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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data privacy

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk The Ohio State University Electrical and Computer Engineering Department United States Bts Group Turkey Istanbul Technical University Department of Ai and Data Engineering Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

ISBN: (纸本)9798350368369

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025 IEEE.

关键词： Authentication Deep Reinforcement Learning Identity-Based Cryptography Proximal Policy Optimization Task Offloading Vehicular Twin Networks

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A Novel Mode Selection-Based Fast Intra Prediction Algorithm for Spatial SHVC

A Novel Mode Selection-Based Fast Intra Prediction Algorithm...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yu Sun Weisheng Li Lele Xie Xin Lu Frederic Dufaux Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science University of Central Arkansas Computing Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University CNRS CentraleSupélec Laboratoire des Signaux et systèmes Université Paris-Saclay School of Information and Communication Engineering University of Electronic Science and Technology of China

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss.

关键词： Machine learning algorithms Costs Signal processing algorithms Machine learning Gaussian distribution Prediction algorithms Encoding

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Development of a Sensor-Based Wearable Fetal and Maternal Health Monitoring System

Development of a Sensor-Based Wearable Fetal and Maternal He...

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Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Yugashi Jayalath MWP Maduranga Valmik Tilwari WMSRB Wijayarathna WAAM Wanniarachchi Debashree Sharma School Of Pharmacy & Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Department of Data Science Faculty of Computing and IT Sri Lanka Technology Campus Padukka Sri Lanka Department of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India Department of Information Technology General Sir Jhon Kotelawala Defence University Colombo Sri Lanka

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

According to the World Health Organization, approximately 360,000 pregnant women are registered annually in Sri Lanka. Among these, about 328,000 babies are born healthy, while 5,800 are born with birth defects, some of which are severe and impact daily life. To address the Sri Lankan stillbirth rate of 6.01%, continuous monitoring of fetal and maternal health is essential. Although commercial devices exist to fulfill this requirement, their high costs and safety issues often preclude regular use. To overcome these challenges, there is a need for a passive, wearable, and portable monitoring system for fetal and maternal health. This system should track key parameters such as fetal heart rate, fetal movements, and maternal heart rate to mitigate health risks for both the fetus and the mother. In this study, we developed a device using the Arduino, appropriate sensors, and a microcontroller to monitor these vital parameters. The device is user-friendly and suitable for regular use. MATLAB was utilized for the signal processing requirements of the proposed device. Test results demonstrated that fetal movement detection was accurate, although potential errors may arise during real-time testing. For pulse sensor readings, an average percentage difference of 3.025% was observed compared to pulse oximeter readings, likely due to placement errors and sensor instability in the proposed device.

关键词： Pregnancy Pulse oximeter Tracking Signal processing Real-time systems Sensors Safety Biomedical monitoring Monitoring Testing

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Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro

Digital Innovations Development: Causal factors and relation...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Ivana Ognjanović Luka Laković Ramo Šendelj Laszlo Bokor Peter A. Kara Christoph Reich Manav Madan Milovan Roganović Emmanouil Zouilas John Mantas Jevto Eraković Tanja Radusinović Nada Rakočević Faculty for Information Systems and Technologies University of Donja Gorica Podgorica Montenegro Humanistic Studies University of Donja Gorica Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Institute of Data Science Cloud Computing and IT Security Furtwangen University Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens Goudi Greece Chamber of Economy of Montenegro Podgorica Montenegro

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts.

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Investigation into the Use of Novel Pretreatments in the Fermentation of Alaria Esculenta by Lactiplantibacillus Plantarum and Kombucha Scoby

SSRN

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SSRN 2023年

作者： Healy, Laura Zhu, Xianglu Dong, Gaoya Selli, Serkan Kelebek, Hasim Sullivan, Carl Tiwari, Uma Tiwari, Brijesh K. Teagasc Food Research Centre Ashtown Dublin Ireland Department of Food Science and Environmental Health Technological University Dublin Dublin Ireland Technological University Dublin Ireland Department of Food Engineering Faculty of Agriculture Cukurova University Adana01330 Turkey Department of Nutrition and Dietetics Faculty of Health Sciences Cukurova University Adana01330 Turkey Department of Food Engineering Faculty of Engineering Adana AlparslanTurkes Science and Technology University Adana Turkey Faculty of Computing Digital and Data School of Mathematics and Statistics Technological University Dublin Dublin Ireland

An investigation into the impact of novel pretreatments on fermentation indicators of seaweed was carried out. High pressure processing (HPP), ultrasound probe (USP) and ultrasound bath (USB) technologies were employed as a pretreatment ahead of fermentation of brown seaweed species Alaria esculenta by Lactiplantibacillus plantarum (LAB) and a symbiotic culture of bacteria and yeast (SCOBY). pH was significantly different (p 0.05). Organic acids differed significantly (p © 2023, The Authors. All rights reserved.

