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检索条件"任意字段=18th International Conference on Software Engineering and Knowledge Engineering"

共 35581 条记录，以下是671-680 订阅

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Multi-Step-Ahead Prediction of User Movement on 5G Networks Using the Long Short-Term Memory Method 18

Multi-Step-Ahead Prediction of User Movement on 5G Networks ...

引用

18th international conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Timoteo, Adriel Benyamin, Fabian Rafinanda Anargyarahman, Naufal Bhanu Iskandar Yuliana, Hajiar School of Electrical Engineering and Informatics Telecommunications Engineering Program Bandung Institute of Technology Bandung Indonesia Jenderal Achmad Yani University Faculty of Engineering Department of Electrical Engineering Cimahi Indonesia

ISBN: (纸本)9798331527686

Since the initial launch of 5G networks in 2018, 5G technology has rapidly expanded to cover about 40% of the world's population, offering higher bandwidth, faster connectivity, and lower latency than 4G. However, due to its higher frequency operation, 5G necessitates more frequent handovers, making efficient handover performance crucial. this research focuses on enhancing handover efficiency by predicting user movement using Long Short-Term Memory (LSTM) models for multi-step-ahead prediction. the model created will utilize 60 seconds of historical data to predict 10 seconds ahead, with a target to achieve an average prediction accuracy of less than 20 meters with a latency under 100 ms. the study employs TensorFlow for model training, leveraging the Grab-Posisi dataset, and utilizes Plotly and TensorBoard for result visualization. Six model variations were trained, with the 16-unit model trained for 3106 epochs performing best, achieving 16.99 meters accuracy and 31 ms latency. the findings suggest that LSTM is effective for predicting user movement in 5G networks, with potential accuracy improvements through extended training epochs. © 2024 IEEE.

关键词： Prediction models

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Extending the Meta Model for Enterprise Systems Dynamics from a software Tooling Perspective 15

Extending the Meta Model for Enterprise Systems Dynamics fro...

引用

15th international conference on knowledge engineering and Ontology Development, KEOD 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Hussain, Huda de Vries, Marne Department of Industrial and Systems Engineering University of Pretoria Pretoria South Africa

ISBN: (纸本)9789897586712

Literature indicates that systems dynamics (SD) has the potential of modelling the behaviour of a system to understand enterprise behaviour and the effect of enterprise policies to address multiple performance areas. Since SD concepts are ill-defined, a meta model for enterprise systems dynamics (MMESD) was developed, using the general ontology specification language (GOSL). the first version of the MMESD was applied to an existing case within the car industry, where the case was modelled with the software named Vensim. the MMESD was developed without considering meta model implementations within multiple SD software tools. this article investigates the use of SD concepts in different SD software tools, highlighting the differences in the use of symbolic formalisms. the main contribution of the paper is extracting new concepts when we compare existing software tools, identifying concepts that are not already reflected in the first version of MMESD. We use the results to further extend the first version of MMESD, and apply an extended second version of MMESD to an existing teacher education faculties case in Croatia as a demonstration. the paper concludes with suggestions for future research. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

关键词： Ontology

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Unveiling Breast Cancer Causes through knowledge Graph Analysis and BioBERT-Based Factuality Prediction

Unveiling Breast Cancer Causes Through Knowledge Graph Analy...

引用

18th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： El Haji, Hasna Sbihi, Nada El Handri, Kaoutar Bahaj, Adil Elkasri, Mohammed Souadka, Amine Ghogho, Mounir TiCLab International University of Rabat Rabat Morocco University Mohammed V Rabat Morocco Aivancity School of AI & Data for Business & Society Cachan France Surgical Oncology Department National Institute of Oncology University Mohammed V Rabat Morocco

Worldwide, millions of women are affected by breast cancer, with the impact significantly worsened in underserved regions. the profound effect of breast cancer on women’s health has driven research into its causes, with the aim of developing methods for the prevention, diagnosis, and treatment of the disease. the significant influx of research on this subject is overwhelming and makes manual exploration arduous, which motivates automated knowledge exploration approaches. knowledge Graphs (KGs) are one of these approaches that attracted significant attention in the last few years for their ability to structure and present knowledge, making it easier to explore and analyze. Current KGs that include causes of breast cancer are deficient in contextual information, highlighting the uncertainty of these causes (facts). In this work, we present a method for extracting a sub-graph of breast cancer causes and fine-tuning BioBERT to evaluate the uncertainty of these causes. Our automated approach, which simulates human annotation, computes uncertainty scores based on textual factuality and assesses cause reliability using a Closeness Score. We also create a web-based application for easy explorationa. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： knowledge graph

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Synergistic Effects of Ozone Reaction Products and Fine Particulate Matter on Pulmonary Physiology in Children with Asthma 18

Synergistic Effects of Ozone Reaction Products and Fine Part...

