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User-experience oriented POI recommendation with pseudo rating

Multimedia Tools and Applications 2024年第16期84卷 1-21页

作者： Yi, Kun Yamagishi, Ryu Li, Jiyi Li, Taishan Bai, Zhengyang Ma, Qiang Kyoto Institute of Economic Research Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Department of Computer Science and Engineering University of Yamanashi Takeda 4-3-11 Kofu 400- 8511 Yamanashi Japan Research Institute of Non-Timber Forestry Chinese Academy of Forestry Key Laboratory of Non-Timber Forest Germplasm Enhancement & ampUtilization of State Administration of Forestry and Grassland No. 3 Weiwu Road Jinshui District Zhengzhou450003 China RIKEN Center for Computational Science RIKEN Nihonbashi 1-chome Mitsubishi Bld. 15th floor 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Information Science Kyoto Institute of Technology Matsugasaki Sakyo-ku Kyoto Kyoto606-8585 Japan

Point-of-interest (POI) recommendation faces challenges in managing data sparsity and cold-start problems, particularly in terms of personalized recommendation. Firstly, most existing users only visit a small number of POIs, which results in severe data sparsity and makes most conventional methods unsuitable because they require adequate historical data from each user to learn the model. In addition, new users have little or no historical record. Existing ask-to-rate methods for cold start user scenarios treat user answers as historical data, which is inappropriate and causes difficulty in capturing user preferences. Therefore, in this paper, to tackle the data sparsity problem, we propose a user-experience model (UEM) to uncover user behaviors from several aspects. The proposed UEM involves four factors: Who (does it), What (the user does), Where (the user does it), and When (the user does it). In contrast to other methods that model user behavior from individual records, our model introduces a latent variable of user experience to extract similarity among users. This feature makes our model suitable for mitigating the severe sparse-data issue in POI recommendations. Furthermore, to handle the cold-start problem for new users, we propose a pseudo-rating mechanism (PRM) to capture new users’ preferences and recommend POIs. For new users, photos are provided for rating, and these ratings are then used to capture their preferences and estimate the probability of their visits. Extensive experimental results demonstrate that the proposed method achieves better recommendation performance and fairness than the state-of-the-art methods. Moreover, user study results validates that the users’ preferences captured via PRM are satisfactory, and UEM provides satisfactory POI recommendations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Behavioral research

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A class of Generic Approximate Sparse Pseudoinverse Matrix Techniques based on incomplete QR factorization

A class of Generic Approximate Sparse Pseudoinverse Matrix T...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Lipitakis, A.D. Filelis-Papadopoulos, C.K. Gravvanis, G.A. Anagnostopoulos, D. Harokopio University of Athens Department of Informatics and Telematics Tavros17778 Greece University College Cork Cork Ireland School of Engineering Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece

ISBN: (纸本)9781728176246

A class of Generic Approximate Sparse Pseudoin-verse matrices based on modified incomplete QR factorizations with Givens rotations is presented. The filtering criteria for the Incomplete QR factorization have been modified to improve the quality of the incomplete upper triangular factor R, by removing the restriction on the number of nonzero elements per row. Moreover, the Givens rotation matrices are dropped during the incomplete factorization process in order to reduce memory requirements. The proposed approximate pseudoinverse preconditioning scheme is computed, using a restricted solution process, based on approximate pseudoinverse sparsity patterns, derived from powers of the sparsified incomplete upper triangular matrix R. The proposed preconditioning scheme is used in conjunction with the Explicit Preconditioned Conjugate Gradient Least Squares method. Numerical results indicating the applicability and efficiency of the proposed algorithmic schemes for solving various model problems are given. © 2020 IEEE.

关键词： Matrix algebra

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Dynamic analysis of high-response storage systems to minimize the generation ramping requirements 12

Dynamic analysis of high-response storage systems to minimiz...

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12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2020

作者： Nikolaidis, Pavlos Fotiou, Stelios Kasparis, Takis Poullikkas, Andreas Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Energy Regulatory Authority P.O. Box 24936 Nicosia1305 Cyprus

ISBN: (纸本)9781839535635

The expansion of intermittent renewable energy sources for electricity production transformed power grids into complex systems. At high penetration levels, the conventional generating units occur inadequate for load following calling for more flexible resources. In this work, we provide the outcomes derived by a Genetic algorithm-driven priority list approach which effectively quantifies the benefits of electricity storage. Once optimally sized, different battery storage systems are subjected into life-cycle cost analysis based on realistic modelling and updated data found in the literature. According to the results, zinc-air offers the best feasible solution due to its low capital cost, followed by sodium-sulphur and lead-acid batteries which constitute viable solutions for minimizing the generation ramping requirements and maximizing renewable penetration rates. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Genetic algorithms

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Development of first-person shooter game with survival maze based on virtual reality 6

Development of first-person shooter game with survival maze ...

