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检索条件"任意字段=19th International Conference on Software Engineering and Knowledge Engineering"

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BiKD-Yolo: A Small Object Detection Algorithm from UAV Perspective Based on BiFormer Attention and knowledge Distillation 20

BiKD-Yolo: A Small Object Detection Algorithm from UAV Persp...

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20th international conference on Natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2024

作者： Hua, Xiang Cai, Zeju Zhu, Xiangyuan Aijia, Ouyang School of Software Engineering Sun Yat-sen University Guangdong China School of Computer Science and Software Zhaoqing University Guangdong China School of Information Engineering Zunyi Normal University Guizhou China

ISBN: (纸本)9798350356328

Object detection is crucial for the real-time operations of Unmanned Aerial Vehicles (UAVs), particularly in identifying small objects within UAV imagery. While existing computer vision techniques have shown success in UAV applications, detecting small objects remains challenging due to limited information, small size, and low resolution. this study presents the BiKD-YOLO approach, which combines BiFormer attention and knowledge distillation to enhance both the precision and efficiency of small object detection. We employ SAHI (Slice-Assisted Hyper-Inference) cropping to enlarge small objects in images, followed by knowledge distillation for model compression, facilitating knowledge transfer from larger to smaller models for streamlined operation. through the incorporation of the BiFormer attention mechanism, the detection process is further refined by focusing on critical regions of interest, thereby improving overall accuracy. Experimental Results from tests on the VisDrone dataset show that BiKD-Yolo greatly surpasses the baseline, delivering superior performance in detection accuracy, mAP, and AP metrics. In summary, the BiKD-Yolo method presents a novel and effective solution for improving small object detection in UAV images, offering enhanced accuracy and speed. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Leveraging Transfer Learning for Robust Medical Image Segmentation 4

Leveraging Transfer Learning for Robust Medical Image Segmen...

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4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tyagi, Priya Sharma, Avadhesh Kumar Masson, Palak Manwal, Manika Goel, Pawan Kumar Varshney, Neeraj Nitra Technical Campus Department of CSE-AIML UP Ghaziabad India Gl Bajaj Institute of Technology and Management Electronics and Communication Engineering Uttar Pradesh Greater Noida201306 India Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University R/S Graphic Era Deemed to Be University Uttarakhand Dehradun India Gla University Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350364729

Transfer mastering is a powerful approach that has been applied to various tasks in many fields, including pc imaginative and prescient and herbal language processing. this paper describes the software of Switch gaining knowledge of medical photo segmentation, a complex project of appropriately isolating character objects from a scientific picture. this approach involves taking the parameters learned from a pre-educated deep mastering community and adapting them to a new scientific picture segmentation undertaking. the proposed method is tested on three unique datasets offering an expansion of abdominal, brain, and cardiac images. Outcomes display that the proposed technique produces higher segmentation outcomes than conventional deep getting-to-know fashions educated from scratch and might extensively reduce the time required to train and optimize those fashions. Furthermore, the proposed method can even outperform end-to-stop networks in some instances. therefore, the proposed switch gaining knowledge of the method has the ability for used for strong medical picture segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Life Cycle Assessment Tools for Aviation 19

Comparative Analysis of Life Cycle Assessment Tools for Avia...

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19th international Scientific conference on New Trends in Aviation Development, NTAD 2024

作者： Koščáková, Martina Korba, Peter Koščák, Peter Szokeová, Lubomira Klamo, Peter Department of Air Transport Management Faculty of Aeronautics Technical University of Košice Košice Slovakia Department of Aviation Engineering Faculty of Aeronautics Technical University of Košice Košice Slovakia

ISBN: (纸本)9798331527747

this paper presents a comprehensive analysis of various Life Cycle Assessment (LCA) tools for the use in the aviation industry. Given the significant environmental impacts associated with aviation, from the manufacture of aircraft to their operational lifecycle, it is crucial to leverage precise and robust LCA tools to guide sustainability initiatives. this study compares several prominent LCA tools including SimaPro, Shpera, Ecochain Mobius, Ecochain Helix, Umberto, OpenLCA, and GREET, based on criteria essential for effective environmental assessment. these criteria encompass the range of impact assessment methods, usability, the capability for generating detailed reports, sector-specific features, collaborative functionalities, and database comprehensiveness. the findings provide critical insights into each tool's strengths and limitations. © 2024 IEEE.

关键词： aviation environmental impact LCA tools Life Cycle Assessment sustainability

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Will you use software development support using biosignals? A survey from software developers 35

Will you use software development support using biosignals? ...

