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arXiv 2025年

作者： Liang, Sichu Zhang, Linhai Zhu, Hongyu Wang, Wenwen He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Department of Informatics King’s College London United Kingdom School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China School of Electrical and Computer Engineering Carnegie Mellon University United States The Alan Turing Insitute United Kingdom

Medical question answering requires extensive access to specialized conceptual knowledge. The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical knowledge through large-scale corpus retrieval and uses this knowledge to guide a general-purpose large language model (LLM) for generating answers. However, existing retrieval approaches often overlook the importance of factual knowledge, which limits the relevance of retrieved conceptual knowledge and restricts its applicability in real-world scenarios, such as clinical decision-making based on Electronic Health Records (EHRs). This paper introduces RGAR, a recurrence generation-augmented retrieval framework that retrieves both relevant factual and conceptual knowledge from dual sources (i.e., EHRs and the corpus), allowing them to interact and refine each another. Through extensive evaluation across three factual-aware medical question answering benchmarks, RGAR establishes a new state-of-the-art performance among medical RAG systems. Notably, the Llama-3.1-8BInstruct model with RGAR surpasses the considerably larger, RAG-enhanced GPT-3.5. Our findings demonstrate the benefit of extracting factual knowledge for retrieval, which consistently yields improved generation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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The Classification of Timor Weaving Using Artificial Neural Network

The Classification of Timor Weaving Using Artificial Neural ...

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International Conference on informatics and Computing (ICIC)

作者： Mendarissan Aritonang Adi Setiawan Krismiyati Theophilus Wellem Dept of Computer Science Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Dept. Mathematics and Data Science Faculty of Science and Mathematics Satya Wacana Christian University Salatiga Indonesia Dept. Informatics & Computer Engineering Education Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Informatics Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.

关键词： Training Accuracy Social networking (online) Neurons Artificial neural networks Feature extraction Weaving Fabrics Pattern recognition Web sites

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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MGC: Multiple-Gray-Code for 3D NAND Flash based High-Density SSDs

MGC: Multiple-Gray-Code for 3D NAND Flash based High-Density...

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IEEE Symposium on High-Performance computer Architecture

作者： Yina Lv Liang Shi Qiao Li Congming Gao Yunpeng Song Longfei Luo Youtao Zhang School of Computer Science and Technology East China Normal University Shanghai China Ministry of Education Software/Hardware Co-Design Engineering Research Center School of Informatics Xiamen University Xiamen China Computer Science Department University of Pittsburgh Pittsburgh USA

QLC (4-bit-per-cell) and more-bit-per-cell 3D NAND flash memories are increasingly adopted in large storage systems. While achieving significant cost reduction, these memories face degraded performance and reliability issues. The industry has adopted two-step programming (TSP), rather than one-step programming, to perform fine-granularity program control and choose gray-code encoding, as well as LDPC (Low-Density Parity-Check Code) for error correction. Different flash manufacturers often integrate different gray-codes in their products, which exhibit different performance and reliability characteristics. Unfortunately, a fixed gray-code encoding design lacks the ability to meet the dynamic read and program performance requirements at both application and device *** this paper, we propose MGC, a multiple-gray-code encoding strategy, that adaptively chooses the best gray-code to meet the optimization goals at runtime. In particular, MGC first extracts the performance and reliability requirements based on application-level access patterns and detects the reliability degree of SSD. It then determines the appropriate gray-code to encode the data, either from host/user application or due to garbage collection, before writing the pages to the flash memory. MGC is integrated in FTL (flash translation layer) and enhances the flash controller to enable runtime gray-code arbitration. We evaluate the proposed MGC scheme. The results show that MGC achieves better performance and lifetime guarantee compared with state-of-the-arts and introduces little overhead.

关键词： Performance evaluation Three-dimensional displays Runtime Programming Writing Encoding Parity check codes

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Augmented reality for children as a learning media for introduction to historical site

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AIP Conference Proceedings 2024年第1期3116卷

作者： Z. E. Ferdi Fauzan Putra Hamidillah Ajie Claudia Shindi Mersa Ali Idrus Department of Informatics and Computer Engineering Education Universitas Negeri Jakarta Jakarta Indonesia

This research aimed to produce an augmented reality Android-based for historical site named Pesanggrahan Putri Tujuh, which is suitable to be used as an learning media for children. The target of the study was children aged 9-12 years in Dumai city, Riau. The method used in this study is a Multimedia Development Life Cycle (MDLC) with only 5 (five) stages: concept, design, material collecting, assembly, and testing. Feasibility testing of this product used the black box method. The test results showed that product validation by experts got a value of 100% and media experts of 92.6%. In comparison, the product evaluation results by users aged 9-12 years in Dumai City obtained a value of 89.71%. The conclusion is that the product that has been developed is suitable to be used as learning media for the historical site named Pesanggrahan Putri Tujuh.

