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2024 IEEE International Conference on Big Data, BigData 2024

作者： Berger, Armin Bashir, Ali Hamza Berghaus, David Mowmita Afsan, Nazia Grigull, Lorenz Fendrich, Lara Hogl, Henriette Ernst, Gundula Schmidt, Ralf Bascom, David Lagones, Tom Anglim Deuber, Tobias Bell, Thiago Lubbering, Max Sifa, Rafet Fraunhofer Iais Department of Media Engineering Germany University of Bonn Department of Computer Science Germany Federal Ministry of Education and Research West-AI Germany Lamarr Institute Germany University Hospital Bonn Center for Rare Diseases Germany Children's Network for Chronic Illnesses and Disabilities Germany Medical School Hannover Department of Medical Psychology Germany Department of Health Queensland Australia Griffith University - School of Medicine Australia

ISBN: (纸本)9798350362480

We present RepLLaMA, a neural ranking model for optimizing patient matching in rare disease communities. Using data from *** consisting of over two thousand profiles and over ten thousand ratings, our bi-encoder architecture maps profiles to 4096-dimensional vectors, enabling efficient similarity computations. The system processes unstructured symptom descriptions and structured responses, incorporating expert-guided LLM enhancements. Results show Top-10 Recall of 49.36% $(\pm 2.03)$, surpassing baselines while maintaining generalization. The implementation provides a scalable solution for rare disease patient matching, addressing computational complexity challenges. © 2024 IEEE.

关键词： Diseases

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Advancing Personalized Medicine: A Scalable LLM-based Recommender System for Patient Matching

Advancing Personalized Medicine: A Scalable LLM-based Recomm...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Berger, Armin Berghaus, David Bashir, Ali Hamza Grigull, Lorenz Fendrich, Lara Lagones, Tom Anglim Högl, Henriette Ernst, Gundula Schmidt, Ralf Bascom, David Deußer, Tobias Bell, Thiago Lübbering, Max Sifa, Rafet Fraunhofer Iais Department of Media Engineering Germany University of Bonn Department of Computer Science Germany Federal Ministry of Education and Research West-AI Germany Lamarr Institute Germany University Hospital Bonn Center for Rare Diseases Germany Department of Health Queensland Australia Griffith University School of Medicine Australia Children's Network for Chronic Illnesses and Disabilities Germany Medical School Hannover Department of Medical Psychology Germany

ISBN: (纸本)9798350362480

This study explores efficient algorithms to enhance user matching in ***, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse SPLADE embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as TopicBERT for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform. © 2024 IEEE.

关键词： Personalized medicine

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Comprehensive Assessment of Open Science Practices in Indoor Positioning: Open Data, Code and Material

IEEE Journal on Indoor and Seamless Positioning and Navigati...

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IEEE Journal on Indoor and Seamless Positioning and Navigation 2025年 3卷 175-194页

作者： Anagnostopoulos, Grigorios G. Barsocchi, Paolo Crivello, Antonino Pendao, Cristiano Silva, Ivo Torres-Sospedra, Joaquin HES-SO Geneva School of Business Administration Geneva Switzerland National Research Council of Italy Institute of Information Science and Technologies Italy University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila real Portugal University of Minho Algoritmi Research Centre Guimarães Portugal Department of Computer Science Universitat de Valéncia Burjassot46100 Spain Valencian Graduate School and Research Network of Artificial Intelligence VALGRAI ValÉncia4602 Spain

Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of Open Science practices. These considerations are particularly relevant to the research field of Indoor Positioning. Open Data and Open Code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of Open Science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN conference. The work thoroughly examines the Open Data and Code usage, and the use of other types of Open Materials while performing a particular, close-up review of the Open Data that are leveraged in these studies. Our findings reveal that 21.7% of papers use Open Research Data (ORD), 8.3% utilize Open Code, and 20.2% incorporate other Open Materials. However, only 6.8% of papers provide both Open Data and Code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of Open Science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of Indoor Positioning. © 2023 CCBY.

关键词： Codes Reproducibility of results Open data Standards Replicability Software Reviews Indoor navigation Europe Surveys

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Radon CLF:A Novel Approach for Skew Detection Using Radon Transform

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computer Systems Science & engineering 2023年第10期47卷 675-697页

作者： Yuhang Chen Mahdi Bahaghighat Aghil Esmaeili Kelishomi Jingyi Du Laboratory for Control Engineering Xi’an University of Science&TechnologyXi’anChina Computer Engineering Department Imam Khomeini International UniversityQazvinIran MOE Key Laboratory for Intelligent and Network Security Xi’an Jiaotong UniversityXi’anChina

In the digital world,a wide range of handwritten and printed documents should be converted to digital format using a variety of tools,including mobile phones and ***,this is not an optimal procedure,and the entire document image might be *** conversion effects due to noise,motion blur,and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition(OCR)*** Document Image Analysis Systems(DIAS),skew estimation of images is a crucial *** this paper,a novel,fast,and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function(CLF),so-called Radon CLF,was *** Radon CLF model aims to take advantage of the properties of Radon *** Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon *** significant performance indicators,including Mean Square Error(MSE),Mean Absolute Error(MAE),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Measure(SSIM),Accuracy,and run-time,were taken into consideration when assessing the performance of our *** addition,a new dataset named DSI5000 was constructed to assess the accuracy of the CLF *** two-dimensional image signal and the Radon space have been used in our simulations to compare the noise *** results show that the proposed method is more effective than other approaches already in use,with an accuracy of roughly 99.87%and a run-time of 0.048(s).The introduced model is far more accurate and timeefficient than current approaches in detecting image skew.

