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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Kexin Yao Han Li Ming Liu Bo Gao Ke Xiong Pingyi Fan Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

This paper proposes an Attention-enhanced ResNet based Wi-Fi Gesture Recognition (ARES-WiGR), in which it first extracts Doppler Frequency Shift (DFS) vector parameters from Channel State Information (CSI) via conjugate multiplication and antenna selection. Then, the DFS spectrogram is obtained by Short Time Fourier Transform (STFT), and the DFS spectrogram features are used as the input of the proposed neural network model. Moreover, with the attention mechanism, ARES-WiGR is able to automatically recognize the important information and achieve better recognition effect. The performance is examined in real environment, which shows that the proposed ARES-WiGR effectively extracts gesture features and improves recognition accuracy to about 96%.

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Quantum Algorithms and Experiment Implementations Based on IBM Q

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computers, Materials & Continua 2020年第11期65卷 1671-1689页

作者： Wenjie Liu Junxiu Chen Yinsong Xu Jiahao Tang Lian Tong Xiaoyu Song Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Engineering Jiangsu Maritime InstituteNanjing211100China Department of Electrical and Computer Engineering Portland State UniversityOregon97207USA

With the rapid development of quantum theory and technology in recent years,especially the emergence of some quantum cloud computing platforms,more and more researchers are not satisfied with the theoretical derivation and simulation verification of quantum computation(especially quantum algorithms),experimental verification on real quantum devices has become a new *** this paper,three representative quantum algorithms,namely Deutsch-Jozsa,Grover,and Shor algorithms,are briefly depicted,and then their implementation circuits are presented,*** program these circuits on python with QISKit to connect the remote real quantum devices(i.e.,ibmqx4,ibmqx5)on IBM Q to verify these *** experimental results not only show the feasibility of these algorithms,but also serve to evaluate the functionality of these devices.

关键词： Quantum algorithms implementation circuit IBM Q QISKit program

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A Comprehensive Survey on Self-Interpretable Neural networks

arXiv

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arXiv 2025年

作者： Ji, Yang Sun, Ying Zhang, Yuting Wang, Zhigaoyuan Zhuang, Yuanxin Gong, Zheng Shen, Dazhong Qin, Chuan Zhu, Hengshu Xiong, Hui Guangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211100 China Computer Network Information Center Chinese Academy of Sciences Beijing100083 China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making scenarios. Post-hoc interpretability, which provides explanations for pre-trained models, is often at risk of robustness and fidelity. This has inspired a rising interest in self-interpretable neural networks, which inherently reveal the prediction rationale through model structures. Despite this progress, existing research remains fragmented, relying on intuitive designs tailored to specific tasks. To bridge these efforts and foster a unified framework, we first collect and review existing works on self-interpretable neural networks and provide a structured summary of their methodologies from five key perspectives: attribution-based, function-based, concept-based, prototype-based, and rule-based self-interpretation. We also present concrete, visualized examples of model explanations and discuss their applicability across diverse scenarios, including image, text, graph data, and deep reinforcement learning. Additionally, we summarize existing evaluation metrics for self-interpretation and identify open challenges in this field, offering insights for future research. To support ongoing developments, we present a publicly accessible resource to track advancements in this domain: https://***/yangji721/Awesome-Self-InterpretableNeural-network. Copyright © 2025, The Authors. All rights reserved.

关键词： Neural network models

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Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

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Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

arXiv

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arXiv 2023年

作者： Zhang, Lihao Sun, Haijian Sun, Jin Parasuraman, Ramviyas Ye, Yinghui Hu, Rose Qingyang The School of Electrical and Computer Engineering University of Georgia AthensGA United States The School of Computing University of Georgia AthensGA United States Shanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications China The Electrical and Computer Engineering Department Utah State University LoganUT United States

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency. © 2023, CC BY.

关键词： Channel state information

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Lingchang Kong Xinyu Wang Hao Zhou Fengyu Zhou Shenyao Jiang Peide Zhu Qi Song Zhi Liu LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186 % while reducing the mechanical rotations by 83.3 %.

关键词： Location awareness Coils Transmitters Magnetic resonance imaging Prototypes Reinforcement learning Wireless power transfer Transforms Sensors Impedance

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Job-SDF: a multi-granularity dataset for job skill demand forecasting and benchmarking 24

Job-SDF: a multi-granularity dataset for job skill demand fo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xi Chen Chuan Qin Chuyu Fang Chao Wang Chen Zhu Fuzhen Zhuang Hengshu Zhu Hui Xiong University of Science and Technology of China Computer Network Information Center Chinese Academy of Sciences Baidu Inc. Institute of Artificial Intelligence Beihang University and SKLSDE School of Computer Science Beihang University AI Thrust The Hong Kong University of Science and Technology (Guangzhou) and Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong SAR

ISBN: (纸本)9798331314385

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark.

