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1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： An, Zhaohui Min, Gaochen Jiang, Xiangzuo Yu, Xinbo He, Wei Silvestre, Carlos School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9789819927883

In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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Retraction Note: Studying efficacy of traditional software quality parameters in quantum software engineering

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Faryal, Memoona Rubab, Saddaf Khan, Malik M. Khan, Muhammad Attique Shehab, Abdulaziz Tariq, Usman Chelloug, Samia Allaoua Osman, Lobna National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah UAE Department of Computer Science HITEC University Taxila Taxila Pakistan Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia Department of Information Systems College of Computer Science Mansoura University Mansoura Egypt College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering & Technology Talkha Egypt

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A Machine Learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering 3

A Machine Learning-Enhanced IoT Wearable Device for Monitori...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Suttarwala, Hussain Kumar, Ritesh Deorankar, Madhavi L. Daulat Sarpate, Dikshendra Shingade, Pallavi Kumar Sharma, Surender More, Vrushali Patkar, Uday Chandrakant Sharma, Deepak K. American Express Senior Software Engineer PhoenixAZ85054 United States Dr. Akhilesh Das Gupta Institute of Professional Studies Dept. of AI and Data Science Delhi New Delhi110053 India Marathwada Mitramandal College of Engineering Dept. of Electronics and Telecommunications Maharashtra Pune411052 India Zeal College of Engineering and Research Dept. of AI and Data Science MaharashtraNarhe Pune411041 India D Y Patil College of Engineering Dept. of AI and Data Science Akurdi Pune411041 India Poornima University Dept. of Electrical Engineering Jaipur India Genba Sopanrao Moze College of Engineering Dept. of AI and Data Science Balewadi Maharashtra Pune411045 India Bharati Vidyapeeth's College of Engineering Department of Computer Engineering Lavale Pune412115 India Savitribai Phule Pune University Dept. of AI and Data Science Maharashtra Pune India

ISBN: (纸本)9798350361391

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device's superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device's contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders. © 2024 IEEE.

关键词： Convolutional neural networks

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Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber Sensor: A Machine Learning Approach

Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber S...

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International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Mahmudul Hoque Mahmud Ariful Hoque Maruf Mohammad Nasir Uddin Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Software Engineering Institute of Information Technology University of Dhaka Dhaka Bangladesh Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

This study introduces a novel approach to improving Photonic Crystal Fiber (PCF) Surface Plasmon Resonance (SPR) sensors by incorporating machine learning (ML) models for predicting core mode index. Traditionally, identifying core phenomena in PCF SPR sensors required manual visual inspection by experts, a process that is both time-consuming and prone to errors. To address this, a dual-core PCF model is developed and used Finite Element Method (FEM) simulations to create extensive training data for ML models. The proposed PCF model shows exceptional sensitivity to wavelength changes, with a recorded sensitivity of $\mathbf{6 0 0 0} \mathrm{nm} /$ RIU, making it highly effective for accurate mode prediction. The study evaluated nine ML models, including XGBoost, Random Forest, and Decision Tree, achieving accuracies of 0.9742,0.9645, and 0.9667, respectively. By integrating ML models, the core mode identification process is significantly faster and more efficient, offering major benefits for PCF SPR research. This work not only advances PCF sensor technology but also enhances efficiency and accuracy in various SPR-based applications.

关键词： Accuracy Sensitivity Photonic crystal fibers Predictive models Sensor phenomena and characterization Surface plasmon resonance Data models Sensors Finite element analysis Numerical models

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Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

arXiv

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arXiv 2024年

作者： Ye, Mang Shen, Wei Du, Bo Snezhko, Eduard Kovalev, Vassili Yuen, Pong C. The National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China United Institute of Informatics Problems of Belarus National Academy of Sciences National Academy of Sciences of Belarus Minsk Belarus Department of Computer Science Hong Kong Baptist University Hong Kong

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cross-domain collaboration. However, the corresponding research is scattered and lacks organization. To advance VFL research, this survey offers a systematic overview of recent developments. First, we provide a history and background introduction, along with a summary of the general training protocol of VFL. We then revisit the taxonomy in recent reviews and analyze limitations in-depth. For a comprehensive and structured discussion, we synthesize recent research from three fundamental perspectives: effectiveness, security, and applicability. Finally, we discuss several critical future research directions in VFL, which will facilitate the developments in this field. We provide a collection of research lists and periodically update them at https://***/shentt67/VFL_Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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Active Learning of One-Clock Timed Automata using Constraint Solving

arXiv

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arXiv 2022年

作者： Xu, Runqing An, Jie Zhan, Bohua State Key Lab. of Computer Science Institute of Software CAS Beijing China University of Chinese Academy of Sciences Beijing China Max Planck Institute for Software Systems Kaiserslautern Germany