引用

18th international conference on Indoor Air Quality and Climate, INDOOR AIR 2024

作者： He, Linchen Weschler, Charles J. Morrison, Glenn Black, Marilyn Bergin, Michael H. Zhang, Junfeng Department of Community and Population Health College of Health Lehigh University BethlehemPA United States Environmental and Occupational Health Sciences Institute Rutgers University PiscatawayNJ United States International Centre for Indoor Environment and Energy Technical University of Denmark Lyngby Denmark Department of Environmental Science and Engineering Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel HillNC United States Underwriters Laboratories Inc MariettaGA United States Department of Civil and Environmental Engineering Duke University DurhamNC United States Nicholas School of the Environment Duke University DurhamNC United States

ISBN: (纸本)9798331306816

Exposure to ozone (O3) reaction products has been linked to adverse pulmonary health effects. these reaction products may be carried by fine particulate matter (PM2.5) that can deposit in the deep lung. In a panel of 43 asthmatic children, we measured indoor and outdoor O3 concentrations and used the difference, termed "O3 loss", as a proxy of exposure to O3 reaction products. Biomarkers of pulmonary physiology were assessed 4 times for each participant over 8 weeks. Our findings revealed significant and adverse associations between O3 loss and these biomarkers, but only when PM2.5 levels were above the mean value measured during the study. Similarly, PM2.5 exposure had stronger adverse effects on some biomarkers when O3 loss levels were above the mean value. these results support the hypothesis of synergistic effects between O3 reaction products and PM2.5 on pulmonary health. © 2024 18th conference of the international Society of Indoor Air Quality and Climate, INDOOR AIR 2024 - conference Program and Proceedings. All rights reserved.

关键词： Indoor air pollution

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2024 14th international conference on Computer and knowledge engineering, ICCKE 2024

2024 14th International Conference on Computer and Knowledge...

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14th international conference on Computer and knowledge engineering, ICCKE 2024

ISBN: (纸本)9798331511272

the proceedings contain 79 papers. the topics discussed include: an interactive approach for query-based multi-document scientific text summarization;enhancing Persian word sense disambiguation with large language models techniques and applications;assessing users' influence on respondents in conversation quality: a quantitative study on reddit based on the cooperative principle;non-negative matrix factorization improves residual neural networks;cluster sampling: a cluster-driven sampling strategy for deep metric learning;a scalable blockchain-based educational network for data storage and assessment;towards efficient capsule networks through approximate squash function and layer-wise quantization;automated software design using machine learning with natural language processing;evaluation of efficient electrocardiomatrix-based identification using deep learning methods;disturbance rejection in quadruple-tank system by proposing new method in reinforcement learning;and an improved and accurate measure for mining correlated high-utility itemsets.

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Achieving Last-Mile Functional Coverage in Testing Chip Design software Implementations 23

Achieving Last-Mile Functional Coverage in Testing Chip Desi...

引用

IEEE/ACM 45th international conference on software engineering - software engineering in Practice (ICSE-SEIP)

作者： Yan, Ming Chen, Junjie Mao, Hangyu Jiang, Jiajun Hao, Jianye Li, Xingjian Tian, Zhao Chen, Zhichao Li, Dong Xian, Zhangkong Guo, Yanwei Liu, Wulong Wang, Bin Sun, Yuefeng Cui, Yongshun Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China Huawei Hisilicon Beijing Peoples R China