关键词： Fermentation

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Cyclic Quantum Teleportation of Two-Qubit Entangled States by using Six-Qubit Cluster State and Six-Qubit Entangled State

arXiv

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arXiv 2024年

作者： Slaoui, A. El Kirdi, M. Ahl Laamara, R. Alabdulhafith, M. Chelloug, S.A. Abd El-Latif, A.A. LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Cyclic quantum teleportation schemes requires at least the existence of three collaborators acting all as senders and receivers of quantum information, each one of them has an information to be transmitted to the next neighbour in a circular manner. Here, new cyclic quantum teleportation scheme is proposed for perfectly transmitting cyclically three arbitrary unknown two-qubit states (α, β and γ) among the three collaborators. In this scheme, Alice can send to Bob the quantum information contained in her two-qubit state α and receive from Charlie the quantum information contained in the two-qubit state in his possession γ and similarly, Bob can transmit to Charlie the quantum information contained in his two-qubit state β through a quantum channel of twelve-qubit state consisting of a six-qubit cluster state and a six-qubit entangled state by sequentially and cyclically performing Bell state measurements. Subsequently, each one of the three participants can afterwards retrieve his own desired two-qubit state using classical channel and by performing appropriate unitary Pauli operators and we have shown that our proposed scheme performs efficiently. © 2024, CC0.

关键词： Qubits

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A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics

arXiv

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arXiv 2023年

作者： Zhang, Dongyan Yang, Wuyue Wen, Wanqi Peng, Liangrong Zhuge, Changjing Hong, Liu School of Mathematics Sun Yat-Sen University Guangdong Guangzhou510275 China Beijing Institute for Scientific and Engineering Computing Department of Mathematics Faculty of Science Beijing University of Technology Beijing100124 China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing101408 China College of Mathematics and Data Science Minjiang University Fujian Fuzhou350108 China

Background On May 5th, 2023, WHO declared an end to the global COVID-19 public health emergency, which means a significant transition from global critical emergency response activities to long-term sustained COVID-19 prevention and control. At this very moment, we make a retrospective review on various control measures taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 7, 2020, and evaluate their impacts on the epidemic dynamics quantitatively. Methods We collect and evaluate the control measures implemented by each country. The SEIR-QD model, as a representative for general compartment models, is used to fit the epidemic data, enabling the extraction of crucial model parameters and dynamical features. The mediation effect of the SEIR-QD model is revealed by using the mediation analysis with structure equation modeling for multiple mediators operating in parallel. The inherent impacts of these control policies on the transmission dynamics of COVID-19 epidemics are clarified and compared with results derived from both multiple linear regression and neural-network-based nonlinear regression. Results A dramatic distinction in the control measures is observed among major countries/territories around the world, which largely affects the spreading rate and infected population size in each country. Several key dynamical features, like the normalized cumulative numbers of confirmed/cured/death cases on the 100th day and the half time, show statistically significant linear correlations with the control measures, thereby confirming the dramatic impacts of control measures and their respective implementation intensity on the epidemic. Most importantly, the SEIR-QD model, especially its infection rate and protection rate, has been confirmed to exhibit a statistical significant mediation effect between the control measures and dynamical features of epidemics. The mediation effect along the pathway from control measures in Category 2 to four

关键词： COVID-19

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Multi-Layer Transmitting RIS-Aided Receiver for Collaborative Jamming and Anti-Jamming Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Junjie Yang, Liang Hao, Wanming Ahmad, Ishtiaq Liu, Hongwu Shu, Feng Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering China Jishou University School of Communication and Electronic Engineering China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Czech Technical University in Prague Faculty of Electrical Engineering Department of Telecommunication Engineering Prague166 27 Czech Republic Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Hainan University School of Information and Communication Engineering Haikou570228 China Nanyang Technological University College of Computing and Data Science Singapore

In this paper, we propose a novel architecture for a multi-layer active-passive cascade transmitting reconfigurable intelligent surface (RIS)-aided receiver. We aim at enhancing the scalability of antenna dimensions and energy efficiency for user equipment (UE), as well as improving the amplitude freedom of antenna gain. This architecture integrates reflecting RIS for applications in both jamming and anti-jamming scenarios. We formulate a problem to maximize the worst-case spectral efficiency (SE) of the UE, based on imperfect channel state information (CSI) of the malicious device, thereby ensuring the SE of our UE while disrupting the signal reception of the illegal UE. To address the inherently non-convex nature of the formulated problem, we propose an alternating optimization framework, which decomposes the main problem into several subproblems. Specifically, we uniformly discretize the uncertain domains to obtain robust CSI, allowing us to determine an optimal receiver vector. To balance computational complexity and performance, we propose solutions for the subproblems of base station beamforming and coefficients with different patterns of RIS. Furthermore, to effectively disrupt malicious inter-device communication while avoiding detection and localization, we develop a collaborative interference pattern incorporating silent interference and non-interference protocols. Importantly, the pattern carefully balances performance with the overhead of CSI acquisition. Finally, simulation results validate the efficiency of the proposed algorithms, demonstrating that the proposed architecture achieves better jamming and anti-jamming performance. © 2002-2012 IEEE.

关键词： Jamming

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A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

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IEEE Internet of Things Journal 2025年

作者： Lamaakal, Ismail Essahraui, Siham Maleh, Yassine Makkaoui, Khalid El Ouahbi, Ibrahim Bouami, Mouncef Filali El-Latif, Ahmed A. Abd Almousa, May Peng, Jialiang Sultan Moulay Slimane University Laboratory LaSTI ENSAK Khouribga Morocco Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Heilongjiang University School of Data Science and Technology Harbin150080 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore639798 Singapore

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA. © 2014 IEEE.

关键词： Embedded systems

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