关键词： Document image analysis skew detection Radon transform pattern recognition

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Graph-Based Proximal Policy Optimization Empowered Adaptive Task Scheduling Leveraging Cloud-Edge Collaboration for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Li, Peisong Yi, Meng Iqbal, Muddesar Wang, Xinheng Xiao, Ziren Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou215123 China Southeast University School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Prince Sultan University Renewable Energy Laboratory Communications and Networks Engineering Department College of Engineering Riyadh11586 Saudi Arabia CIP Technology ltd United Kingdom

The rapid advancement of IoT, AI, and edge computing has led to a significant increase in consumer devices and computation tasks, with new electronics incorporating these technologies to enhance services like VR/AR and autonomous driving, requiring real-time processing for safety and efficiency. However, recent research has focused on optimizing IoT task scheduling and resource allocation through various methods, yet overlooks the dynamic nature of IoT environments, fails to adapt to changing device counts and movements, and often ignores task completion rate in favor of minimizing latency and energy cost. In this context, an adaptive task scheduling and resource allocation strategy is proposed for Edge IoT systems, based on the designed Graph-based Proximal Policy Optimization (GPPO) algorithm. Firstly, the GPPO algorithm enhances PPO for adaptive task scheduling in the fluctuating MEC scenarios, adjusting for the varying number of nearby edge servers. Secondly, it accounts for consumer mobility by opting for local task execution if the consumer risks moving outside the edge server's range, ensuring result reception. Thirdly, it prioritizes task completion rate to increase the number of tasks finished within their acceptable duration. Experimental results demonstrated that the proposed method outperforms traditional methods. © 1975-2011 IEEE.

关键词： Scheduling algorithms

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Enriching molecular graph representation via substructure learning

Enriching molecular graph representation via substructure le...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： HongHao Wang Acong Zhang Ping Li School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Network Department China Mobile Jiangxi Co. Ltd. Ganzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Continuous molecular graph representations are highly useful for effective molecule property predictions. However, learning graph-specific structure information remains challenging. Current graph neural network models typically aggregate all node embeddings of a graph into a single vector, which, while being an efficient compressor, may not capture the high-level function-related substructure information, e.g., motifs. Moreover, the nodes that are loosely relevant to the graph representation may lead to the sub-optimal representation learning. Towards these limitations, in this paper we propose a new approach to enrich molecular graph representation, where the most relevant motif are selected to represent the graph at a high level and the key-node spanned subgraph is constructed to filter out irrelevant nodes. By comparing to recently proposed unimodal graph neural network models and multi-modal methods, we show that our method achieves state-of-the-art performance on molecular property prediction task.

关键词： Representation learning Accuracy Biological system modeling Aggregates Predictive models Graph neural networks Vectors Bioinformatics

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Rerouting in Cognitive Optical networks

Rerouting in Cognitive Optical Networks

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IEEE International Conference on Communications (ICC)

作者： Anny Xijia Zheng Vincent W.S. Chan Department of Electrical Engineering and Computer Science Claude E. Shannon Communication and Network Group Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We investigate the design of an efficient rerouting scheme for dynamic bursty high granularity traffic as a part of an agile network management and control scheme in cognitive optical networks. An efficient rerouting scheme allows more efficient use of wavelengths resources. The design includes the fast reconfiguration of wavelengths in the existing lit and spatially routed fibers, and the setting up and lighting of new fibers. Rerouting is necessary to maintain users' quality of service when the network load exceeds the load limit of the primary path. Our design is to trigger reconfiguration when a load threshold is crossed, and the threshold is determined by requirements on both the delay and the blocking (i.e. no available lightpath) probability. The delay is dependent on the probability of exhausting all wavelengths on the primary route, the availability of secondary routes, the holder (buffer) size, and the topology of the network. A dynamic rerouting scheme implemented with a shortest-path algorithm with immediate wavelength availability is proposed to find the alternate paths. It is desirable that the alternate paths for rerouting have small numbers of hops and are disjoint with other busy paths. In addition, a good candidate network topology should have high edge-connectivity. Wavelength reservation for rerouted traffic does not maximize wavelength utilization. Traffic with different sizes should be broken up into multiclasses with dedicated partitioned resources and will have different maximum load thresholds for triggering rerouting to meet the performance requirements.

