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arXiv 2025年

作者： Kutukcu, Basar Xie, Sinan Baidya, Sabur Dey, Sujit Department of Electrical and Computer Engineering University of California San DiegoCA United States Department of Computer Science and Engineering University of Louisville KY United States

Nvidia Jetson boards are powerful systems for executing artificial intelligence workloads in edge and mobile environments due to their effective GPU hardware and widely supported software stack. In addition to these benefits, Nvidia Jetson boards provide large configurability by giving the user the choice to modify many hardware parameters. This large space of configurability creates the need of searching the optimal configurations based on the user’s requirements. In this work, we propose JExplore, a multi-board software and hardware design space exploration tool. JExplore can be integrated with any search tool, hence creating a common benchmarking ground for the search algorithms. Moreover, it accelerates the exploration of user application and Nvidia Jetson configurations for researchers and engineers by encapsulating host-client communication, configuration management, and metric measurement. © 2025, CC BY.

关键词： Space research

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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning

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Discover Applied sciences 2025年第6期7卷 1-34页

作者： Bijoy, Md. Hasan Imam Mia, Md. Jueal Rahman, Md. Mahbubur Arefin, Mohammad Shamsul Dhar, Pranab Kumar Shimamura, Tetsuya Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Faculty of Science and Engineering Waseda University Tokyo Japan Department of Information and Computer Sciences Saitama University Saitama Japan

Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnostics can assist nephrologists in early detection and accurate classification, which are essential for improving patient outcomes. This study utilized clinical features of CKD to develop and evaluate six base machine learning classifiers (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron) alongside two novel ensemble models (MKR Stacking and MKR Voting) for CKD prediction and classification. The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, Information Gain, and Relief. The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. Such integration has the potential to transform CKD management, facilitating early interventions and personalized treatment plans through advanced machine-learning applications.

关键词： k-nearest neighbors

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AFFORDANCES IN ACTION UP TO USER NEEDS: READING DESIGN-ORIENTED SOCIOLOGY IN THE DESIGN OF A RELATIONAL TRUSTWORTHY GOVERNABLE PLATFORM 18

AFFORDANCES IN ACTION UP TO USER NEEDS: READING DESIGN-ORIEN...

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18th International Conferences IADIS Information Systems 2025 and 23rd International Conference on e-Society 2025

作者： Jacucci, Gianni Department of Information Engineering and Computer Science - DISI University of Trento Via Sommarive TN Povo9I-38123 Italy

ISBN: (纸本)9789898704658

This focused and dedicated short research paper examines the architectural design work of a Trustworthy Governable Platform (TGP), that redefines the information paradigm for institutional communication platforms-particularly in relational enterprises-where complex social dynamics are at play. Examples include addressing accountability and ensuring contextual integrity. We adopt a design-oriented sociological perspective to uncover how the platform's creator ensured that its social use and meaning align with the specific situational needs from the outset. The study begins by examining the key features of social interactions that characterise relational enterprises, as identified and articulated by the author of the design process through selected theoretical approaches. Next, we analyse the communicative affordances of technology in action that are necessary to realise these social interaction features. Finally, we explore the design elements of the supporting information and communication infrastructure that the author deemed essential to enable the appropriate options for action. These elements ensure users experience the intended social usage and meaning of the technology in the context of relational enterprises. The purpose of the essay is to stimulate discussion on what we deem a most promising enterprise. TGP has been under active technical development for a number of years and, at the time of writing, is undergoing initial trials for deployment and appropriation. © 2025 Proceedings of the International Conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Communications on Information Complex Adaptive System Entrepreneurial Management & Organisation Cybernetics Relational Enterprises Socio-Cybernetics Theory

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Jinpeng Zhao, Chunna Yang, Jing Huang, Yaqun Yang, Yaoyuan Yee, Por Lip School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data;2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model. © 2025 IEEE.

