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7th International Symposium on Advances in Electrical, Electronics, and Computer engineering

作者： Jinchen, Xu Yuanyuan, Xia Jiangwei, Hao Hao, Cao State Key Laboratory of Mathematical Engineering and Advanced Computing No. 62 Science Avenue High-Tech Zone Henan Zhengzhou450001 China National Key Laboratory of Science and Technology on Integrated Information Systems Technology Institute of Software Chinese Academy of Sciences No. 4 Zhongguancun South Fourth Street Haidian District Beijing10190 China

ISBN: (纸本)9781510656437

Error analysis is an important support for improving the precision of floating-point calculations. However, the floating-point error distribution does not have dominant characteristics, making it difficult to optimize floating-point calculation precision. This paper proposes the LFEE (Locating hot spots of floating-point expression errors) algorithm. The algorithm analyzes the error detection result of the expression to obtain the error hot spot, i.e., the step with the maximum error in the expression calculation sequence, followed by preforming precision optimization scheme for the error hot spot. The experimental results show that the LFEE algorithm can accurately locate the error hot spots and improve the accuracy of the function by optimizing the error hot spots. © 2022 SPIE. All rights reserved.

关键词： Error detection

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A Model-Based Approach to Automotive Feature Development for Updates and Upgrades

A Model-Based Approach to Automotive Feature Development for...

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2023 IEEE International Conference on Service-Oriented System engineering (SOSE)

作者： Marc Schindewolf Jan Willem Wittler Thomas Kühn Daniel Grimm Eric Sax Institut für Technik der Informationsverarbeitung Karlsruhe Institute of Technology Karlsruhe Germany Dependability of Software-intensive Systems Karlsruhe Institute of Technology Karlsruhe Germany Software Engineering and Programming Languages Martin Luther University Halle-Wittenberg Halle-Wittenberg Germany

The automotive industry faces challenges due to the increasing demand for customization and individualization by consumers. With a wide range of equipment levels and feature options, it has become impossible to test all possible configurations for a single vehicle during development and especially for vehicles already in service. As a basis for solving these challenges, integrated approaches for the development of upgradeable vehicles are required. In this context, future upgrade options should be considered and planned already in the design stage to enable sustainable business models for next vehicle generations. This paper proposes a refined variability model that combines two existing approaches to address the challenges faced by the industry. The same model can perform buildability or upgradeability checks and dynamic allocation optimization. The model is evaluated qualitatively by three exemplary use cases.

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Identification of Critical Test Scenario Groups for Aircraft Functions Based on Operational Design Domains

Identification of Critical Test Scenario Groups for Aircraft...

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AIAA Science and technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Krause, S. Reglitz, S. Hillig, D. Thielecke, F. Franke, M. Automated Driving & Software Solutions dSPACE GmbH NRW Paderborn33102 Germany Real-Time Test & Development Solutions dSPACE GmbH NRW Paderborn33102 Germany Institute of Aircraft Systems Engineering Hamburg University of Technology Hamburg21129 Germany Division of ICT Applications for Productio Bremer Institut für Produktion und Logistik GmbH Bremen28359 Germany

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The development of aircraft systems requires robust verification and validation processes to ensure safety, reliability, and functionality. The rapid advancements in system automation have introduced a level of complexity where traditional testing methods reach their limits. Such methods often encounter challenges in identifying critical test scenarios early in the development cycle, which can cause delays. This delay can lead to significant cost implications due to late error detection and subsequent redevelopments. The primary objective of this research is to develop a method that enhances the early identification of critical test scenarios, thereby improving the efficiency and effectiveness of the testing process by using the concept of operational design domains (ODD) as a framework. By employing a hierarchical ODD approach, this study creates smaller, priority test groups that can be continuously used and shared among stakeholders throughout the testing process. As a further step, a stochastic test exploration method is used to generate a variety of scenario-based test cases, including critical test cases. These test cases, written in TA-SCXML, are analyzed using ODD-based observers within a virtual integration environment. To identify test cases to be shared between stakeholders, a grouping concept is proposed. The groups contain critical test cases that verify the behavior as described by the ODD, as well as test cases that explore behavior outside of the ODD. The method is applied on a flight control use case involving a virtually integrated software prototype for investigating new flight automation concepts for an UAV. The proposed method successfully identifies critical scenario-based test cases, which are then grouped and provided to suppliers and other stakeholders. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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software and data provenance as a basis for eScience workflow 17

