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15th International Conference on Social computing and Social Media, SCSM 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023

作者： Otsuka, Atsushi Hama, Kenta Nomoto, Narichika Ishii, Ryo Fukayama, Atsushi Nakamura, Takao NTT Digital Twin Computing Research Center Nippon Telegraph and Telephone Corp. 29F Shinagawa Season Terrace 2-70 Konan 1-chome Minato-ku Tokyo108-0075 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan

ISBN: (纸本)9783031359149

Embedded representations that express the user’s personality are essential for personalizing the output of machine learning. However, annotating training data to learn the embedding is difficult because one cannot directly observe a person’s internal personality. This paper proposes a method for learning user embedding representations from social networking service data to make language models behave with personality. The method focuses on text posted by social networking service users and obtains the user’s embedded representation by learning a model that predicts and generates sentences before and after the text input to the social networking service. Evaluation experiments showed that the proposed method could learn embedded expressions that reflected the user’s attributes, such as location or personality. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Embeddings

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DetecVFuzz: Enhancing Security in Consumer Electronic Devices Through Scalable Vulnerability Testing of Virtual Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Xiao, Xi Guo, Yongjian Jolfaei, Alireza Chen, Chuan Haghighi, Mohammad Sayad Wen, Sheng Lin, Yuanyi Tsinghua University Shenzhen International Graduate School Shenzhen China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Peng Cheng Laboratory Guangdong Shenzhen China Flinders University College of Science and Engineering SA5042 Australia Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Ministry of Education China Qilu University of Technology Shandong Academy of Sciences National Supercomputer Center in Jinan Jinan China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

The interconnection facilitated by consumer electronics has led to the creation of a complex network of devices. Testing efficiency of such consumer electronic devices can be improved through hardware emulation. QEMU is a classical emulator for simulation and testing. Fuzzing is a promising solution to test the security of such devices, which has become pivotal in identifying device vulnerability. However, Qtest libfuzzer, the QEMU native fuzzer, requires manual writing of stub code, which makes conducting the fuzzing process difficult and yields poor performance. To address these challenges in virtual device security analysis, we propose DetecVFuzz, a novel automatic fuzzing framework based on QEMU-Qtest, aiming to find vulnerabilities in virtual PCI devices, including USB, audio, network, etc. We inject mutated inputs into nested data structures as parameters of memory-related APIs to ensure the stable operation of VM procedures. We implemented DetecVFuzz and evaluated it on different virtual devices, such as virtual memory USB stack and drivers. In preliminary evaluations on nine recent versions of the QEMU, we found 15 new memory errors in virtual devices. In general, DetecVFuzz achieves 89.37% line coverage and 78.65% branch coverage in virtual devices. We identified 26 new crashes/errors, three of which were unseen new bugs. © 1975-2011 IEEE.

关键词： Virtual addresses

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Controlling discrete time crystals via single-site operations in zero-field diamond quantum simulators

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Physical Review B 2025年第14期111卷 144311-144311页

作者： Naoya Egawa Kaoru Mizuta Joji Nasu Department of Physics Tohoku University Sendai Miyagi 980-8578 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Hongo 7-3-1 Bunkyo Tokyo 113-8656 Japan Photon Science Center Graduate School of Engineering The University of Tokyo Hongo 7-3-1 Bunkyo Tokyo 113-8656 Japan RIKEN Center for Quantum Computing (RQC) Hirosawa 2-1 Wako Saitama 351-0198 Japan

Discrete time crystals (DTCs) have emerged as novel nonequilibrium phases of matter that spontaneously break discrete time-translation symmetry in periodically driven systems. Rigorous experimental validation of DTCs, which requires highly controllable quantum simulators, has stimulated extensive research across diverse fields in condensed matter physics and quantum information technologies. Among these advances, DTCs were demonstrated in a hybrid spin register within diamond, comprising a processor spin and surrounding memory spins. However, in conventional strategies involving a bias magnetic field, the field application effectively restricts the controllability of the processor spin. This limitation can be a significant barrier to the next goal of DTCs: achieving multifunctionality through enhanced local controllability. In this study, we theoretically propose multiple DTC protocols through the design of specific single-site control within the entire system. To this end, we consider a concrete model of a diamond-based quantum simulator operating without a bias magnetic field, thereby eliminating the restrictions on the processor spin. Our findings demonstrate that single-site operations enable access to DTCs with multiple distinct features in terms of periodicity and lifetime. Therefore, this approach provides a promising platform for creating diverse DTCs induced by single-site operations.

