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Metaverse Perspectives from Japan: A Participatory Speculative design Case Study

Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-51页

作者： Hohendanner, Michel Ullstein, Chiara Miyamoto, Dohjin Huffman, Emma Fukuwatari Socher, Gudrun Grossklags, Jens Osawa, Hirotaka Munich Center for Digital Sciences and AI Munich University of Applied Sciences Munich Germany Department of Computer Science Technical University of Munich Germany Department of Computer Science Technical University of Munich Munich Germany Tokyo University Tokyo Japan Kyoto Design Lab Kyoto Institute of Technology Kyoto Japan Faculty of Science and Technology Keio University Yokohama Japan

Currently, the development of the metaverse lies in the hands of industry. Citizens have little influence on this process. Instead, to do justice to the pluralism of (digital) societies, we should strive for an open discourse including many different perspectives on the metaverse and its core technologies such as AI. We utilize a participatory speculative design (PSD) approach to explore Japanese citizens’ perspectives on future metaverse societies, as well as social and ethical implications. Our contributions are twofold. Firstly, we demonstrate the effectiveness of PSD in engaging citizens in critical discourse on emerging technologies like the metaverse by presenting our workshop framework and participants’ processes. Secondly, we identify key themes from participants’ perspectives, providing insights for culturally sensitive design and development of virtual environments. Our analysis shows that participants imagine the metaverse to have the potential to solve a variety of societal issues;for example, breaking down barriers of physical environments for communication, social interaction, crisis preparation, and political participation, or tackling identity-related issues. Regarding future metaverse societies, participants’ imaginations raise critical questions about human-AI relations, technical solutionism, politics and technology, globalization and local cultures, and immersive technologies. We discuss implications and contribute to expanding conversations on metaverse developments. © 2024 Copyright held by the owner/author(s).

关键词： Virtual environments

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Effects of Auditory Integration Training Combined with Music Therapy on Children with Autism Spectrum Disorder

Effects of Auditory Integration Training Combined with Music...

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2023 International Symposium on World Ecological design, ISWED 2023

作者： Liu, Zhenyu Wang, Qiurui He, Caihong Yao, Cheng Ying, Fangtian College of Computer Science and Technology Zhejiang University China Department of Industrial Design Eindhoven University of Technology Netherlands Guangzhou Wanqu Cooperative Institute of Design China School of Industrial Design Hubei University of Technology China

ISBN: (纸本)9781643685069

Objective: This study explored the effects of Auditory Integration Training combined with Music Therapy on Autistic Children. Methods: Eighty Autistic Children who received treatment at a Rehabilitation Center for Children with Autism in Guangzhou from January to June 2023 were selected as participants. They were randomly allocated to an Experimental or a Control group, giving 40 in each condition. The Control group received Auditory Integration Training, while the Experimental group received Auditory Integration Training combined with Music Therapy. Sociability, Language, Behavior, and Neuron Development of the two groups of children were compared before and six months after treatment. Results: By comparing the Sociability, Language, Behavior, and Neuron Development scores of two groups of children before and after six months of treatment, the combination of these two treatments has positively impacted the rehabilitation of children with autism. Conclusion: The combination of Auditory Integration Training and Music Therapy has shown promising results in its application for Children with Autism Spectrum Disorder (ASD). © 2024 The Authors.

关键词： Integration

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Integer wavelet transform based an effective fragile watermarking scheme for exact authentication and restoration

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4841-4852页

作者： Singh, Durgesh Udmale, Sandeep Sambhaji Singh, Sanjay K. Department of Computer Science and Engineering PDPM Indian Institute of Information Technology Design and Manufacturing MP Jabalpur India Maharashtra Mumbai India Varanasi221005 India

Verifying image authentication is necessary when images support evidence for essential purposes such as law enforcement and forensic investigation. The self-embedding fragile watermarking method is a powerful technique for confirming exact content authentication and restoration. This paper proposes the integer wavelet transform(IWT) based watermarking approach for accurate authentication and restoration. Here, watermark bits are calculated from each block of 2 × 2 pixels. The authentication bits are inserted into the block’s three LSBs, while restoration bits are inserted into the mapping block. Restoration bits are calculated from the five MSBs of the cover image using IWT. In contrast, authentication bits are calculated from secret keys, MSBs and, the location of pixels. The experimental outcomes prove that the PSNR and NCC of the reconstructed image in the suggested scheme are significantly high analyzed with the existing methods. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Restoration

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Enhance Intrusion Detection by Analyzing the Behavior of Labeled and Unlabeled Classification to Obtain Better Accuracy 2

Enhance Intrusion Detection by Analyzing the Behavior of Lab...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Suriya Prakash, J. Srinidhi, N. Latha, A. Chaithra Kiran, S. Department of Computer Science and Engineering Karnataka Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Sri Krishna Institute Of Technology Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Bengaluru India KSIT Department of Computer Science and Design Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519056