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Quality Grading of Beef using Deep Convolutional Neural Network 9

Quality Grading of Beef using Deep Convolutional Neural Netw...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Sharma, Aarav Choudhury, Tanupriya Sarkar, Tanmay Mohanty, Madhusmita Bansal, Nikunj Mohanty, Sachi Nandan Informatics Cluster School of Computer Science Uttarakhand Dehradun2480007 India Cse Dept. Uttarakhand Dehradun India Graphic Era Hill University Cse Dept. Uttarakhand Dehradun248002 India Malda Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Department of Food Processing Technology Malda732102 India Andhra Pradesh Amaravati India VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati5222237 India

ISBN: (纸本)9798350327502

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy. © 2023 IEEE.

关键词： computer vision

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Fistula Condition Monitoring System by the Noise Changes Dynamics

Fistula Condition Monitoring System by the Noise Changes Dyn...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： N. I. Yumagulov D. A. Fedorov D. V. Topolsky Dep. for Education in Physics and Mathematics Nizhnevartovsk State University Nizhnevartovsk Russia Department of Informatics and Computer Engineering Surgut State University Surgut Russia Department of Electronic Computing Machines South Ural State University (NRU) Chelyabinsk Russia

The paper presents an automated system for collecting and processing data for diagnosing the condition of an arteriovenous fistula. In the fistula, due to increased blood flow, characteristic noises and vibrations of the vessel walls occur. Using the data acquisition device, the acoustic noise of the arteriovenous fistula is recorded. The system processes and analyzes the measured data, evaluates the main indicators of the fistula condition. Changing the main indicators allows you to evaluate the dynamics of changes in the fistula condition. The developed system can be used for remote, long-term and continuous diagnostics of blood vessels in patients on hemodialysis.

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Improving partial weight bearing compliance with a smart insole: Validity, reliability, and feasibility study

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Foot and Ankle Surgery 2025年

作者： Avcı, Ender Ersin Akgün, Gazi Uygur, Mehmet Esat Polat, Mine Gülden Demirbüken, İlkşan Marmara University Health Sciences Faculty Department of Physiotherapy Education and Informatics Istanbul Turkey University of Tennessee at Chattanooga College of Engineering and Computer Science (CECS) Department of Engineering Management & Technology /Mechatronics United States Istanbul Medeniyet University Medicine Faculty Surgical Sciences Orthopaedic and Traumatology Istanbul Turkey

Background: Partial weight bearing (PWB) plays a critical role in early stages of rehabilitation following lower-extremity orthopedic surgery. However, it has been reported patients often have difficulty complying with PWB restrictions, particularly in home setting. The newly designed smart insole system aims to address this issue by remotely monitoring the patient's weight-bearing loads and providing real-time feedback for non-compliant weight-bearing to support PWB compliance. The aim of this study was to assess the validity and reliability of the system in measuring weight-bearing loads and to investigate its feasibility in improving PWB compliance. Methods: The research involved two phases: an experimental design to test validity and reliability in a clinical setting, and a randomized controlled design to test feasibility with remote monitoring at home. Validity and reliability were assessed on a healthy subject using simultaneous load measurements from a smart insole and a force plate under three metronome conditions (30,40, and 60bpm). In the feasibility phase, twelve patients who had undergone microfracture surgery for talar osteochondral lesions were recruited at 4 weeks postoperatively. Participants used Android smartphones and met Ferkel & Scaglione classification criteria I, IIA, and IIB. They were randomized into groups with feedback (FB) or without feedback (NFB) and their compliance with PWB limits was monitored remotely 24-hours for one week using the Insole system. Results: The insole demonstrated high reliability (SEM:1.67 N, ICC_1.1:0.97) and, excellent agreement with the force plate (R²=0.9175, p < 0.001). The FB group achieved higher compliance with prescribed PWB thresholds compared to the NFB group. Conclusions: The newly developed insole system is a valid and reliable rehabilitation tool for accurately measuring weight bearing loads. The system's ability to provide continuous remote patient monitoring and real-time feedbac

关键词： Feedback Gait Mobile Applications Patient Compliance Postoperative Care Rehabilitation

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Reliable Indoor Localization in Multi-Building Environments: Leveraging Environment-Invariant and Position-Related Features

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IEEE Internet of Things Journal 2025年

作者： Long, Wenhan Wen, Xinlong Liu, Hao Li, Mengyao Li, Songquan Wu, Yonghui Chen, Fuxiang Liu, Lu Zhu, Rongbo Huazhong Agricultural University College of Informatics Wuhan430070 China Ministry of Education Engineering Research Center of Intelligent Technology for Agriculture Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom University of Exeter Department of Computer Science Exeter United Kingdom

Received Signal Strength Indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3D indoor environments, including cross-floor and multi-building structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What’s more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor positioning model predictions. To address these issues, we propose EP-3DLoc, a novel 3D indoor localization framework that combines an Environment-Invariant feature-based Data Completion (EIC) method with a Position-Related feature-based Localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multi-scale RSSI signal processing (raw data and image-like data) with a multi-task network that analyzes location relationships, enhancing localization accuracy in 3D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multi-building environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. © 2014 IEEE.

关键词： Floors

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