关键词： Magnetic fields

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Quantum kernel t-distributed stochastic neighbor embedding

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Physical Review Research 2024年第4期6卷 043234-043234页

作者： Yoshiaki Kawase Kosuke Mitarai Keisuke Fujii Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan and Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan and Center for Quantum Information and Quantum Biology Osaka University 560-0043 Osaka Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University Osaka 560-0043 Japan and RIKEN Center for Quantum Computing Wako Saitama 351-0198 Japan

Data visualization is important in understanding the characteristics of data that are difficult to see directly. It is used to visualize loss landscapes and optimization trajectories to analyze optimization performance. Popular optimization analysis is performed by visualizing a loss landscape around the reached local or global minimum using principal component analysis. However, this visualization depends on the variational parameters of a quantum circuit rather than quantum states, which makes it difficult to understand the mechanism of the optimization process through the property of quantum states. Here, we propose a quantum data visualization method using quantum kernels, which enables us to offer fast and highly accurate visualization of quantum states. In our numerical experiments, we visualize a hand-written digits data set and apply a k-nearest neighbor algorithm to the low-dimensional data to quantitatively evaluate our proposed method compared with a classical kernel method. As a result, our proposed method achieves comparable accuracy to the state-of-the-art classical kernel method, meaning that the proposed visualization method based on quantum machine learning does not degrade the separability of the input higher-dimensional data. Furthermore, we visualize the optimization trajectories of finding the ground states of the transverse field Ising model, and we successfully find the trajectory characteristics. Since quantum states are higher-dimensional objects that can only be seen via observables, our visualization method, which inherits the similarity of quantum data, would be useful in understanding the behavior of quantum circuits and algorithms.

关键词： Machine learning Quantum circuits

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Augmented Reality-Assisted Pediatric Surgery: A Machine Learning-Based Approach for Enhancing Accuracy 1

Augmented Reality-Assisted Pediatric Surgery: A Machine Lear...

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1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bhardwaj, Sumedha Narla, Swapna Absur, Md Nurul Dutta, Shuvo Hyzil, J.B. Krishna Ganesh, S.M. DIT University Department of School of Computing Uttarakhand Dehradun India Tek Yantra Inc CA United States CUNY Graduate Center New YorkNY United States Western Michigan University Department of Physics United States Govt College for Women Affiliated to University of Kerala Department of Zoology Thiruvananthapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331518578

Numerous advancements have been identified as potentially disruptive innovations in the surgical workplace as surgical workflow continues to improve in the digital age. The application of virtual reality (VR) and augmented reality (AR) within health care to enhance the use of data for medical reasons is unavoidable given their rapid expansion in availability, affordability, and accessibility. Using augmented reality may help paediatric orthopaedic and trauma surgeons overcome the difficult problem of comprehending the spatial linkages among the implants, anatomy, and their instruments. As needs be, the current examination proposes another AR of the surgical scene that deftly incorporates the various information sources given by a mobile C-arm and a Kinect RGB-depth sensor. This paper present a learning-based worldview to perceive the significant items or life structures in both Kinect and data of X-ray, and to produce an object unambiguous pixel-wise alpha guide that permits importance based combination of the X-ray pictures and the video inside a solitary view. We demonstrate very encouraging results in 10 simulated procedures with the goal of giving physicians a better grasp of the surgical environment and an enhanced perception. © 2025 IEEE.

关键词： Pediatrics

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Optimal Second-Order Rates for Quantum Information Decoupling

Optimal Second-Order Rates for Quantum Information Decouplin...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Shen, Yu-Chen Gao, Li Cheng, Hao-Chung National Taiwan University Department of Electrical Engineering Graduate Institute of Communication Engineering Taipei10617 Taiwan Department of Mathematics National Taiwan University Taipei10617 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei10617 Taiwan Department of Mathematics University of Houston HoustonTX77204 United States Quantum Computing Research Center New Taipei City236 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan

ISBN: (纸本)9781665475549

In this paper, we consider the standard quantum information decoupling, in which Alice aims to decouple her system from the environment by discarding some of her systems. To achieve an ?-decoupling with trace distance as the error criterion, we establish a one-shot characterization for the largest size of the remainder system in terms of the conditional (1 - ?)-hypothesis-testing entropy. When the underlying system is independent and identically prepared, our result leads to the matched second-order rate as well as the moderate deviation rate. © 2023 IEEE.

