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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Tseng, Wei-Fu Chen, Kai-Chun Xiao, Zi-Hong Li, Yen-Huan Department of Mathematics National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan School of Medicine National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan

Online learning of quantum states with the logarithmic loss (LL-OLQS) is a quantum generalization of online portfolio selection (OPS), a classic open problem in online learning for over three decades. This problem also emerges in designing stochastic optimization algorithms for maximum-likelihood quantum state tomography. Recently, Jézéquel et al. (2022, arXiv:2209.13932) proposed the VB-FTRL algorithm, the first regret-optimal algorithm for OPS with moderate computational complexity. In this paper, we generalize VB-FTRL for LL-OLQS. Let d denote the dimension and T the number of rounds. The generalized algorithm achieves a regret rate of O(d2 log(d + T)) for LL-OLQS. Each iteration of the algorithm consists of solving a semidefinite program that can be implemented in polynomial time by, for example, cutting-plane methods. For comparison, the best-known regret rate for LL-OLQS is currently O(d2 log T), achieved by an exponential weight method. However, no explicit implementation is available for the exponential weight method for LL-OLQS. To facilitate the generalization, we introduce the notion of VB-convexity. VB-convexity is a sufficient condition for the volumetric barrier associated with any function to be convex and is of independent interest. © 2025 W.-F. Tseng, K.-C. Chen, Z.-H. Xiao & Y.-H. Li.

关键词： Stochastic systems

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method 30

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Yuan, Yankun Wang, Lin Xiao, Chonghui Liu, Zijuan Dang, Fan Wang, Xu Zhao, Haitian Miao, Xin Yanshan University School of Information Science and Engineering China National Computer Network Emergency Response Technical Team/Coordination Center of China China Tsinghua University Department of Automation and BNRist China Tsinghua University Global Innovation Exchange China Tsinghua University School of Software and BNRist China

ISBN: (纸本)9798331515966

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications. © 2024 IEEE.

关键词： Adaptive algorithm Anomaly detection Energy conservation IoT LSTM Models QoE

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QCACNN: A Quantum Convolutional Neural network Algorithm for Traffic Sign Recognition in Carbon-Intelligent Electric Vehicles

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IEEE Internet of Things Journal 2025年

作者： Qu, Zhiguo Xia, Yichen Sun, Le Liu, Wenjie Muhammad, Ghulam Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education School of Computer Science Nanjing210044 China Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Nanjing University of Information Science and Technology School of Software Nanjing210044 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Traffic sign recognition is essential for autonomous driving, enhancing driving efficiency and ensuring road safety. Traffic signs utilize colors and patterns to relay critical information, with color accuracy being especially significant. Traditional models, reliant on extensive data and computing resources, struggle to meet the efficiency demands of electric vehicles with carbon-intelligent computing. Leveraging the physical properties of quantum superposition states, quantum computing offers a solution with its unique parallel computing capabilities, potentially enhancing recognition efficiency and facilitating real-time processing. Quantum Convolutional Neural networks (QCNNs) show promise in processing large-scale image data with improved efficiency and accuracy. However, most of QCNN researches focus on grayscale image classification, with limited studies on multi-channel data. This paper introduces the Quantum Channel Attention Convolutional Neural network (QCACNN), which incorporates a Quantum Channel Attention Layer (QCAL) to enhance multi-channel data classification. The experimental results demonstrate that QCACNNs surpasses traditional QCNNs in traffic sign recognition, performing comparably to conventional CNNs and SENet models with fewer computing resources. Detailed performance analysis and ablation studies validate the effectiveness of each component within this architecture. Quantum noise resistance tests confirm the robustness of QCACNN, making it a viable solution for electric vehicles with enhanced scalability. © 2014 IEEE.