Active automata learning in the framework of Angluin's L∗ algorithm has been applied to learning many kinds of automata models. In applications to timed models such as timed automata, the main challenge is to determine guards on the clock value in transitions as well as which transitions reset the clock. In this paper, we introduce a new algorithm for active learning of deterministic one-clock timed automata and timed Mealy machines. The algorithm uses observation tables that do not commit to specific choices of reset, but instead rely on constraint solving to determine reset choices that satisfy readiness conditions. We evaluate our algorithm on randomly-generated examples as well as practical case studies, showing that it is applicable to larger models, and competitive with existing work for learning other forms of timed models. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Context-Based Cultural Visits 11th

Context-Based Cultural Visits

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11th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Assis, Mariana Andrade, Maria Teresa Viana, Paula Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Polytechnic of Porto School of Engineering Porto Portugal

ISBN: (纸本)9783030736026

Mobile Augmented Reality (MAR) systems have emerged and greatly evolved in the last two decades. They have application in many domains, most notably in the field of Cultural Heritage (CH) and tourism, where people tend to rely on smartphones when visiting a new city to obtain additional information on the city landmarks. Expectations are that they obtain precise and tailored information to the visitor’s needs. Therefore, researchers started to investigate innovative approaches for presenting and suggesting digital content related to cultural and historical places. This article presents a novel MAR application, NearHeritage, which uses emergent technologies to assist visitors in finding and exploring Cultural Heritage. The research focuses on combining the use of context-awareness with Augmented Reality (AR). By sensing the context surrounding the user, the NearHeritage app discloses not only the list of nearby points-of-interest (POI) but also detailed information about the POIs in the form of AR content adapted to the user context. The solution presented uses built-in sensors of Android devices and takes advantage of various APIs (Foursquare API, Google Maps API and IntelContextSensing SDK) to retrieve information about the landmarks and the visitor context. Results from initial experimentation indicate that the concept of a context-aware MAR application can improve the user experience in discovering and learning more about Cultural Heritage, creating an interactive, enjoyable and unforgettable adventure. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

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IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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Context-aware Inductive Graph Matrix Completion with Sentence BERT

Context-aware Inductive Graph Matrix Completion with Sentenc...

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IEEE International Conference on Web Services (ICWS)

作者： Donghee Han Daehee Kim Keejun Han Mun Yong Yi Graduate School of Data Science Korea Advanced Institute of Science and Technology Daejeon Republic of Korea School of Computer Engineering Hansung University Seoul Republic of Korea Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

Existing graph neural network (GNN) based recommendation models depend highly on initial node features and graph structures. Most prior studies do not support the use of additional information on user-item interactions and adopt a transductive method, which has limitations for actual web service. To overcome these problems, we propose Context-aware Inductive Graph Matrix Completion (CGMC), which utilizes context vectors that represent user-item interactions and user-item bipartite graph structures in an inductive manner. Our method constructs context vectors from the review, timestamp, and rating and uses them as edge features of the user-item bipartite graph. Relations between homogeneous nodes are also constructed based on common neighbors. Our model combines context vectors and relational information using Context-aware Graph Attention Networks and Edge Fusion Graph Convolutional Networks. We conducted extensive experiments using six real-world datasets, and the results show that the proposed model achieves superior performances over other competing models. Furthermore, we analyzed the inductive characteristics of CGMC through the cross-domain transferability. The source code is available in the repository at [https://***/venzino-han/CGMC_SBERT].

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BFTRAND: Low-Latency Random Number Provider for BFT Smart Contracts

BFTRAND: Low-Latency Random Number Provider for BFT Smart Co...

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International Conference on Dependable systems and Networks (DSN)

作者： Jinghui Liao Borui Gong Wenhai Sun Fengwei Zhang Zhenyu Ning Man Ho Au Weisong Shi Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China Computer Science Wayne State University USA Neo Foundation Department of Computing The Hong Kong Polytechnic University China Computer and Information Technology Purdue University USA Hunan University China Computer and Information Sciences University of Delaware USA

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Random numbers play a crucial role in decen-tralized applications (dApps) like decentralized finance (DeFi) and non-fungible tokens (NFTs). However, their generation faces challenges due to blolckchain's deterministic and decentralized nature, risking smart contract security and ecosystem stability. Prior solutions, including Oracles, employing commit-execute schemes, suffer from higher transaction fees, extended processing times, and increased on-chain storage, compromising efficiency. This paper proposes a novel random number provider (RNP) protocol for smart contracts, eliminating dependencies on traditional commit-execute approaches. Furthermore, we systematically identify potential random number-related attacks on smart contracts, particularly Post-reveal Undo Attacks (PUAs), where attackers may reverse contract operations when randomness is unfavorable, and discuss the security requirements. Our protocol addresses these attacks by (1) incorporating distributed random beacons (D RBs) with consensus processes, bridging the semantic gap between DRB and consensus, and (2) thoroughly analyzing and classifying four types of PUA and offering robust mitigations, alongside presenting a security proof. Our experiments show the protocol significantly enhances response times and security for random number queries in smart contracts, slashing request fees by at least 89 % and reducing on-chain data by 76.4% versus current methods. This work advances the integration of DRB protocols and consensus mechanisms, securing and optimizing random number applications in dApps, thus fostering the creation of more dependable, robust systems.

关键词： Protocols Prevention and mitigation Smart contracts Finance Decentralized applications Stability analysis Security

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