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35th international conference on software engineering and knowledge engineering, SEKE 2023

作者： Soga, Ryo Kanuka, Hideyuki Kubo, Takatomi Ishio, Takashi Matsumoto, Kenichi Research & Development Group Hitachi Ltd Japan Graduate School of Science and Technology Nara Institute of Science and Technology Japan Department of Media Architecture Future University Hakodate Japan

Biosignals reflect the mental states of software developers and could improve support technologies for software development activities. Although several technologies for software development support using biosignals (BioSDS) have been proposed, BioSDS has not yet been deployed in actual software development workplaces. As a prerequisite for industrial deployment, BioSDS must be well understood and accepted by software developers. However, the current level of their acceptance has not been comprehensively assessed. In this study, we conducted a survey to clarify the current level of acceptance of BioSDS and potential attributes that influence the level of acceptance. We defined eleven use-cases based on six previous primary studies related to BioSDS, and then asked developers at Hitachi, a Japanese IT company in the FORTUNE 500, about the level of acceptance of each use-case. Our analysis of eighty-six responses revealed that four out of eleven use-cases had some level of acceptance by software developers. In addition, we found four attributes that affect the level of acceptance: subject to be measured, objectives, interventions, and timing. these findings help to identify barriers to the adoption of BioSDS in the workplace. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： software design

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Where is it? Tracing the Vulnerability-Relevant Files from Vulnerability Reports 24

Where is it? Tracing the Vulnerability-Relevant Files from V...

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44th ACM/IEEE international conference on software engineering, ICSE 2024

作者： Sun, Jiamou Chen, Jieshan Xing, Zhenchang Lu, Qinghua Xu, Xiwei Zhu, Liming Csiro SydneyNSW Australia Csiro & Australian National University CanberraACT Australia

ISBN: (纸本)9798400702174

With the widely usage of open-source software, supply-chain-based vulnerability attacks, including SolarWind and Log4Shell, have posed significant risks to software security. Currently, people rely on vulnerability advisory databases or commercial software bill of materials (SBOM) to defend against potential risks. Unfortunately, these datasets do not provide finer-grained file-level vulnerability information, compromising their effectiveness. Previous works have not adequately addressed this issue, and mainstream vulnerability detection methods have their drawbacks that hinder resolving this gap. Driven by the real needs, we propose a framework that can trace the vulnerability-relevant file for each disclosed vulnera-bility. Our approach uses NVD descriptions with metadata as the inputs, and employs a series of strategies with a LLM model, search engine, heuristic-based text matching method and a deep learning classifier to recommend the most likely vulnerability-relevant file, effectively enhancing the completeness of existing NVD data. Our experiments confirm that the efficiency of the proposed framework, with CodeBERT achieving 0.92 AUC and 0.85 MAP, and our user study proves our approach can help with vulnerability-relevant file detection effectively. To the best of our knowledge, our work is the first one focusing on tracing vulnerability-relevant files, laying the groundwork of building finer-grained vulnerability-aware software bill of materials. © 2024 ACM.

关键词： Open source software

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Analysis of Cooling Loads and their Effects on Energy Costs for an Integrated Insulating Materials-Based Direct Iron Processing Plant 19

Analysis of Cooling Loads and their Effects on Energy Costs ...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Anthony, Adeyanju A. Mechanical and Manufacturing Engineering Department The University of the West Indies St. Augustine Campus Trinidad and Tobago

this study discusses the surge in energy consumption in the Caribbean region over the past decade, notably in Trinidad, where per capita consumption exceeds 6500 kWh. In response to rising electricity tariffs, energy sector entities are implementing conservation initiatives. the study focuses on a Direct Reduced Iron (DRI) plant in Trinidad's Point Lisas Industrial Estate, specifically examining alternatives to conventional air conditioning in a DRI processing laboratory. Various insulating materials were simulated using CHVAC software and evaluated against a 5-ton air-conditioning unit using the CLTD method. Analysis reveals standalone use of insulating systems in the laboratory is impractical due to orientation, location, and internal heat loads, necessitating a hybrid cooling system. Economically viable configurations involve a 3-ton A/C unit paired with PVC, foam, or fibreglass walls and ceilings. Despite higher initial costs, configurations with a 3-ton unit offer savings in maintenance and electricity. Findings extend beyond the laboratory, potentially influencing passive cooling material adoption and active cooling load reduction in other contexts, thus promoting sustainable energy practices. © 2024 the Authors. Published by Elsevier B.V.

关键词： Air conditioning

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Multimodal Sentiment Analysis under modality deficiency with prototype-Augmentation in software engineering 30

Multimodal Sentiment Analysis under modality deficiency with...