关键词： Quantum channel

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Deep-Learning-Based Risk Prediction with Urban Sensing Data for Consumer Driving Safety

Deep-Learning-Based Risk Prediction with Urban Sensing Data ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Kun-Yu Lin Pei-Yi Liu Chih-Lin Hu Sheng-Zhi Huang Yung-Hui Chen Lin Hui Department of Communication Engineering National Central University Taoyuan City Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taiwan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

With the provision of IoT-driven and user-provided sensing data sources in smart cities, we take advantage of deep learning techniques to analyze the spatio-temporal traffic data and predict traffic risks for driving safety. Our study continues to collect the traffic data from multiple sensing sources, and meanwhile adopts both CNN and LSTM to interpret the data collection in spatial and temporal dimensions. Thus, a novel traffic risk prediction scheme based on CNN and LSTM, named TRP-CL, is proposed to generate a traffic warning map of risks and hazard situations on a grid-scaled city map. Not only a theoretic formation but also an experimental implementation of the TRP-CL scheme are made to show the practical feasibility.

关键词： Deep learning Smart cities Soft sensors Transportation Data collection Spatial databases Hazards Long short term memory Consumer electronics

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ARTIFICIAL INTELLIGENCE ASSISTED SERVICE MARKETING USING DEEP ASSISTED NEURAL network

ARTIFICIAL INTELLIGENCE ASSISTED SERVICE MARKETING USING DEE...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Shanthana, S. Gopal, Vidhya Navulla, Durgaprasad Seal, Sudeshna Sohail, Mohammed Ali Paul, Gunti Anil Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering Sona College of Technology Tamilnadu Salem India Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India OmDayal Group of Institutions West Bengal Uluberia India Department of Computer and Network Engineering College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Mechanical Engineering Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India

ISBN: (纸本)9798350305708

Improving marketing abilities, the quality of one product, and one connections with one target audience are at the service marketing approach. The marketing of goods and services can both be considered to come within the scope of this category. This data is stored in a centralized location in these databases for ease of access and analysis. Based on the findings of this study, AI has played a crucial role in big data analytics, making it possible to foresee and provide guided experiences that are individualized based on the needs of customers. In this paper, we develop a AI assisted customer support system to deliver experiences that result in advocates and consumers that remain loyal over time, the usage of AI and predictive analytics is crucial. © 2023 IEEE.

关键词： artificial intelligence neural network Service marketing validation

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Real-time behavior recognition of animal: an IoT-based system design using acceleration data

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Multimedia Tools and Applications 2024年 1-26页

作者： Tran, Duc-Nghia Manh, Do Viet Van Thanh, Pham Shankar, A. Achyut Joshi, Kireet Tran, Duc-Tan Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam University of Fire Prevention and Fighting Hanoi100000 Viet Nam Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Center of Research Impact and Outcome Chitkara University Punjab Chandigarh India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam

By analyzing large amounts of animal behavior data, humans can assess cattle’s condition. It leads to research and development a field using accelerometers and machine learning algorithms to ‘study’ behavior from acceleration data. It is challenging to analyze animal behavior due to the activity complexity. For example, acceleration data of some cow behaviors (e.g., feeding and standing) are similar if these behaviors contain similar gestures. In practical, real-time applications even bring more challenging since it is necessary to minimize a data window for behavior consideration. Using short data window often reduces the performance of classification algorithms. This study proposes a new solution for the mentioned challenges by combining neck-mounted and leg-mounted accelerometers, then using the vector of the dynamic body acceleration (VeDBA) and Mean features of synchronized acceleration data for the Decision Tree algorithm. The proposed method is demonstrated that performs well (0.94 accuracy, 0.91 sensitivity, 0.90 positive predictive value) within a low computational complexity algorithm and short data window (3-second data window, at the rate of one sample per second). It is particularly suited for the case of a real-time IoT-based system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Accelerometers

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A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

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computer Systems science & engineering 2022年第6期41卷 1043-1056页

作者： Xiaorui Zhang Wenfang Zhang Wei Sun Xingming Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszowVoivodeship100031Poland

In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the *** order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this *** proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square ***,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