Software and data provenance as a basis for eScience workflo...

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17th IEEE International Conference on eScience, eScience 2021

作者： Conquest, Joseph Stiber, Michael University of Washington Bothell Computing and Software Systems Division School of Science Technology Engineering and Mathematics BothellWA98011 United States

ISBN: (纸本)9781665403610

While tracking data and software provenance is necessary in eScience, and often implemented in scientific workflow management tools, such tools generally don't provide graphical UIs that use provenance to guide workflow. Graphitti Workbench was built to center workflow around its PROV-DM based ProVis software and data provenance visualizer, making provenance more readily accessible to scientists. Using ProVis as the system desktop highlights the importance of provenance to inform future experiments and improves provenance understandability. Our usability study indicates that this solution is highly acceptable and that it provides easy deployment of experiments using provenance-based creation. © 2021 IEEE.

关键词： e-Science

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Report-Guided Cross-Modal Representation Learning for Predicting EGFR Mutations by Whole Slide Image

Report-Guided Cross-Modal Representation Learning for Predic...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qiao, Qi Shi, Jun Jiang, Zhiguo Wang, Wei Wu, Haibo Zheng, Yushan Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Hefei University of Technology School of Software Hefei230601 China Beihang University Image Processing Center School of Astronautics Beijing102206 China University of Science and Technology of China Division of Life Sciences and Medicine Department of Pathology First Affiliated Hospital of Ustc Hefei230036 China University of Science and Technology of China Intelligent Pathology Institute Division of Life Sciences and Medicine Hefei230036 China Beihang University School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beijing100191 China

ISBN: (纸本)9798350386226

Traditional PCR/NGS-based multigene panel testing is time-consuming and costly. Predicting EGFR mutations directly from H&E stained whole slide images (WSIs) can alleviate these limitations. Furthermore, histopathological reports contain valuable textual information that correlates with tissue areas in WSIs. However, recent research mainly analyses EGFR mutation status only from a single modality, ignoring rich information contained in reports. In this paper, we propose a report-guided cross-modal representation learning method for predicting EGFR mutations by WSIs. Specifically, we reconstruct report-level embeddings through exploring intrinsic relationships between diagnostic words in histopathological reports and tissue areas in WSIs. Finally, reconstructed histopathological report embedding and aggregated WSI embedding are fused for final prediction. More importantly, molecular testing report is also introduced as prior supervision information at the training stage to guarantee semantic consistency of fused feature and molecular report embedding. We evaluate our method on the TCGA-EGFR public benchmark dataset and an in-house clinical dataset (USTC-EGFR). Experimental results demonstrate that our method outperforms existing approaches in EGFR mutation prediction, highlighting the benefits of cross-modal learning in enhancing feature representational ability. The code is available at https://***/HFUT-miaLab/RCRL. © 2024 IEEE.