关键词： dynamic graph learning Fractional Residual decomposition spatial-temporal graph Traffic prediction

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Reliability Assessment of VSM-Based Microgrid Incorporating Transient Stability

Reliability Assessment of VSM-Based Microgrid Incorporating ...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Ram Prasad Pandey Nga Nguyen Electrical Engineering and Computer Science University of Wyoming Laramie WY

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Microgrids (MGs) with low inertia are more susceptible to instability from faults and disturbances compared to large power grids. This leads to an increased likelihood of instability when they move from pre-fault to post-fault situations. Traditional reliability analysis often overlooks transient stability and assumes that the system will stabilize following a contingency. To avoid overestimating reliability indices, it is important to integrate transient stability criteria alongside adequacy assessments. However, accurately modeling the dynamic behavior of MG components, like inverter-based resources, controllers, etc., faces significant challenges, particularly when considering nonlinear factors like voltage and current limitations, as well as control system interactions. This paper addresses these challenges by investigating the dynamic modeling of MGs and developing a more comprehensive reliability assessment framework incorporating transient stability. As the transmission lines of microgrids are more vulnerable to failure, their outage and capacity are also included in addition to adequacy during reliability assessment. A direct method is adopted for transient analysis in the integrated reliability framework. The proposed reliability assessment approach not only aids in making informed operational decisions following major contingencies but also supports long-term planning for more reliable MG operations.

关键词： Stability criteria Power system dynamics Contingency management Microgrids Power system stability Planning Nonlinear dynamical systems Reliability Transient analysis Voltage control

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High-Q AlGaAs-on-insulator microresonators for spontaneous parametric down-conversion

High-Q AlGaAs-on-insulator microresonators for spontaneous p...

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Smart Photonic and Optoelectronic Integrated Circuits 2025

作者： Tan, Bo Xue Wang, Jiantao Poon, Andrew W. Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781510684881

Aluminum gallium arsenide (AlGaAs) is a promising photonic platform for photon-pair generation through spontaneous parametric down-conversion (SPDC) or spontaneous four-wave mixing due to its high optical nonlinearities. In this paper, we report our progress in the design and fabrication of high-quality-factor waveguide-coupled microring resonators in an AlGaAs-on-insulator (AlGaAsOI) platform using i-line (365 nm) photolithography. We design the waveguide geometries for the modal-phase matching for SPDC. Our preliminary results suggest the potential for efficient photon-pair generation in an AlGaAsOI platform for quantum photonic applications. © 2025 SPIE.

关键词： Four wave mixing

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Solar Irradiance Forecasting Using Deep Learning: Impact of Hyperparameter Optimization

Solar Irradiance Forecasting Using Deep Learning: Impact of ...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Sanjeeb Humagain Nga Nguyen Electrical Engineering and Computer Science University of Wyoming Laramie WY

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

The balance of supply and demand is pivotal in ensuring efficient and reliable power grid utilization. With the growth of demand, the integration of renewable energy resources (RERs) into the power grid is increasing phenomenally. Because of the random nature of RERs, the power grid is vulnerable to frequency and voltage stability issues. Enhancing the performance of generation forecasts is one of the efficient ways to deal with these issues. An accurate generation forecast will facilitate better planning to reduce the impacts caused by the unpredictable nature of RERS. Implementing deep learning (DL) methods in forecasting is becoming more popular as it has been proven to generate forecasts with high accuracy. This paper implements a DL method for solar irradiance forecasting using hyperparameter optimization (HPO). Solcast dataset is utilized to validate the efficacy of the proposed method. The model performance with activation functions, rectified linear unit, Leaky $\mathbf{ReLu}$ , and exponential linear unit with and without HPO is compared. Additionally, Bayesian and random search optimization methods are implemented, and their accuracy is also compared. The result shows the significant enhancement of the model performance after HPO, proving its importance in deep learning applications. Finally, the activation function and the optimization method based on accuracy and time of execution are suggested.

关键词： Solar irradiance Deep learning Accuracy Supply and demand Optimization methods Voltage Hyperparameter optimization Power grids Stability analysis Forecasting

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Big Data Analytics for Healthcare Resource Allocation

Big Data Analytics for Healthcare Resource Allocation

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sharma, Brijesh Kumar Jayant, Khel Prakash Pujari, Purvi Vikram, Devendra Saraswat, Manish Kathuria, Kajol Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engieering UP Ghaziabad India Dy Patil University Vijay Patil School of Management Department of Management Navi Mumbai India Abes Institute of Technology Computer Science & Engieering UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Gla University UP Mathura India Dronacharya Group of Institutions Dept Computer Science and Information Technology UP Greater Noida India