关键词： Integrated optics network topology Heuristic algorithms Wavelength assignment Lighting Quality of service Optical fiber networks

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A Wearable High-Voltage Functional Electrical Stimulator with Sub-20 ns Switching Time for Stimulation with Reduced Pain

A Wearable High-Voltage Functional Electrical Stimulator wit...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Ruizhe Tang Haiduo Wang Bruno Almeida Xilin Liu Department of Electrical and Computer Engineering (ECE) University of Toronto Toronto ON Canada KITE Research Institute Toronto Rehabilitation Institute - University Health Network (UHN) Toronto ON Canada

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

This paper presents a wearable high-voltage functional electrical stimulation (FES) device. Compared to conventional FES devices, the proposed design aims to offer users a more comfortable therapeutic experience. This is enabled by achieving a sub- 20 ns fast switching time, activating muscles but fewer pain receptors. The FES device is equipped with a high compliance voltage up to 135 V, designed to accommodate different muscle groups and is suitable for users with varying skin conductance levels. At the core of the FES system is a switched capacitor (SC) stimulator for fast switching, and a flyback converter responsible for stepping up the battery voltage. The system incorporates only off-the-shelf components, which significantly lowers the prototyping cost. The proposed design methods are applicable across a wide range of applications in neural rehabilitation.

关键词： Buck converters Pain Voltage Switches High-voltage techniques Muscles Iron Skin Sensors Switching circuits

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The Proof is in the Pudding: Decision-Oriented Machine-Type Wireless Video Transmission

The Proof is in the Pudding: Decision-Oriented Machine-Type ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jie Song Yun Gao Dan Wu Liang Zhou Yi Qian Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China Institute of Communications Engineering Army Engineering University of PLA Nanjing China Department of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the rise of the Internet of Everything, machine-type wireless video applications like intelligent surveillance are increasingly becoming mainstream network services. However, the massive transmission of video streams heavily burdens wireless networks. Existing methods typically establish a correlation between the transmitted content and the quality of experience or service, making it challenging to balance trade-offs among decision quality, bandwidth utilization, and latency requirements. To address this issue fundamentally, this paper proposes a novel perspective by designing wireless video transmission strategies from the angle of decision quality, where only the content that impacts the decision outcomes is transmitted. The highlight of this paper is designing a lightweight yet efficient binary classifier that predicts whether the current content will change decision outcomes based on content discrepancies, measured only through pixel-level differences and macroblock-level similarity. Furthermore, these discrepancies can be used to distinguish the background, thereby further saving bandwidth by reusing the background information. Additionally, a fine-tuning module is incorporated to flexibly update the classifier model, ensuring adaptability across various scenarios. Extensive results demonstrate that the proposed strategy achieves over 81% redundant bandwidth savings while maintaining decision quality and catering to strict latency requirements on computation-constrained devices.

关键词： Correlation Wireless networks Surveillance Current measurement Bandwidth Streaming media Quality of experience Internet of Things Global communication Streams

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Robust Particle Swarm Optimization Algorithm for Modeling the Effect of Oxide's Thermal Properties on 304l Stainless Steels Amig Welds

SSRN

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SSRN 2024年

作者： Djoudjou, Rachid Touileb, Kamel Hedhibi, Abdeljlil Chihaoui Boubaker, Sahbi Ouis, Abousoufiane Abdo, Hani Said Department of Mechanical Engineering College of Engineering in Al-Kharj Prince Sattam bin Abdulaziz University P.O. 655 Al-Kharj 16273 Saudi Arabia National Engineering School of Sousse University of Sousse ENISO Tunisia Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah21959 Saudi Arabia King Saud University P.O. 800 Al-Riyadh11421 Saudi Arabia Department of Metallurgical and Materials Engineering Faculty of Petroleum and Mining Engineering Suez University Suez43512 Egypt

The MIG welding process has several advantages, which explain its increased use in different welding industries such as automobiles, marines and structure. AMIG (activating metal inert gas) technique is a variant of MIG process where the same equipment is used except of the deposition of a thin layer of flux prior to the welding operation. The main purpose of this work is to investigate the influence of physical properties of individual oxide fluxes on stainless steel 304L welding morphology as well as to what extent it may help in identifying the relationship between the weld depth penetration and weld aspect ratio and the input physical oxides’ properties. Five kinds of oxides, namely;TiO2, SiO2, Fe2O3, Cr2O3, Mn2O3 were tested on butt joint design without edges preparation. A robust algorithm based on the particle swarm optimization (PSO) technique is used to optimally tune the models’ parameters such as the quadratic error between the actual outputs (depth and aspect ratio) and the estimated by the models’ outputs is minimized. The obtained results have shown the proposed PSO model to be first robust against uncertainties in measurement devices and modeling errors and second to successfully relate and quantify the weld depth penetration and weld aspect ratio to thermal oxides properties. © 2024, The Authors. All rights reserved.

关键词： Thermodynamic properties

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