Intrusion Detection System has accelerated globally as a result of the need to identify intrusions that occur in network data flow. The two IDS methods that are primarily used by machine learning and network pattern detection are anomaly-based and signature-based methods. A network can be made foolproof by keeping it safe by keeping an eye out for any hazardous activity. In network dataflow systems, network traffic can be produced via simulators. Simulators are a useful tool for detecting harmful behavior. Intrusions that are host-based, protocol-based, application protocol-based, network-based, and hybrid-based are some of the most prevalent kinds that have been discovered. In this paper,open source datasets ISP datasets and WIDE datasets are employed for experimentation. The crux of this paper is to carry out network classification by using minimal training sets and classifying maximal test sets for procuring accurate results. Lastly, the Labelled and Unlabelled technique is used to compare various accuracy outcomes. The suggested scheme's effectiveness is confirmed by the empirical investigation conducted on ISP and WIDE data. In this paper, the labeled and unlabeled classification proposed here identifies the intruder application with minimal labeled dataset. Most importantly automatic labeling of large test sets with small training set is accomplished. The analysis process for classification methods involving ISP and WIDE datasets by an experimental way has produced efficient classification results. The Labeled classification algorithm produces 93% accuracy and the unlabeled classification algorithm produces 84% accuracy. © 2024 IEEE.

关键词： Network intrusion

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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A Novel Intelligent Task Offloading Scheme for Multi-Controller Environment in Software Defined Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 20018-20029页

作者： Hussain, Mir Wajahat Sangaiah, Arun Kumar Reddy, K. Hemant Kumar Roy, Diptendu Sinha Alenazi, Mohammed J.F. Javvaji, Pavan Kumar Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Anekal562106 India International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Meghalaya 793003 India College of Computer and Information Sciences Department of Computer Engineering King Saud University Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing platform meets these stringent computation requirements, but it has a significant latency that fog computing effectively handles. Software Defined Network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network pre-requisites in an agile manner. Task Offloading (TO) is a crucial issue in Software Defined-IoV (SDIoV), particularly when the vehicle's resources are insufficient. Fog nodes schedule offloaded tasks;however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this paper introduces two proactive Intelligent Task Offloading (ITO' and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO' and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7-13.9)% more than the existing scheme. They also reduce average packet loss, delay, Round Trip Time (RTT), and offloaded node time selection by (45.35-21.3)%, (50-33.3)%, (53.9-9.9)% and (43.2-26.06)% which demonstrates the efficacy of the proposed scheme. © 2025 IEEE.

关键词： Fog computing

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Synergizing Investment Portfolios: A Technologically Advanced Approach with Integrated Chatbot Capabilities

Synergizing Investment Portfolios: A Technologically Advance...

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2024 MIT Art, design and technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Shelke, Priya M. Pawar, Rajendra G. Pawar, Parth Rathod, Rupali Kawoor, Tanishq BRACT's Vishwakarma Institute of Information Technology Pune India Design and Technology University Engineering MIT Art Department of Computer Science Pune India

ISBN: (纸本)9798350362879

A key component of financial management portfolio optimization, which gives investors the ability to allocate capital strategically and make well-informed decisions. Traditional portfolio optimization techniques might not adequately reflect the complexities of real-world markets, despite frequently relying on mathematical models and historical data. In this paper, a novel framework for portfolio optimization with an intelligent chatbot is proposed. The research work requests information from the user, including his name, salary, investment amount, and desired investment period. Based on these parameters, it optimizes the portfolio distribution based on the user's risk appetite (low, medium, or high) and calculates the predicted rate of return. The chatbot serves as an interactive interface for investors, gathering information about their investment goals, risk tolerance, and preferences. Readers who find your article through an abstracting service may never see the rest of your article. Be sure the abstract conveys why your research problem is important and how your work moves the field forward. © 2024 IEEE.

关键词： Abstracting

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Data Processing and Pattern Recognition for Electronic Nose 3

Data Processing and Pattern Recognition for Electronic Nose

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raymond, David Chaudhri, Shivnath Shaikh, Kahkasha Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Department of Computer Science and Medical Engineering Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Depertment of Computer Science and Design Wardha India

ISBN: (纸本)9798331509675

Electronic nose (e-nose) technology has become a powerful tool for identifying and evaluating complex scents in a variety of contexts, such as environmental monitoring, medical diagnostics, and food quality control. This review study looks at how important pattern recognition and data processing are to improving e-nose system performance. This study covered challenges, including data complexity, cross-sensitivity and interference, sensor drift, and a lack of standardization. The article covers several future directions, such as the use of artificial intelligence (AI) and machine learning to advance pattern recognition and predictive analytics, integration with the Internet of Things (IoT) and big data for real-time monitoring, and advancements in sensor technology for improved sensitivity and *** research work is underway towards the development of low power flexible sensors, improved multi sensor fusion Methodologies, smart adaptive algorithms, Application for personalized health monitoring and diagnostics as well to make it green technologies. To move forward with emerging technologies and for e-nose technology to be applicable, it will have to overcome these weaknesses. © 2025 IEEE.

关键词： Electronic nose

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