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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Ma, Xinhong Zeng, Yehong Qi, Mingyang Si, Yali Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Changshu Institute of Technology Jiangsu Changshu215500 China

ISBN: (纸本)9789819623754

In the era of open cloud, cloud API is an important component and key enabling technology to achieve efficient data transmission, artificial intelligence algorithm empowerment, and software development cost reduction and efficiency. With the proliferation of cloud API with similar functionality, quality of service (QoS) becomes essential for differentiating cloud API performance, and QoS-driven prediction for cloud API has emerged as a critical topic for high quality cloud API selection. However, existing methods often inadequately utilize contextual information in resource-constrained scenario, limiting the improvement of cloud API prediction performance. We in this paper propose a geo-enhanced high-order feature interaction network (GHFIN) for cloud API QoS prediction. Firstly, a correlation analysis was conducted on real-world QoS dataset to explore the relationship between QoS data and geospatial context. Then, we design a geo-enhanced strategy that effectively utilizes the hierarchical relationships of geospatial entities (country, province, city, and district) through an offline real-time coordinate conversion module. On this basis, a geo-enhanced cloud API QoS prediction network GHFIN was designed, which aims to learn the high-order feature interactions through layer-wise feature stacking. Experimental results on two real-world QoS datasets illustrate the effectiveness of GHFIN compared to the classic and state-of-the-art methods, and our extensive analysis shows how the geospatial context and high-order feature interaction positively impact the performance of cloud API QoS prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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Deep Learning Based Uplink Precoding for High Speed Train Communications in FD-RAN

Deep Learning Based Uplink Precoding for High Speed Train Co...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zeyu Sun Jiwei Zhao Yunting Xu Jiacheng Chen Haibo Zhou Lian Zhao School of Electronic Science and Engineering Nanjing University Information Science and Electronic Engineering Zhejiang University Department of Strategic and Advanced Interdisciplinary Research Peng Cheng Laboratory Department of Electrical Computer Biomedical Engineering Toronto Metropolitian University

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

High speed train (HST) communications with multi-user multiple-input multiple-output (MU-MIMO) techniques have shown great potential in system performance improvements. However, the challenges caused by higher pilot overhead and the belated channel state information (CSI) feedback in such high mobility scenarios still need to be further addressed. To this end, fully-decoupled radio access network (FD-RAN) with novel location-based feedback-free transmission scheme and cooperative transmission/reception in separated downlink/uplink networks is regarded as a promising solution. In this paper, we study the uplink precoding design in FD-RAN for HST communications. To capture the inherent relation between location and precoding, the line-of-sight (LoS) channel is derived from location and then fed into a proposed precoding design neural network (PDNN) which learns to jointly optimize the precoding scheme for all the multi-antenna mobile relays (MRs) in uplink MU-MIMO. We adopt a custom loss function to optimize the spectrum efficiency (SE). Moreover, a joint signal reception method is given, also based solely on the LoS channel derived from location, so as to avoid frequent pilot transmission. Simulation results show the advantages of FD-RAN against other architectures and demonstrate that our proposed PDNN achieves better performance than traditional precoding scheme in high mobility scenarios.

关键词： Vehicular and wireless technologies Precoding OFDM system performance Simulation Neural networks Rician channels

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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Detail-Enhancing Framework for Reference-Based Image Super-Resolution

arXiv

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arXiv 2024年

作者： Wang, Zihan Xiong, Ziliang Tang, Hongying Yuan, Xiaobing Shanghai Institution of Micro-system and Information Technology University of Chinese Academy of Science China Computer Vision Laboratory Department of Electrical Engineering LiU Sweden School of Information Science and Technology Shanghai Tech University China

Recent years have witnessed the prosperity of reference-based image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Optimizing Acoustic Echo Cancellation with Variable Step Size in Adaptive Filtering

Optimizing Acoustic Echo Cancellation with Variable Step Siz...

