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Mobile Health from Developers’ Perspective

SN computer Science 2024年第1期5卷 132页

作者： Oliveira, Pedro Almir M. Junior, Evilasio Costa Andrade, Rossana M. C. Santos, Ismayle S. Neto, Pedro A. Santos Group of Computer Networks Software Engineering and Systems (GREat) Federal University of Ceará Ceará Brazil Federal Institute of Maranhão (IFMA) Maranhão Brazil Laboratory of Software Optimization and Testing (LOST) Federal University of Piauí Piauí Brazil

This paper extends an initial investigation of eHealth from the developers’ perspective. In this extension, our focus is on mobile health data. Despite the significant potential of this development area, few studies try to understand the challenges faced by these professionals. This perspective is relevant to identify the most used technologies and future perspectives for research investigation. Using a KDD-based process, this work analyzed eHealth and mHealth discussions from Stack Overflow (SO) to comprehend this developers’ community. We got and processed 6082 eHealth and 1832 mHealth questions. The most discussed topics include manipulating medical images, electronic health records with the HL7 standard, and frameworks to support mobile health (mHealth) development. Concerning the challenges faced by these developers, there is a lack of understanding of the DICOM and HL7 standards, the absence of data repositories for testing, and the monitoring of health data in the background using mobile and wearable devices. Our results also indicate that discussions have grown mainly on mHealth, primarily due to monitoring health data through wearables and about how to optimize resource consumption during health-monitoring. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： eHealth mHealth Mining software repositories Stack overflow

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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

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engineering 2024年第2期33卷 178-189页

作者： Xiaoming Yuan Jiahui Chen Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen Qinhuangdao Branch Campus Northeastern UniversityQinhuangdao 066004China State Key Laboratory of Integrated Services Networks&the Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada SUSTech Institute of Future Networks Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility *** order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client *** enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for *** problem is decoupled into two convex *** to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration *** on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their *** simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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A Dynamic Selective Parameter Sharing Mechanism Embedded with Multi-Level Reasoning Abstractions 26

A Dynamic Selective Parameter Sharing Mechanism Embedded wit...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Yan He, Ying Ming, Zhong Yu, F. Richard Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Integrated Services Networks Xidian University China Shenzhen China

ISBN: (纸本)9781643684369

Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially constrain policy diversity and limit cooperation flexibility. Recent methods that group agents into distinct sharing categories often exhibit poor performance due to challenges in precisely differentiating agents and neglecting the issue of promoting cooperation among these categories. To address these issues, we introduce a dynamic selective parameter sharing mechanism embedded with multi-level reasoning abstractions (DSPS-MA). Our approach uses self-comparison sequences to infer agents' abstract concepts, defining the differences between agents and allowing them to dynamically select partners to share parameters based on these abstract concepts. We also design an intrinsic reward to offer comprehensive collaboration guidance for agents, and introduce a policy cosine similarity regularization term to ensure sufficient policy diversity. Empirical evaluations demonstrate that our approach yields higher returns and faster convergence than state-of-the-art methods. © 2023 The Authors.

关键词： Dynamics

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A Novel Association Rule Mining Method for Streaming Temporal Data

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Annals of Data Science 2022年第4期9卷 863-883页

作者： Zheng, Hui Li, Peng He, Jing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Nanjing China School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia

Streaming temporal data contains time stamps and values, challenging to quantify relationships of time stamps and corresponding values. Moreover, the characteristics and relationships of streaming temporal data are not invariable. Thus, it is impossible to analyse all data by a trained model at the beginning of data streams. Practically, the trained model to analyse streaming temporal data should change according to the increasing volume of data. Association rule mining, on the other hand, can find potential relationships from given data. This paper proposes an association rule mining method for streaming temporal data to discover potential relationships from streaming temporal data. Our experiments verify our proposed method. A public data set is applied to compare the performance of the proposed method and its counterpart. A small data set is also applied for two case studies to further illustrate our proposed method mine association rules with streaming temporal data with time stamps and corresponding values. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Association rule mining Streaming data Potential relationships Timestamp Value Data mining Association rule learning

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Analysis of the Spatial Smoothing Influence on the Perfomance of 2D-MUSIC for Ultra-Dense networks

Analysis of the Spatial Smoothing Influence on the Perfomanc...