ISBN: (纸本)9798331508685

Big Data Analytics for Healthcare Resource Allocation is the new mantra as it integrates vast amounts of complementary data to allocate healthcare resources efficiently. There is an increasing demand for healthcare services, and resources are in limited supply, so doing something as simple as allocating resources can ensure that care quality remains high. The process includes gathering and organizing large datasets from several resources, including electronic health records, patient demographics, service utilization and detailed analysis to generate meaningful findings. Utilizing advanced statistical and analytical techniques, by definition, amongst others, Big Data Analytics can help you decipher the data to show you patterns in resource utilization (how resources are being used), areas where inefficiencies occur which have gone unnoticed long enough that cause them become ripe for incremental year over year cost savings targets or staff reactions if not corrected as then gain media attention likely) some even assembled through different techniques to predict future needs based on past use cases. The advantages of this are manifold. It will help identify high-risk patients, enhance clinical decision-making, and facilitate evidence-based resource allocation. It can also help quantify how well-existing resource distribution strategies work and influence resource planning. Big Data Analytics for Healthcare Resource Allocation: Better Outcome, Cutting Cost and Effective Utilization of Resources This have the potential to revolutionize healthcare resource allocation and improve national healthcare systems' efficiency and effectiveness. © 2025 IEEE.

关键词： Electronic health record

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PEP: An Accurate and Efficient Pupil Detection Framework Based on Pupil Edge Points

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IEEE Sensors Journal 2025年

作者： Zhang, Wei Liu, Yu Lv, Linfeng Wang, Xiang Li, Ziqiang Liu, Lei Li, Bin Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China Carnegie Mellon University Department of Electrical and Computer Engineering Pittsburgh United States Nanjing University of Information Science and Technology School of Computer Science Nanjing China

With the rapid development of headmounted devices, eye tracking as an emerging human-computer interaction technology, has gained increasing importance. However, pupil detection, the core algorithm in eye tracking, suffered low accuracy in complex scenarios and long runtime, which limits the frame rate. This paper proposes PEP (neural network based on Pupil Edge Points), an efficient pupil detection framework that not only achieves high detection accuracy but also significantly reduces computational costs, thereby addressing the performance bottlenecks of existing methods in challenging environments. The core innovation of the PEP framework lies in its focus on detecting edge-representative points (the points at the edge of the pupil) along the pupil edge, a method more precise than traditional full-region segmentation and better suited for accurate pupil ellipse fitting. Additionally, PEP enhances the accuracy of pupil edge point predictions through two key features: (1) a loss function that includes natural ellipse prior regularization term and (2) a data augmentation strategy that randomly masks edges. We evaluated PEP against state-of-the-art (SOTA) model-based and learning-based methods across multiple datasets. The evaluation results show that PEP achieved superior overall performance regarding pupil detection rate and accuracy. Moreover, PEP’s parameter number and computational cost are 80% lower than other learning-based methods, making it highly efficient. The proposed framework is practical, scalable, and demonstrates exceptional potential for pupil detection and gaze-tracking applications. © 2001-2012 IEEE.

关键词： Virtual reality

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RL with Balanced Reward and Masking Mechanism for Multi-NUMA Virtual Machine Scheduling 19

RL with Balanced Reward and Masking Mechanism for Multi-NUMA...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Gao, Yucen Cheng, Yunlong Ping, Chan Tin Gao, Xiaofeng Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331507817

With the rapid development of data center and cloud computing, the importance of resource management is increasing in recent years. In this paper, we focus on the virtual machine scheduling problem in resource management, that is, how to allocate the limited virtual machine resources when a request arrives. Existing works usually model the virtual machine scheduling problem as a bin-packing problem, but they do not consider the multi-NUMA architecture servers that have been widely used in recent years. Compared with the traditional symmetric multiprocessing architecture, the multi-NUMA architecture has the advantages of faster access time and higher energy efficiency, which is more suitable for dealing with today’s scenarios with a large number of computing requirements. In this paper, we first formalize the multi-NUMA virtual machine scheduling problem as a combinatorial optimization problem and explain why it is suitable to be solved by reinforcement learning. A reinforcement learning model called BM-DQN is then proposed, which designs a balanced reward function suitable for the multi-NUMA architecture and adopts a masking mechanism to screen out the optional servers in each step of reinforcement learning, so as to improve the solution performance. Experimental results on the open-source real-world virtual machine scheduling dataset of Huawei Cloud show that BM-DQN is superior to the existing methods in terms of the number of completed requests. © 2025 IEEE.

关键词： Resource allocation

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