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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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DILATANCY OF NON-CEMENTED POORLY GRADED SAND DURING SHEAR 19th

DILATANCY OF NON-CEMENTED POORLY GRADED SAND DURING SHEAR

引用

19th international Middle Eastern Simulation and Modelling conference, MESM 2024

作者： Al-Taie, Abbas J. Ahmed, Mahmood D. Department of Civil Engineering Al-Nahrain University Baghdad10070 Iraq Department of Civil Engineering University of Baghdad Baghdad10070 Iraq

ISBN: (纸本)9789492859341

the shear strength of the non-cemented soils is usually presented in terms of internal friction. the utilization of the angle of internal friction and the dilation angle is another alternative relevant to the frictional behavior of these soils, which in turn, is strictly linked with the volumetric strain. In the development of modeling to analyze and design different geotechnical facilities, like earth retaining structures, the selection of appropriate models to evaluate the soil behavior is crucial. More complicated models require more input parameters to identify the selected model. therefore, models with fewer parameters, like Mohr—Coulomb, may approximate the soil behavior to an acceptable degree for practical application purposes. However, to provide input parameters for this model, the dilation angle is required. In such a case, the calculation of the dilation angle during soil shearing is then substantial. this paper presents different methods for quantifying the dilation angle of non-cemented poorly graded sand with emphasis on the direct shear test. © 2025 EUROSIS-ETI.

关键词： Internal friction

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On the perceived relevance of critical internal quality attributes when evolving software features 16

On the perceived relevance of critical internal quality attr...

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16th IEEE/ACM international conference on Cooperative and Human Aspects of software engineering (CHASE)

作者： Fernandes, Eduardo Kalinowski, Marcos Fed Univ Minas Gerais UPMG Dept Comp Sci Belo Horizonte MG Brazil Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Informat Rio De Janeiro Brazil

ISBN: (纸本)9798350337341

Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Indeed, internal attributes can become critical if their measurements assume anomalous values. Yet, current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. this qualitative study investigates the developers' perception of the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industrial case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant when adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, the reasons behind the relevance of each critical attribute, and the interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. the other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.

关键词： internal quality attribute refactoring software feature software evolution industry case study

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Dynamic knowledge graph completion through time-aware relational message passing 28

Dynamic knowledge graph completion through time-aware relati...

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28th international Computer conference, Computer Society of Iran, CSICC 2023

作者： Baqinejadqazvini, Amirhossein Tahery, Saedeh Farzi, Saeed K. N. Toosi University of Technology Faculty of Computer Engineering Department of Software Engineering Tehran Iran K. N. Toosi University of Technology Faculty of Computer Engineering Department of Artificial Intelligence Tehran Iran Tehran Iran

ISBN: (纸本)9798350338195

As the structure of knowledge graphs may vary over time, static knowledge graph completion methods do not deal with time-varying knowledge graphs. However, examining the paths between entities and entities' context information can lead to more accurate completion methods. this paper attempts to complete dynamic (time-varying) knowledge graphs by combining time-aware relational paths and relational context. the proposed model can improve dynamic knowledge graph completion methods by leveraging neural networks. Experimental results conducted on two standard datasets, ICEWS14 and ICEWS05-15, indicate our model's superiority in terms of Mean Reciprocal Rank (MRR) and Hit@k over its well-known counterparts, such as DE-TransE and DE-DistMult. © 2023 IEEE.

关键词： knowledge graph

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Towards Building a Reputation-Based Microservices Trust Model Using Similarity Domains

Towards Building a Reputation-Based Microservices Trust Mode...

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19th international conference on software Technologies, ICSOFT 2024

作者： Lu, Zhongyi Delaney, Declan T. Li, Tong Lillis, David School of Computer Science University College Dublin Dublin 4 Ireland School of Electrical and Electronic Engineering University College Dublin Dublin 4 Ireland Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789897587061

Microservices have been seen as a solution to open systems, within which microservices can behave arbitrarily. this requires the system to have strong trust management. However, existing microservices trust models cannot fully support open systems. In this paper, we propose a reputation-based trust model designed for open microservices that groups similar microservices within the same "similarity domain" and includes a trust bootstrapping process and a comprehensive trust computation method. Our proposal introduces a new concept called "trust balancing" to assure that all microservices can fairly be incorporated into the operation of the system. the design of the evaluation plan is also introduced to demonstrate the suitability of the proposed model to open microservice systems. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Open systems

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Identifying Gender Factors and Strategies that Promote Women's Retention in engineering and STEM MOOCs: A Systematic Literature Review 27th

Identifying Gender Factors and Strategies that Promote Women...