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6th Information Technology International Seminar, ITIS 2020

作者： Herumurti, Darlis Yunanto, Andhik Ampuh Senna, Guruh Arya Kuswardayan, Imam Arifiani, Siska Institut Teknologi Sepuluh Nopember Keputih Department of Informatics Surabaya City Sukolilo East Java60111 Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Keputih Sukolilo Surabaya City East Java60111 Indonesia

ISBN: (纸本)9781728177267

Game is one of the entertainment media that is rapidly growing. On this day, playing games is one way to spare time for refreshing. One of the popular games this year is an action game such as First-Person Shooter (FPS). Besides, the FPS game genre has developed rapidly to make it more interesting for the player. Non-player Character (NPC) has an essential function in artificially and automatically to make a game more immersive and consisting of various kinds. Also, from observation, adding challenges when playing, namely enemy characters, can make a player more enjoyable. Besides, one of modern technology to develop innovations for supporting a game is called virtual reality. So, in this research, the FPS game is built by adding NPC elements with virtual reality technology. Also, the gameplay in this game is a survival maze. Based on the evaluation, this game application can display objects in a virtual reality smoothly, and all feature in this game can display and run effectively. Besides, the respondents also give good feedback for user testing which has average score 4.66 from several users. So, the conclusion from this research is this game can make the FPS game more interesting for users by adding NPC, 3D element, and virtual reality. © 2020 IEEE.

关键词： Virtual reality

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Battle Royale Optimizer for solving binary optimization problems[Formula presented]

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Software Impacts 2022年 12卷

作者： Akan, Taymaz Agahian, Saeid Dehkharghani, Rahim Istanbul Ayvansaray University Istanbul Turkey Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic Erzurum Technical University Erzurum Turkey Department of Computer Engineering Işik University Istanbul Turkey

Battle Royale Optimizer (BRO) is a recently proposed metaheuristic optimization algorithm used only in continuous problem spaces. The BinBRO is a binary version of BRO. The BinBRO algorithm employs a differential expression, which utilizes a dissimilarity measure between binary vectors instead of a vector subtraction operator, used in the original BRO algorithm to find the nearest neighbor. To evaluate BinBRO, we applied it to two popular benchmark datasets: the uncapacitated facility location problem (UFLP) and the maximum-cut (Max-Cut) graph problems from OR-Library. An open-source MATLAB implementation of BinBRO is available on CodeOcean and GitHub websites. © 2022 The Author(s)

关键词： Battle Royale Optimization Binary Battle Royale Optimization Discrete optimization Optimization

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Digital Forensics Investigation and Incident Response in Internet of Things (IoT-DFIR): Challenges and Models

Digital Forensics Investigation and Incident Response in Int...

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International Conference on Wireless and Telematics (ICWT)

作者： Syaiful Ahdan Eki Ahmad Zaki Hamidi Sampurna Dadi Riskiono Ridha Muldina Negara Ratna Mayasari Suci Ramadona Ade Nurhayati Adi Sucipto Jupriyadi Galih Nugraha Nurkahfi Dzata Farahiyah Muchammad Naseer Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Elearical Engineering Study Program Politeknik Caltex Riau Riau Indonesia Faculty of Engineering Institut Teknologi Telkom Jakarta Jakarta Indonesia National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Yogyakarta Indonesia Informatics Department Sekolah Tinggi Teknologi Bandung Bandung Indonesia

Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuously exchanging data via sensors. Due to increased IoT security concerns, digital forensic investigators now possess greater knowledge and abilities to investigate IoT devices. IoT-DigFor are required in terms of cybercrime due to the rapid growth in the number of electronic devices and the collection and consumption of enormous amounts of data. IoT systems with billions of devices have produced a significant amount of evidence, which presents a significant challenge to digital investigators and practitioners who must connect with IoT devices to conduct fast and thorough investigations into cybercrimes. This article presents detailed information about forensics in the IoT environment. The IoT forensic paradigm and the challenges that design-based security requirements and IoT system security offer for IoT-DigFor are covered in great detail at the beginning of the article.