关键词： Supervised learning

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${\sf Img2Acoustic}$Img2Acoustic: A Cross-Modal Gesture Recognition Method Based on Few-Shot Learning

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IEEE Transactions on Mobile Computing 2024年第3期24卷 1496-1512页

作者： Yongpan Zou Jianhao Weng Wenting Kuang Yang Jiao Victor C. M. Leung Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Information Hub Hong Kong University of Science and Technology Guangzhou China

Acoustic-based human gesture recognition (HGR) offers diverse applications due to the ubiquity of sensors and touch-free interaction. However, existing machine learning approaches require substantial training data, making the process time-consuming, costly, and labor-intensive. Recent studies have explored cross-modal methods to reduce the need for large training datasets in behavior recognition, but they typically rely on open-source datasets that closely align with the target domain, limiting flexibility and complicating data collection. In this paper, we propose ${\sf Img2Acoustic}$ , a novel cross-modal acoustic-based HGR approach that leverages models trained on open-source image datasets (i.e., EMNIST, Omniglot) to effectively recognize custom gestures detected via acoustic signals. Our model incorporates a task-aware attention layer (TAAL) and a task-aware local matching layer (TALML), enabling seamless transfer of knowledge from image datasets to acoustic gesture recognition. We implement ${\sf Img2Acoustic}$ on commercial devices and conduct comprehensive evaluations, demonstrating that our method not only delivers superior accuracy and robustness compared to existing approaches but also eliminates the need for extensive training data collection.

关键词： Training Gesture recognition Acoustics Data models Sensors Feature extraction Target recognition Mobile computing Image recognition Few shot learning

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A Novel Algorithm for Vertex Sum Distinguishing Total Coloring Based on Multiobjective Optimization

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Journal of engineering Science and technology Review 2021年第6期14卷 107-115页

作者： Huanping, Zhao Dangqin, Xue Huojie, Shi School of Computer and Software Nanyang Institute of Technology Nanyang473004 China School of Intelligent Manufactuing Nanyang Institute of Technology Nanyang473004 China Department of Biological Systems Engineering Washington State University PullmanMI98101 United States

Graph coloring is an important research field in graph theory. In vertex sum distinguishing total coloring, the constraint condition, that is, the color sum of each vertex is different from that of its associated edges is added on the basis of the normal total coloring. A novel algorithm was designed to increase the vertex sum distinguishing total coloring effciency. Subobjective functions were designed according to the vertex constraint, edge constraint, vertex-edge constraint, and vertex-edge sum constraint to improve the vertex sum distinguishing total coloring efficiency of random graphs. In each subobjective function, iterative swap was performed step by step using the coloring matrix and color set complement, in which the conflict was handled following the idea of impressed variation, thereby realizing the vertex sum distinguishing total coloring of random graphs. In the end, the lower bound of chromatic number in the vertex sum distinguishing total coloring was proven through the theoretical analysis and experimental comparison. Results demonstrate that when the order of a graph is smaller than 1000 and the edge density is smaller than 0.1, the conflict handling function can be converged at a high rate. When the order of the graph is greater than 1000, the convergence rate of the algorithm declines rapidly, and the accurate coloring result can be obtained only by increasing the coloring quantity, indicating that the vertex sum distinguishing total chromatic number of a connected graph with the order of not smaller than 3 ranged from μΣt(G) to μΣt(G)+3. This finding is consistent with the algorithm analysis result. On this basis, this algorithm can effectively complete the vertex sum distinguishing total coloring of random graphs, and its time complexity does not exceed O(n3). The proposed algorithm provides evidence for exploring complex networks, transportation, and network security © 2021 School of Science, IHU. All rights reserved

关键词： Multiobjective optimization

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AI-Infused Telemedicine for Rural Wellness: A Comprehensive Approach

AI-Infused Telemedicine for Rural Wellness: A Comprehensive ...