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Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

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Computer Systems science & engineering 2022年第12期43卷 1069-1084页

作者： Minghu Tang Wei Yu Xiaoming Li Xue Chen Wenjun Wang Zhen Liu key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu UniversityXining810007China School of Computer Science Qinghai Minzu UniversityXining810007China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of International Business Zhejiang Yuexiu UniversityShaoxing312069China Law School Tianjin UniversityTianjin300072China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki851-0193Japan

Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future *** the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five rea

关键词： Link prediction cold-start nonnegative matrix factorization graph regularization

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Exploring Child-Robot Interaction in Individual and Group Settings in India

Exploring Child-Robot Interaction in Individual and Group Se...

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International Conference on Robotics and Automation sciences (ICRAS)

作者： Gayathri Manikutty Sai Ankith Potapragada Devasena Pasupuleti Mahesh S. Unnithan Arjun Venugopal Pranav Prabha H. Arunav Vyshnavi Anil Kumar P. R. Rthuraj Rao R Bhavani AMMACHI Labs Amrita Vishwa Vidyapeetham Kollam Kerala India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Systems Innovation Graduate School of Engineering Science Osaka University Japan Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Kollam Kerala India

ISBN: (数字)9798350352504

ISBN: (纸本)9798350352511

This study evaluates the effectiveness of child-robot interactions with the HaKsh-E social robot in India, examining both individual and group interaction settings. The research centers on game-based interactions designed to teach hand hygiene to children aged 7–11. Utilizing video analysis, rubric assessments, and post-study questionnaires, the study gathered data from 36 participants. Findings indicate that children in both settings developed positive perceptions of the robot in terms of the robot's trustworthiness, closeness, and social support. The significant difference in the interaction level scores presented in the study suggests that group settings foster higher levels of interaction, potentially due to peer influence and collaborative dynamics. While both settings showed significant improvements in learning outcomes, the individual setting had more pronounced learning gains. This suggests that personal interactions with the robot might lead to deeper or more effective learning experiences. Consequently, this study concludes that individual interaction settings are more conducive for focused learning gains, while group settings enhance interaction and engagement.

关键词： Social robots Collaboration Aging

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A Comparative Study of Clustering Distance Metrics for Personalized Federated Learning in Human Activity Recognition

A Comparative Study of Clustering Distance Metrics for Perso...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Xiaoxu Wen Chenggang Lu Ruixiang Hu Yingrui Geng Yan Wang Hongnian Yu Qiangsong Zhao School of Automation and Electrical Engineering Zhongyuan University of Technology Zhengzhou China School of Intelligent Sensing and Instrumentation Zhongyuan University of Technology Zhengzhou China Graduate School of Science and Engineering Ritsumeikan University Shiga Japan School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh UK

ISBN: (数字)9798350366204

ISBN: (纸本)9798350366211

In Federated Learning (FL), Non-Independent and Identically Distributed (Non-IID) data across different clients presents a significant challenge for a global model to effectively generalize to all clients, thus affecting fairness across the system. To address this, the Clustered Personalized Federated Learning (CPFL) framework groups clients with similar data distributions and trains models that are tailored to the characteristics within each cluster. In this study, we experimentally evaluate the performance of four distance metrics - Cosine distance, Euclidean distance, Manhattan distance, and Mahalanobis distance - across varying numbers numbers of clusters within the CPFL framework, focusing on their impact on model accuracy and fairness across client models. Our results demonstrate that Euclidean distance not only improves model accuracy but also significantly reduces performance disparities between clients, exhibiting the lowest variance and enhancing fairness. In contrast, the other distance metrics introduce larger variance in certain scenarios, leading to greater differences in client model performance. This study provides empirical support for the use of distance metrics in CPFL, showing that selecting appropriate clustering methods can enhance both overall performance and fairness across client model.

关键词： Measurement Accuracy Mechatronics Federated learning Shape Focusing Euclidean distance Data models Human activity recognition Principal component analysis

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