引用

27th international conference on Interactive Collaborative Learning-ICL

作者： Villa, Genny Nantsou, Tina Dagklis, Evangelos Callea, Viviana Otegui, Ximena Tovar, Edmundo Ursache, Mihai Univ Montreal Montreal PQ Canada Natl & Kapodistrian Org Univ Athens Sect Elect Phys & Syst Dept Phys Athens 15784 Greece Univ Macedonia Dept Appl Informat Egnatia 56 Thessaloniki 54636 Greece Sapienza Univ Rome Fac Civil & Ind Engn I-00184 Rome RM Italy Univ La Republ Fac Engn JH & Reissig 565 Montevideo Uruguay Univ Politecn Madrid Languages & Informat Syst & Software Engn Dept Campus Montegancedo Madrid 28660 Spain FLY FISH SRL Via Gregoriana 5 I-00187 Rome RM Italy

ISBN: (纸本)9783031856518;9783031856525

Massive Open Online Courses (MOOCs) allow their participants to acquire knowledge in various fields, such as STEM, at low cost or for free, provided they have access to the Internet and a web-enabled device. this study investigates a critical phenomenon in STEM MOOCs: dropout rates from a gender perspective. Our objective is to identify retention factors of women in engineering and STEM MOOCs through a PRISMA-based Systematic Literature Review of conference papers and journal articles retrieved from IEEE Xplore and Scopus. Seventy-two papers were retrieved, of which 15 were relevant, with the majority of the information pertaining to the strategies and best practices enhancing women's retention and engagement in STEM MOOCs. Gender socialization, lower self-efficacy and time constraints were the main dropout factors, although female students have been found to adapt more easily to digital learning environments. this comprehensive analysis aims to establish solid foundations for future MOOCs and academic studies on this topic.

关键词： MOOC engineering-STEM Gender-Women Dropout Retention

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Optimal Coordination of UUVs based on Deep Q-Learning 19

Optimal Coordination of UUVs based on Deep Q-Learning

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Pham Van, Tien Wireless Communication Laboratory School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9798331507817

this paper presents an autonomous strategy in planning movement, task execution, resource recharging, and data reporting for unmanned underwater vehicles (UUV). they are assumed to collaboratively work on an underwater campaign, executing such tasks as seabed surveillance, resource exploration, search & rescue, environment cleaning, etc. Each UUV visits multiple underwater sites, both to gather data and to handle tasks thereat. Data exchange among themselves and with surface stations is realized via acoustic links that are of narrow bandwidth. To report heavy data and recharge resources, they consequently need to physically approach surface stations. We formulate the problem of path planning, task assignment and choosing surface stations collectively for a swarm of UUVs as a Markov Decision Process. Cumulative reward earned by UUVs essentially depends on task handling and data collected from the sites, as well as movement costs they have spent. Additionally, the travel of each UUV subjects to constraints on its energy and resource reserves, as well as on task attributes. the optimal coordination is attained by the system if it sticks to the moving and replenishing policy that brings up the highest cumulative reward. To reduce the overall computation and storage costs, we construct a double deep Q-learning scheme. Numerical results clearly show the effectiveness of the proposed strategy regarding cumulative reward, energy consumption and task acceptance. © 2025 IEEE.

关键词： Markov processes

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ResNet-50 Powered Masked Face Detection: A Deep Learning Perspective 9

ResNet-50 Powered Masked Face Detection: A Deep Learning Per...

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9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Choudhary, Nilam Rathore, Pramod Singh Kumar, Loveleen Rajaan, Rajesh Sharma, Aakriti Sinha, Dibakar Swami Keshvanand Institute of Technology Management & Gramothan Department of Computer Science & Engineering Jaipur India Manipal University Jaipur Department of Computer And Communication Engineering Jaipur India Manipal University Jaipur Department of Computer Science And Engineering Jaipur India

ISBN: (纸本)9798350394474

Due to the widespread use of face masks as a result of the COVID-19 pandemic, facial recognition technology, which is routinely employed for security screening in workplaces, is encountering substantial difficulties. these algorithms were commonly trained using photos of unmasked human faces, which posed challenges in recognising individuals wearing masks. In order to tackle this problem, this study introduces a deep learning model that is specifically tailored to properly identify individuals wearing masks. the model utilises the ResNet-50 architecture and has high proficiency in recognising individuals wearing face masks. the findings of this study can be incorporated into existing facial recognition software utilized for security screening purposes. © 2024 IEEE.

关键词： Face recognition

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MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined through Enhanced Repository Discovery 20

MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined th...

