7th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Papaspirou, Vassilis Papathanasaki, Maria Maglaras, Leandros Kantzavelou, Ioanna Douligeris, Christos Ferrag, Mohamed Amine Janicke, Helge University of West Attica Department of Informatics and Computer Engineering Athens Greece University of Thessaly Department of Cs and Telecommunications Lamia Greece Cyber Technology Institute De Montfort University Leicester United Kingdom University of Piraeus Department of Informatics Piraeus Greece Guelma University Department of Computer Science Guelma Algeria Cyber Security CRC Edith Cowan University Perth Perth6027 Australia

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In contrast to the rapid advancement and evolution of technology, computer systems rely significantly on passwords to maintain their security for an unreasonably long time. Although the introduction of two or more factors improved existing authentication procedures and gave rise to new multi-factor authentication schemes, more advanced and focused assaults have revealed they are also sensitive and ineffective to assure only authorized access to systems. This study suggests a revolutionary two-factor authentication mechanism that uses QR codes and honeytokens. In the current implementation, honeywords and Google authenticator work together. In order to speed up the identification process, the honeywords technique is utilized as a second factor on a different computing device. By using a stronger and more effective authentication mechanism than a single password mechanism or a common two-factor authentication scheme, the novel and straightforward approach presented here intends to add additional layers of security and protection into a system, thereby increasing its security. © 2022 IEEE.

关键词： Authentication

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Intelligent Verbal Interaction Methods with Non-Player Characters in Metaverse Applications 5

Intelligent Verbal Interaction Methods with Non-Player Chara...

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5th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2023

作者： Pariy, Vladislav Lipianina-Honcharenko, Khrystyna Brukhanskyi, Ruslan Sachenko, Anatoliy Tkachyk, Fedir Lendiuk, Dmytro West Ukrainian National University Department for Information Computer Systems and Control Ternopil Ukraine West Ukrainian National University Department of Business Analytics and Innovative Engineering Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland West Ukrainian National University Department of Taxes and Fiscal Policy Ternopil Ukraine

ISBN: (纸本)9798350372571

This article is devoted to the development of Intelligent Verbal Interaction Methods with Non-Player Characters in Metaverse Applications. The creation of a character model using MetaHuman Creator and the implementation of dialogue animation are considered. The construction of answers based on text analysis and their submission to the chat window is described. The system has been tested and found to be stable and have a satisfactory response time of approximately 2 seconds. The results show the successful implementation of methods of verbal interaction with non-game characters in metaverse, which can be useful for further research and development in this area. © 2023 IEEE.

关键词： Animation

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Edge Intelligence Based Collaborative Learning System for IoT Edge 12

Edge Intelligence Based Collaborative Learning System for Io...

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12th IEEE Annual Information technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Welagedara, Lahiru Harischandra, Janani Jayawardene, Nuwan Informatics Institute of Technology Computing Department Colombo Sri Lanka University of Moratuwa Department of Computer Science and Engineering Sri Lanka

ISBN: (纸本)9781665400664

Edge Intelligence based collaborative learning systems have been developed to perform collaborative learning among multiple devices in a distributed environment. Majority of the collaborative learning systems have been designed using resources containing high computational power. It was identified that a system could be implemented to facilitate collaborative learning in resource constrained Internet of Things (IoT) devices. The existing collaborative learning systems were critically reviewed and analyzed to identify the ideal collaborative learning approach for resource constrained IoT edge. During the initial stages of the research, partitioned model training was identified as the most ideal approach. The research paved the way to design and implement two training architectures based on partitioned model training approach to facilitate environments with adequate and limited access to edge infrastructure. The proposed system utilized a hybrid deep learning model in partitioned model training approach for the first time. Furthermore, the research utilized a lightweight containerization mechanism to deploy the proposed collaborative learning system. The testing and evaluation phases of the research proved that the system was able to significantly reduce the resource consumption of the devices while achieving high model accuracy. The experimental setup reached up to 97% in model accuracy while consuming a significantly lower CPU consumption of 6.33%. The proposed system also proved to function efficiently by reducing energy consumption and reducing operational temperature by up to 4°C. © 2021 IEEE.

关键词： Internet of things

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