ISBN: (纸本)9798350300376

Defective chips may cause huge losses (even disasters), and thus ensuring the reliability of chips is fundamentally important. To ensure the functional correctness of chips, adequate testing is essential on the chip design implementation (CDI), which is the software implementation of the chip under design in hardware description languages, before putting on fabrication. Over the years, while some techniques targeting CDI functional testing have been proposed, there are still a number of hard-to-cover functionality points due to huge input space and complex constraints among variables in a test input. We call the coverage of these points last-mile functional coverage. Here, we propose the first technique targeting the significant challenge of improving last-mile functional coverage in CDI functional testing, called LMT, which does not rely on domain knowledge and CDI internal information. LMT first identifies the relevant variables in test inputs to the coverage of last-mile functionality points inspired by the idea of feature selection in machine learning, so as to largely reduce the search space. It then incorporates Generative Adversarial Network (GAN) to learn to generate valid test inputs (that satisfy complex constraints among variables) with a larger possibility. We conducted a practical study on two industrial CDIs in Huawei to evaluate LMT. the results show that LMT achieves at least 49.27% and 75.09% higher last-mile functional coverage than the state-of-the-art CDI test input generation techniques under the same number of test inputs, and saves at least 94.24% and 84.45% testing time to achieve the same functional coverage.

关键词： Chip Design Testing Test Generation Functional Coverage Machine Learning

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Low-cost irrigation system based on multi-tenant and WSN technologies(SCA) 36

Low-cost irrigation system based on multi-tenant and WSN tec...

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36th international conference on software engineering and knowledge engineering, SEKE 2024

作者： Ma, Jinwei Li, Meian Chao, Lumeng Computer and Information Engineering College Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Agriculture University Hohhot China

ISBN: (纸本)1891706594

the increase in irrigated crop areas lead to the requirements for freshwater will in fact rise significantly. Considering this kind of need, this study aims to develop a low cost irrigation system that provide the monitoring and control of irrigated agriculture using Cloud Computing, Internet of things(IoT) to improve the efficiency of water use in irrigated agriculture and reduce the cost of the solution. In this paper, an irrigation system driven by Internet of things and cloud based web application is developed. the web application uses a multi-tenant web framework (MWF) that can provide cost-effective services for scenarios where a large number of small tenants, only some of which are active at the same time. the system uses wireless sensor network(WSN) communication technology to integrate various sensors, solenoid valves and edge devices scattered throughout the crop area for monitoring and control. the solution significantly improves water use efficiency while reducing costs, making it attractive to users. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Solenoid valves

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MMGCRN: Multimodal and Multiview Graph Convolutional Recurrent Network for Traffic Prediction 36

MMGCRN: Multimodal and Multiview Graph Convolutional Recurre...

引用

36th international conference on software engineering and knowledge engineering, SEKE 2024

作者： Meng, Xiangfu Xie, Weipeng Cui, Jiangyan School of Electronic and Information Engineering Liaoning Technical University Huludao125105 China

ISBN: (纸本)1891706594

Traffic prediction is essential for intelligent transportation systems and smart city applications, yet existing spatio-temporal models face limitations. these include inadequate spatial feature extraction, neglect of spatial heterogeneity, and omission of factors like traffic speed and travel time. To address the above challenges, we propose a multimodal and multiview traffic flow prediction method called MMGCRN, which adaptively learns the spatio-temporal correlation features of multimodal traffic data. In MMGCRN, we design a spatial heterogeneous perception cross-attention module to model the spatial heterogeneous relationships of nodes. In addition, we use spatio-temporal embeddings to generate the dynamic feature correlation graph of nodes across time steps, which is combined with the topological graph and semantic graph and fed into the Multiview Graph Convolutional Recurrent Network (MGCRN) to extract the multi-dimensional correlations between nodes. We learn and fuse different modal traffic data through multiple MGCRN modules. Finally, experiments on two real-world datasets show that in 60-minute-ahead long-term forecasting, the MMGCRN model achieves a minimum improvement of 0.96% and a maximum improvement of 8.69% over baseline models. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Travel time

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An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry 23

An Empirical Study of Pre-Trained Model Reuse in the Hugging...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Jiang, Wenxin Synovic, Nicholas Hyatt, Matt Schorlemmer, Taylor R. Sethi, Rohan Lu, Yung-Hsiang thiruvathukal, George K. Davis, James C. Purdue Univ W Lafayette IN 47907 USA Loyola Univ Chicago Chicago IL USA

ISBN: (纸本)9781665457019

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.