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms

Enhancing Security and Scalability of Metaverse with Blockch...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Anand Singh Rajawat S.B. Goyal Aarti Goyal Kavita Rajawat Maria Simona Raboaca Chaman Verma Traian Candin Mihaltan School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Mind Intellect Technology Indore India ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world.

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Benchmark Comparison of Methods for Approximate Neighborhood Estimation of Road Segments in Large-Scale Traffic Networks 24

Benchmark Comparison of Methods for Approximate Neighborhood...

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24th Pan-Hellenic Conference on informatics, PCI 2020

作者： I. Salamanis, Athanasios A. Gravvanis, George K. Filelis-Papadopoulos, Christos Michail, Dimitrios Department of Electrical and Computer Engineering School of Engineering Democritus University of Thrace University Campus Xanthi67100 Greece University College Cork Cork Ireland Department of Informatics and Telematics Harokopio University of Athens Greece

ISBN: (纸本)9781450388979

The accuracy of a traffic prediction model used to predict the future state of a link (i.e. road segment) of interest in a traffic network, can be enhanced by integrating the traffic information of adjacent links. The approximate estimation of such a neighborhood of links for all links of the traffic network is a type of approximate k-nearest neighbor search problem (k-NNS), which can prove to be quite computationally expensive especially for large-scale networks. In this paper, methods for solving this problem in large-scale traffic networks are presented and compared with each other in terms of both accuracy and running-time performance. Experiments have been conducted using real-world map data from the city of Thessaloniki, and the preliminary results indicate the effectiveness of the methods in solving the neighborhood estimation problem. © 2020 ACM.

关键词： Nearest neighbor search

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Indexed Multiple Access with Reconfigurable Intelligent Surfaces: The Reflection Tuning Potential

arXiv

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arXiv 2023年

作者： Singh, Rohit Kaushik, Aryan Shin, Wonjae Alexandropoulos, George C. Toka, Mesut Renzo, Marco Di The Department of Electronics and Communication Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar144027 India The School of Engineering and Informatics University of Sussex BrightonBN1 9RH United Kingdom The Department of Electrical and Computer Engineering Ajou University Suwon-si16499 Korea Republic of The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates Paris-Saclay University CNRS CentraleSupelec Laboratoire des Signaux et Systemes 3 Rue Joliot-Curie Gif-surYvette91192 France

Indexed modulation (IM) is an evolving technique that has become popular due to its ability of parallel data communication over distinct combinations of transmission entities. In this article, we first provide a comprehensive survey of IM-enabled multiple access (MA) techniques, emphasizing the shortcomings of existing non-indexed MA schemes. Theoretical comparisons are presented to show how the notion of indexing eliminates the limitations of non-indexed solutions. We also discuss the benefits that the utilization of a reconfigurable intelligent surface (RIS) can offer when deployed as an indexing entity. In particular, we propose an RIS-indexed multiple access (RIMA) transmission scheme that utilizes dynamic phase tuning to embed multi-user information over a single carrier. The performance of the proposed RIMA is assessed in light of simulation results that confirm its performance gains. The article further includes a list of relevant open technical issues and research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Transmissions

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iTCLab PID Control Tuning Using Deep Learning

iTCLab PID Control Tuning Using Deep Learning

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Information Technology International Seminar (ITIS)

作者： Basuki Rahmat Minto Waluyo Tuhu Agung Rachmanto Agussalim Alfan Rizaldy Pratama Muljono Muljono I Gede Susrama Mas Diyasa Made Hanindia Prami Swari Novirina Hendrasarie Information Technology Masters Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Industrial Engineering Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Environmental Engineering Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Center for Research and Innovation on Advanced Transportation Electrification - Politeknik Elektronika Negeri Surabaya Indonesia Computer Science Department Universitas Dian Nuswantoro Semarang Indonesia Informatics Department Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia

This paper presents a novel approach for tuning Proportional-Integral-Derivative (PID) controllers using deep learning techniques, specifically applied to the iTCLab, an advanced industrial control laboratory system. PID controllers are widely used in industrial processes for their simplicity and effectiveness. However, their performance heavily relies on proper tuning, which can be a complex and time-consuming task. In this study, we leverage the capabilities of deep learning to automate and optimize the PID tuning process for the iTCLab setup. We propose a data-driven methodology that combines system identification, Deep Learning networks, and optimization algorithms to achieve superior PID controller tuning.

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