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Spectrum Sharing Towards Delay Deterministic Wireless network: Delay Performance Analysis

arXiv

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arXiv 2023年

作者： Wei, Zhiqing Zhang, Ling Nie, Gaofeng Wu, Huici Zhang, Ning Meng, Zeyang Feng, Zhiyong The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

To accommodate Machine-type Communication (MTC) service, the wireless network needs to support low-delay and low-jitter data transmission, realizing delay deterministic wireless network. This paper analyzes the delay and jitter of the wireless network with and without spectrum sharing. When sharing the spectrum of the licensed network, the spectrum band of wireless network can be expanded, such that the delay and jitter of data transmission are reduced. The challenge of this research is to model the relation between the delay/jitter and the parameters such as node distribution, transmit power, and bandwidth, etc. To this end, this paper applies stochastic geometry and queueing theory to analyze the outage probability of the licensed network and the delay performance of the wireless network with and without spectrum sharing. By establishing the M/G/1 queueing model for the queueing of the Base Station (BS) in the wireless network, the downlink delay and jitter are derived. Monte Carlo simulation results show that the spectrum sharing reduces the delay and jitter without causing serious interference to the licensed network, which can lay a foundation for the application of spectrum sharing in delay deterministic wireless network supporting MTC *** Codes 94A99 Copyright © 2023, The Authors. All rights reserved.

关键词： Queueing theory

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The Classification Method of EEG Motor Imagery Based on INFO-LSSVM 22

The Classification Method of EEG Motor Imagery Based on INFO...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Xinrong Wang Abdelkader Nasreddine Belkacem Penghai Li Zufeng Zhang Jun Liang Dongdong Du Chao Chen Tianjin University of Technology China Department of Computer and Network Engineering College of Information Technology UAE University UAE Tianjin Medical University General Hospital China China Academy of Industrial Internet China

ISBN: (纸本)9781450397056

For the current situation that the classification accuracy of EEG motor image data is not high in the BCI system, a vector weighted average algorithm optimization algorithm is proposed, and the optimized least squares support vector machine algorithm is proposed to classify the EEG motor image data. A motor imagination EEG experimental paradigm was designed and compared with the unoptimized LSSVM and three other typical classification methods on the same dataset. The experimental data were band-pass filtered by the fourth-order Butterworth filter of 0.5-30Hz, and the electrical interference was removed by independent component analysis. The HHT features obtained by empirical mode decomposition (EMD) and Hilbert Yellow transform (HHT) in the time-frequency domain were input into INFO-LSSVM for classification. Compared with dense feature fusion convolutional neural network (DFFN), Restricted Boltzmann machine optimized support vector Machine classifier (RBM-SVM) and public space pattern based artificial Neural network (CSP-ANN) classification algorithm, the highest classification accuracy of the proposed algorithm is 92.13%, and the average accuracy is 90.325%. It can be seen that compared with the existing algorithms with higher performance, the proposed algorithm effectively improves the classification accuracy and can better classify and identify EEG signals, which provides a new optimization idea for people's EEG signal classification.

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Cipher Text to Secure Li-Fi System Using Hybrid Encryption Algorithm

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2025年第2期6卷

作者： Ahmed, Mohammed M. Alnajjar, Satea H. Department of Computer Engineering College of Engineering Al-iraqia University Baghdad Iraq Department of Network Engineering College of Engineering Al-iraqia University Baghdad Iraq

It is important to pay attention to develop our security systems, due to the increase in cyberattacks and the development of their methods and the work to develop quantum computers capable of penetrating the solution of complex equations of encryption algorithms. The need to develop protection methods has emerged in line with the development of hackers to ensure the security and safety of users’ data. In this research, work was done on the modern Li-Fi technology based on comparing the values with Kim’s model and a distance of more than 13 km was reached in the fresh air. This technology provides security and privacy inside rooms because it sends signals through light, so the signal cannot be modified from the outside. However, the threat remains through the transmission medium. Therefore, a hybrid algorithm was created from chacha20 with a key size of 256 and RSA with a key size of 2048, which guarantees us a high level of security, making it immune to threats. The chacha20 algorithm provides encryption speed and security, while the RSA algorithm increases the level of security of the algorithm, as the total time taken to encrypt data from the sender and decrypt it from the receiver was 0.036 seconds. This ensures high and fast performance for this hybrid algorithm. © 2025 The Author(s).

关键词： Qubits

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