关键词： 3-D medical watermarking robust watermarking PCA BF-PSO

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Introduction to the Special Issue on Advanced Security for FutureMobile Internet:A Key Challenge for the Digital Transformation

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computer Modeling in engineering & sciences 2024年第12期141卷 1907-1909页

作者： Ilsun You Xiaofeng Chen Vishal Sharma Gaurav Choudhary Department of Financial Information Security Kookmin UniversitySeoul-si02707Republic of Korea School of Network and Information Security Xidian UniversityXi’an710071China School of Electronics Electrical Engineering and Computer ScienceQueen’s University Belfast(QUB)BelfastBT71NNUK Centre for Industrial Software(CIS) The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSønderborg6400Denmark

Mobile internet technologies have transformed our daily lives,allowing us to connect,communi-cate,and access various services and applications anytime and *** technologies are set to play a significant role in the next generation of digital transformation,further increasing their impact by integrating with emerging technologies like 6G,quantum computing,and generative AI.

关键词： services Internet internet

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LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection

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computers, Materials & Continua 2025年第2期82卷 1901-1924页

作者： Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling School of Information and Cybersecurity People’s Public Security University of ChinaBeijing100038China Cyber Investigation Technology Research and Development Center The Third Research Institute of the Ministry of Public SecurityShanghai201204China Department of Cybersecurity Defense Beijing Police CollegeBeijing102202China School of Computer Science Henan Institute of EngineeringZhengzhou451191China

With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal *** deep learning-based text classifiers offer promise,they often exhibit suboptimal performance in fine-grained detection tasks,primarily due to imbalanced datasets and insufficient model representation *** address this challenge,this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS *** the data processing stage,Large Language Models(LLMs)are employed for data augmentation,mitigating dataset *** the data input stage,both word-level and character-level features are utilized as model inputs,enhancing the richness of features and preventing information loss.A dual-stream Transformer serves as the backbone network in the learning representation stage,complemented by a graph-based feature fusion *** the output stage,both supervised classification cross-entropy loss and supervised contrastive learning loss are used as multi-task optimization objectives,further enhancing the model’s feature *** results demonstrate that the proposed method significantly outperforms baselines on a publicly available Chinese malicious SMS dataset.

关键词： Transformers malicious SMS multi-task learning large language models

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Quantum-Inspired Optimization for Task Scheduling in Software Development Projects 4

Quantum-Inspired Optimization for Task Scheduling in Softwar...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Ou, Chia-Ho Li, Yu-Hong Chen, Chih-Yu Wu, Chi-Hsuan Tsai, Yu-Chen Yan, Zhi-You Chang, Ching-Ray National Pingtung University Department of Computer Science and Information Engineering Pingtung Taiwan Chung Yuan Christian University Quantum Information Center Taoyuan Taiwan Chung Yuan Christian University Department of Physics Taoyuan Taiwan

ISBN: (纸本)9798350343236

Software project development, characterized by numerous tasks and several engineers, necessitates effective project scheduling and personnel allocation for successful and timely completion. Tackling the inherent complexities of Software Project Scheduling (SPS) including personnel quality requirements and capability constraints is of paramount importance for software companies. This study aims to engineer robust project scheduling to enhance task completion efficiency, reduce resource waste, and ensure the punctual delivery of project milestones. Our proposed solution models the problem as a Software Project Scheduling Problem (SPSP), subsequently transformed into a Quadratic Unconstrained Binary Optimization (QUBO) model using quantum-inspired techniques. This model is then solved using a digital annealing device. We examine our proposed quantum-inspired method's effectiveness in solving SPSP through this experimental implementation, comparing its performance with the Simulated Annealing (SA) algorithm. The experimental findings reveal that the objective function, encapsulating the weighted sum of all job personnel costs and job end times, produced superior outcomes under the Digital Annealing (DA) algorithm compared to the SA algorithm. Moreover, the DA algorithm demonstrated greater stability and reduced runtime as the volume of input data expanded. © 2023 IEEE.

关键词： Simulated annealing

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