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Innovative Trends in Information technology (ICITIIT), International Conference on

作者： Nidhi B Kurpad S Dhanyashree V Shivank T A S Surya Shobith S Vinila Jinny Computer Science and Engineering Vellore Institute of Technology Vellore India Electronics and Communication Engineering S J B Institute of Technology Bengaluru India Electrical and Electronics Engineering B N M Institute of Technology Bengaluru India Department of Software Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

The paper introduces a transformative Telemedicine Kiosk designed to enhance healthcare in remote areas by harnessing the synergy of computational intelligence and telemedicine. This advanced kiosk utilizes real-time video conferencing and comprehensive patient monitoring systems, all underpinned by Responsible AI practices that ensure ethical data management and the protection of patient privacy. Equipped with an ESP32 Wi-Fi module, the kiosk provides seamless communication between patients and medical officers, facilitating immediate care and consultation. The system goes beyond diagnostics: it supports healthcare professionals with an integrated medication dispensing mechanism powered by IoT technology. Our investigation delves into the implications of human factors, economics, and technological advances for the future of telemedicine. We present an innovative solution that not only advances healthcare accessibility but also embodies a commitment to responsible and ethical healthcare service provision in underserved regions.

关键词： Ethics Data privacy Patient monitoring Telemedicine Medical services Real-time systems Protection

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Difference-Adaptive Knowledge Distillation for Resource-Constrained Embedded Devices

Difference-Adaptive Knowledge Distillation for Resource-Cons...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yi Xiong Wenjie Zhai Zongwei Zhu School of Software Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China

Deep Neural Network (DNN) models have brought significant performance gains to machine learning tasks. Nevertheless, the huge storage and computational costs of high-performance models severely limit their deployment on resource-limited embedded devices. Knowledge distillation (KD) is the mainstream approach for models compression, but the existing KD framework has some limitations. First, the student model underperforms when there is a considerable capacity gap between the student and teacher models, which reduces the model compression rate in knowledge distillation. Second, compared with training from scratch, the billions of additional forward propagation of the complex teacher model is time-consuming, resulting longer train time in the distillation process. This paper proposes AKD, an efficient difference-adaptive knowledge distillation framework. AKD consists of two modules. The distillation module uses a modified auxiliary teaching architecture as the backbone, and the adaptive module introduces a difference-adaptive method, which can adjust the teacher model adaptively according to the continuously improving representation ability of the student model. Experimental results on different kinds of datasets and models indicate that AKD not only improves the efficiency of distillation, but also enhances the performance of the compact student model and raise the compression rate.

关键词： Training Performance evaluation Deep learning Adaptation models Smart cities Computational modeling Neural networks

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Healthcare Professionals’ Perceptions Towards Ai Integration in Jordan's Health Sector

SSRN

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SSRN 2024年

作者： Klaib, Ahmad F. Alhosanie, Tasneem N. Mahdin, Ladees W. Altatar, Enas J. Software Engineering Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Information Systems Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

PurposeThe impact of AI on healthcare is widely recognized there remains a scarcity of studies examining how doctors perceive and approach its use in medicine. This study aims to gather insights from healthcare providers in Jordan concerning the advantages of integrating AI into practices, their perspectives on AI applications in healthcare, and their views on the future role of AI in replacing key tasks within health *** survey was conducted among healthcare professionals working at facilities in Jordan. An online questionnaire was used to collect data on demographics, attitudes toward using AI for tasks, and opinions on the benefits of AI adoption. Categorical variables were presented as counts and percentages, while the continuous variables were interpreted as mean and standard deviation. The associations between the determinants and the outcomes were done using one-way ANOVA. Any test with a P-value 0.05 was considered *** total of 612 healthcare professionals participated in the survey with females comprising a majority of respondents (52.8%). The majority of respondents showed optimism about AI’s potential to improve and revolutionize the field, although there were concerns about AI replacing human roles. Generally, physical therapists, medical researchers, and pharmacists displayed openness to incorporating AI into their work routines. Younger individuals aged between 18 and 40 seemed accepting of AI in the domain. A significant portion of participants believed that AI could negatively impact job opportunities and reduce the time needed for diagnosing conditions, but did not find any correlation, between responses and *** conclude, the results of this study suggest that healthcare professionals, in Jordan, hold receptive views on incorporating artificial intelligence in the medical field similar to their counterparts in developed nations. However, there is a concern about the implications of AI, on job stability a

关键词： Artificial intelligence

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