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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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Intelligent Spectrum Management for UAV Swarm with Multiple Combat Missions: A DQN-Based Solution 16

Intelligent Spectrum Management for UAV Swarm with Multiple ...

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Hou, Yukai Liu, Zhijun Wang, Fan Zhou, Lingyun Shang, Tao Zhang, Rongqing Tongji University School of Software Engineering Shanghai China Xidian University School of Telecommunications Engineering Xi'an China South-Central Minzu University College of Electronics and Information Engineering Wuhan China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China

ISBN: (纸本)9798331506896

In unmanned aerial vehicles(UAV) swarm combat, efficient management of electromagnetic spectrum resources is crucial to ensure the operational performance of UAV swarm. However, in the presence of external radiations, the spectrum interference and competition between UAV onboard communication, radar, reconnaissance, and jamming services greatly affect the combat effectiveness of UAV swarm. Besides, because of the heterogeneity and coupling of different services, traditional spectrum allocation methods lack flexibility and occupy many computing resources in the central nodes. In this paper, by setting the threshold and the utility function for each service, we model the multi-service spectrum scheduling as a joint utility maximization problem. Then, we propose an intelligent and lightweight spectrum management method based on deep Q-network for multi-service cooperative spectrum scheduling. By dynamically and collaboratively allocating spectrum resources, conflicts within and between services are greatly reduced. Extensive simulations demonstrate that the proposed approach provides robust and efficient multi-service spectrum management. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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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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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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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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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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Demo: A Decision Support System for Task Offloading Optimization in Cloud-To-Far-Edge Kubernetes networks

Demo: A Decision Support System for Task Offloading Optimiza...

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2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

作者： Carmona-Cejudo, Estela Iadanza, Francesco Software Networks i2CAT Foundation Barcelona Spain Research and Development Laboratory Engineering Ingegneria Informatica S.p.A. Rome Italy

ISBN: (数字)9781665426718

ISBN: (纸本)9781665426718

Edge computing techniques together with application containerization can greatly reduce the end-To-end latency of delay-sensitive applications. Kubernetes is recognized as the most popular production-grade container orchestrator. In this paper, we demonstrate a decision support system (DSS) that supports custom Kubernetes quality of system (QoS) models, and which takes into account specific, heterogeneous hardware capabilities at all network nodes. Specifically, we demonstrate the implementation of a Kubernetes QoS model based on a three-Tier offloading decision algorithm (TTODA) and its integration into the DSS. © 2022 IEEE.

关键词： Decision support systems

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Location-Aware Beamforming Method for Time Varying Angle of Departure in 5G mmWave Ultra-Dense networks

Location-Aware Beamforming Method for Time Varying Angle of ...

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

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

ISBN: (纸本)9798350348293

The beamforming procedure is necessary in 5G New Radio (NR) Ultra-Dense networks (UDN) to achieve high antenna array gain and overcome path losses in millimeter wave (mmWave) radio channels. In this regard, this article discusses antenna radiation pattern (ARP) formation or adaptive beamforming in order to orient beam between gNB (gNodeB) and user equipment (UE) properly and track it during UE movement in the scenario of time varying angle of departure (AOD) using uniform rectangular array (URA) at gNB side. Simulation results of AOD evaluation according to the considered method is close to true value and is comparable in accuracy with the beam cycling method which, requires 16 times longer time for training period, thus, making it appropriate for beam orientation in vehicle scenarios. © 2023 IEEE.

关键词： Millimeter waves

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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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