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2023 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2023

作者： Grishin, Ilya Fokin, Grigoriy Sevidov, Vladimir Okuneva, Darina The Bonch-Bruevich SPbSUT Department of Communication Networks Saint Petersburg Russia The Bonch-Bruevich SPbSUT Laboratory of Software Defined Radio Saint Petersburg Russia S.M. Budyonny VAS Department of Radio Engineering Saint Petersburg Russia The Bonch-Bruevich SPbSUT Department of Software Engineering and Computer Facilities Saint Petersburg Russia

ISBN: (纸本)9798350348293

In this paper, we analyze the spatial smoothing as a method of signal processing, preceding the algorithm for determining the direction of arrival of 2D-MUSIC signals. The purpose of the method is to compensate the possible coherence of signals coming from different user devices to the uniform rectangular antenna array of the base station of the ultra-dense network operating in the millimeter wavelength range. The results of computational experiments allow to estimate the dependence of the performance of the 2D-MUSIC algorithm to the spatial smoothing procedure. © 2023 IEEE.

关键词： Direction of arrival

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Anonym-Recognizer: Relationship-preserving Face Anonymization and Recognition 3

Anonym-Recognizer: Relationship-preserving Face Anonymizatio...

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3rd International Workshop on Human-Centric Multimedia Analysis, HCMA 2022, held in conjunction with the ACM Multimedia 2022

作者： Peng, Chunlei Wan, Shuang Miao, Zimin Liu, Decheng Zheng, Yu Wang, Nannan School of Cyber Engineering Xidian University Xi'an China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China

ISBN: (纸本)9781450394925

With the widespread application of big data technology, we are exposed to more and more video monitoring. To prevent serious social problems caused by face data leakage, face anonymization has become an important kind of method to protect face privacy. The face anonymization mentioned in this paper refers to the anonymization generation of the visual appearance in face images. Existing face anonymization methods mainly focus on removing identity information. However, in the scenario of face recognition technology that needs to protect privacy, existing face anonymization technology makes anonymized faces that can no longer be used for face recognition, limiting the application scope of face anonymization. Therefore, when using face anonymization, it is equally important to ensure that the anonymized face images can still be used for downstream tasks such as face recognition. To this end, we propose Anonym-Recognizer, a relationship-preserving face anonymization and recognition method. Our method uses relationship cyphertext which can be any binary identity number representing the identity of the image owner and designs a generative adversarial network to perform face anonymization and relationship cyphertexts embedding. In our framework, we first use Visual Anonymizer to manipulate the visual appearance of the input image, then use Cyphertext Embedder to get the anonymized image with the identity information embedded. With the help of Anonym Recognizer, the face recognition system can extract the relationship cyphertexts from the anonymized image as the credentials to match the identity information. The proposed Anonym-Recognizer provides a new perspective for the recognition and application of anonymized face images. Experiments on the Megaface dataset show that our method can encourage a 100% recognition accuracy on anonymized faces while finishing the task of face anonymization with high qualitative and quantitative quality. © 2022 ACM.

关键词： Face recognition

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Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC Systems 16

Denoising Diffusion Model-Based Channel Estimation in IRS-As...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Yi, Lan Miao, Jiansong Zhang, Tao Yuan, Zhenhui Kang, Jiawen Niyato, Dusit Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Engineering University of Warwick CV4 7AL United Kingdom School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798350390643

In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition. © 2024 IEEE.

关键词： Least squares approximations

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming 42

SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shen, Kai Zhang, Dayou Zhu, Zi Zhang, Lei Wang, Fangxin Wang, Dan The Chinese University of HongKong The Future Network of Intelligence Institute Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Peng Cheng Laboratory China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350334142

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%. © 2023 IEEE.

关键词： Quality control

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Realization and Performance Tests of a Dual-Polarization Fiber Optic Gyroscope 8

Realization and Performance Tests of a Dual-Polarization Fib...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Zhou, Tong Zhu, Lanxin Cao, Yuwen Chen, Yanjun Huang, Huimin Li, Zhengbin School of Software and Microelectronics Peking University Beijing102600 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Interferometric fiber optical gyroscopes (IFOGs) have become one of the widely used sensors of inertial technology and rotational seismology, owing to their high precision and stability. In recent years, the dual-polarization IFOG, which allows two orthogonal polarization to work simultaneously, has been proved to achieve better measurement performance than the conventional minimum configuration through mutual compensation. In this work, a prototype of dual-polarization fiber-optic gyroscope is designed and realized. laboratory tests show the self-noise has reached 9 nrad/s/sqrt(Hz) over the frequency from 0.01 to 125 Hz and the angular random walk has achieved 2.6×10-5 deg/sqrt(hr). For an Open-loop IFOG, which does not need a confiscated feedback control system, it avoids the dead-zone of closed-loop IFOG. However, limited by the measurement range, as a result of the periodic and nonlinear response of open-loop demodulation, the scale factor of open-loop IFOG varies around 100~1000 parts per million (ppm) conventionally, which hinders its detection accuracy. In this work, with new measurement extension and linearity compensation methods, the prototype achieves an outstanding precision with scale factor nonlinearity up to 5 ppm within ±10 deg/s, and shows the potential of dual-polarization IFOGs for rotational seismology. © 2022 SPIE

关键词： Seismology

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