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30th IEEE international conference on software Analysis, Evolution and Reengineering (SANER)

作者： Wang, Baolei Zhang, Xuan Du, Kunpeng Yunnan Univ Sch Software Kunming Peoples R China Yunnan Univ Sch Software Yunnan Key Lab Software Engn Kunming Peoples R China

ISBN: (纸本)9781665452786

Sentiment analysis has a wide range of promising applications in software engineering, and the development of deep learning has demonstrated that the uniform representation of different modalities can improve the model performance of sentiment analysis. However, in practical applications, multimodal sentiment analysis always faces unsatisfactory situations, especially when the modality has missing samples, most models may fail. For example, social dynamics of technicians in developer communities can face modality unavailability due to privacy settings. Several existing works based on deep learning and regularization methods have explored the modal missing problem, but these works cannot balance the cases of modal general missing (rate < 50%) and severe missing (rate 50%), and do not consider the resource consumption during model inference. therefore, in this paper, we proposed a prototype augmented multimodal teacher-student network (PAMD) to address the above issues. Specifically, a multi-level and multi-origin distillation strategy is used to minimize the required resources and inference time, and prototype augmentation is used to guarantee the performance of the model when a modality is severely missing. Extensive experiments are conducted on different benchmark datasets to explore a network that balances performance and resource consumption. And It achieves good results in different modalities of missing cases.

关键词： Multimodal Sentiment Analysis software engineering Prototype augmented Distillation Severe Missing

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Multi-Task Regression with Process knowledge-Based Forest Learners in Process Industries 19

Multi-Task Regression with Process Knowledge-Based Forest Le...

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19th IEEE international conference on Automation Science and engineering, CASE 2023

作者： Sun, Linjin Ji, Yangjian Peng, Tao School of Mechanical Engineering Zhejiang University The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang Key Laboratory of Advanced Manufacturing Technology Hangzhou310058 China

ISBN: (纸本)9798350320695

the multi-task regression is of great significance for engineers in process control. It can not only help achieve more than one target variable estimation at once, but more importantly, it offers an opportunity for target variables to share knowledge. However, due to the complexity and nonlinearity of the production process that contains interconnected variables in process industries, it is challenging for conventional regression methods to obtain accurate results. To fill the gaps, more black-box artificial intelligence models are proposed and applied to multi-task regression, while the process knowledge is often ignored during modeling. In this paper, a solution for adopting process knowledge-based learners for multi-task regression is proposed. Specifically, a two-stage learning structure with multiplicative integration weighted-random forest is offered. the initial estimated values of target variables are achieved from the separated base learners in the first stage, and then an integrated model is learned based on the previous predictions. In this manner, the knowledge of target variables and process knowledge among process data can ultimately be learned after training. the proposed method is finally applied to a VRM system in a case study, and the results verify its superiority over baseline methods. © 2023 IEEE.

关键词： Regression analysis

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Next-Generation Metric for software Complexity Analysis 12

Next-Generation Metric for Software Complexity Analysis

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12th international conference in software engineering Research and Innovation, CONISOFT 2024

作者： thathsarani, H.A.N. Hatch Works Building iVedha Inc. Colombo Sri Lanka

ISBN: (纸本)9798331532116

this research introduces the Holistic Cognitive Complexity (HCC) metric, an innovative object-oriented measurement method evolved from the traditional CB metric to offer a more comprehensive assessment of software complexity. By integrating additional complexity factors such as array usage and thread management, the HCC metric addresses both structural and cognitive complexities, providing a holistic view of modern software development. the practical utility of the HCC metric is demonstrated through a comparative analysis with the traditional CB metric, using a sample program to highlight the added intricacies captured by the HCC metric. the detailed complexity assessment facilitated by the HCC metric enables developers to identify potential problem areas early in the development process, guiding targeted refactoring and optimization efforts. this proactive identification of complexity hotspots is crucial for maintaining high standards of software quality and ensuring maintainability and scalability over time. Additionally, the HCC metric aids in better resource allocation and project planning by providing a more accurate measure of complexity, thus supporting effective project estimation and resource distribution. the significant contributions of the HCC metric include its ability to quantify the complexities introduced by advanced programming constructs like arrays and threads, offering deeper insights into software complexity than traditional metrics. this research underscores the importance of continuous enhancement of complexity metrics to address emerging challenges in software development. the HCC metric's robust framework and detailed assessment promise to elevate software quality, reliability, and maintainability, making it a valuable tool for software developers, analysts, and project managers. © 2024 IEEE.

关键词： software quality

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BAMGAN: A KBQA method based GAN and bi-directional attention 6

BAMGAN: A KBQA method based GAN and bi-directional attention

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6th international conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Guo, Jiazhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650504 China

ISBN: (纸本)9781510668249

knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. this method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Generative adversarial networks

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