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International Conference on advanced Technologies for Communications, ATC

作者： Yen-Yi Chen Jian-Hong Wang Po-Cheng Chan Kai-Wen Liang Ze-Yu Wang Jia-Ching Wang Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan School of Computer Science and Technology Shandong University of Technology Zibo Shandong China Advanced Technology Laboratory Chunghwa Telecom Laboratories

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

This paper presents an innovative approach to acoustic echo cancellation (AEC) by applying Variable Step Size (VSS) separately to each adaptive filtering technique: Normalized Least Mean Square (NLMS), and Proportionate Normalized Least Mean Square (PNLMS). This application is tailored to overcome the challenges of step size adjustment and signal sparsity, significantly enhancing system performance. We demonstrate that utilizing VSS with each algorithm independently allows for optimized control over convergence rates-larger step sizes quicken convergence while smaller sizes ensure smoother transitions. This method improves the robustness and efficiency of echo cancellation systems, removing the need for manual step size adjustments and proving highly effective in real-time applications.

关键词： Echo cancellers Filtering Heuristic algorithms system performance Adaptive filters Filtering algorithms Acoustics Robustness Stability analysis Convergence

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. The main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. The study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. The ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R2 score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. This significant R2 score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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Frequency-Self-Adaptive Radio Frequency Power Harvester Enabled by Shape-Reconfigurable Liquid Metal

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Electromagnetic science 2024年第2期2卷 91-103页

作者： Cheng Zhang Yuchao Wang Zebin Zhu Hai Lin Kun Wang Xintong Shi Yi Du Chaoyun Song Long Ren Qiang Cheng Key Laboratory of Materials for High-Power Laser Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China College of Physical Science and Technology Central China Normal UniversityWuhan 430000China Center of Quantum and Matter Science and School of Physics Beihang UniversityBeijing 100191China Department of Engineering King’s College LondonLondon WC2R 2LSUK State Key Laboratory of Radio Frequency Heterogeneous Integration College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518000China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and EngineeringWuhan University of TechnologyWuhan 430000China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

Radio frequency(RF)energy harvester as an efficient tool for capturing and converting the flourishing ambient RF energy provides a promising solution for long-term powering the wireless sensor networks and the Internet of things(IoTs).However,the actual distribution of the environmental RF signals is dynamically frequency-dependent due to the diverse wireless terminals only interacting with specified *** take full advantage of the RF energy carrying this characteristic,an intelligent RF energy harvester is in demand to automatically sense the frequency information of an incident signal and conduct the corresponding RF-to-direct current transformation ***,to the best of my knowledge,a frequency-self-adaptive RF harvester is first presented with the help of the shape-reconfigurable liquid metal,which can precisely identify and efficiently convert an arbitrary signal from the frequency span of 1.8 to 2.6 *** with a microcontroller unit and a tensile system,the dynamic functionality of the entire system is comprehensively demonstrated,showing promising potential to significantly advance various fields,including sustainable IoT applications,green wearable technologies,and self-powered devices.

关键词： Liquid metal Radio frequency power harvesting Frequency-self-adaptive Broadband rectification

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Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization

arXiv

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arXiv 2024年

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are reluctant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the over-reliance on a specific framework is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, data augmentation based on large language models faces two disadvantages: 1) the potential issues or toxicity in the augmented data;2) the expensive costs. Therefore, in this paper, we propose a novel data augmentation framework based on both large and small language models for debiasing opinion summarization. In specific, a small size of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on confusion degree and sentiment classification. Experiments have proved that our framework can effectively alleviate emotional bias same as using only large models, but more economically. Copyright © 2024, The Authors. All rights reserved.

关键词： Data assimilation

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A Secure Multiparty Quantum Homomorphic Encryption Scheme

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computers, Materials & Continua 2022年第11期73卷 2835-2848页

作者： Jing-Wen Zhang Xiu-Bo Chen Gang Xu Heng-Ji Li Ya-Lan Wang Li-Hua Miao Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan610025China Quantum Technology Lab&Applied Mechanics Group University of MilanMilan20133Italy Department of Computer Science Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurreyGU27XHUnited Kingdom Huawei Technologies Co.Ltd Shenzhen518129China

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private *** this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest ***,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and ***,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted ***,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic *** operation and the permission to access the data performed by the client and the server are clearly pointed ***,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

关键词： Quantum homomorphic encryption secure multiparty computation almost dishonest server security

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