引用

20th international conference on Predictive Models and Data Analytics in software engineering (PROMISE)

作者： Akhoundali, Jafar Nouri, Sajad Rahim Rietveld, Kristian Gadyatskaya, Olga Leiden Univ LIACS Leiden Netherlands Islamic Azad Univ Ramsar Iran

ISBN: (纸本)9798400706752

Vulnerability datasets have become an important instrument in software security research, being used to develop automated, machine learning-based vulnerability detection and patching approaches. Yet, any limitations of these datasets may translate into inadequate performance of the developed solutions. For example, the limited size of a vulnerability dataset may restrict the applicability of deep learning techniques. In our work, we have designed and implemented a novel work-flow with several heuristic methods to combine state-of-the-art methods related to CVE fix commits gathering. As a consequence of our improvements, we have been able to gather the largest programming language-independent real-world dataset of CVE vulnerabilities with the associated fix commits. Our dataset containing 26,617 unique CVEs coming from 6,945 unique Github projects is, to the best of our knowledge, by far the biggest CVE vulnerability dataset with fix commits available today. these CVEs are associated with 31,883 unique commits that fixed those vulnerabilities. Compared to prior work, our dataset brings about a 397% increase in CVEs, a 295% increase in covered open-source projects, and a 480% increase in commit fixes. Our larger dataset thus substantially improves over the current real-world vulnerability datasets and enables further progress in research on vulnerability detection and software security. We release to the community a 14 GB PostgreSQL database that contains information on CVEs up to January 24, 2024, CWEs of each CVE, files and methods changed by each commit, and repository metadata. Additionally, patch files related to the fix commits are available as a separate package. Furthermore, we make our dataset collection tool also available to the community.

关键词： Vulnerability dataset software repository mining open-source real-world vulnerability dataset dataset CVE

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RS-YOLOv10: Enhancing YOLOv10 For Accurate Small-Object Detection 19

RS-YOLOv10: Enhancing YOLOv10 For Accurate Small-Object Dete...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Nguyen, Dat Minh-Tien Huynh-the, thien Department of Computer and Communications Engineering HCMC University of Technology and Education Ho Chi Minh City71307 Viet Nam

ISBN: (纸本)9798331507817

the capability to detect objects in remote sensing images has proven to deliver exceptional results spanning multiple fields of application, setting it apart from other techniques. However, with limited resources, it faces numerous challenges related to noise, poor information quality, and particularly the detection of small objects. To overcome these challenges, we introduce RS-YOLOv10, a model built on YOLOv10, designed to reduce resource consumption and improve accuracy for remote sensing applications. First, to enhance the backbone network, we apply the Efficient Attention Mechanism, which helps the model prioritize key information within the feature map, the model boosts detection efficiency. In addition, we propose the Weighted Focused Feature Pyramid Network (WFFPN), which effectively minimizes the network while enhancing the efficiency and performance of multi-scale feature fusion. When evaluated on the VisDrone2019 dataset, RS-YOLOv10 demonstrated an approximate 10% decrease in the number of parameters relative to the baseline model and significantly improves metrics, with precision increasing by 2.5%, mAP50 increasing by 1.4%, and mAP95 increasing by 1.1%. Relative to other cutting-edge models, our model shows superior performance, particularly for smaller object detection. © 2025 IEEE.

关键词： Remote sensing

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A Novel CNN-Transformer Framework for COVID-19 Diagnosis from ECG Images 7

A Novel CNN-Transformer Framework for COVID-19 Diagnosis fro...

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7th international conference on Computer Information Science and Application Technology, CISAT 2024

作者： Huang, Xiaofei Xu, Qingchao Pei, Lisha Cai, Yuecheng Dalian University College of Information Engineering Dalian China Dalian University College of Software Engineering Dalian China

ISBN: (纸本)9798350375107

Research has demonstrated that COVID-19 impacts both the respiratory and cardiovascular systems. Timely and accurate detection of COVID-19 infection using electrocardiogram (ECG) images holds significant research importance. Most researchers have implemented convolutional neural networks (CNNs) to develop COVID-19 diagnostic models based on electrocardiograms (ECGs). However, such models primarily focus on local feature extraction and often lack understanding of inter-lead relationships. this study proposes a novel CNN-transformer framework that enhances the understanding and utilization of inter-lead features by leveraging CNN with CBAM for effective local feature extraction and Transformer encoders for generating rich, stable, and globally representative features. Finally, a classifier based on pre-trained DenseNet161 is employed for diagnosis. the proposed model achieves excellent performance across various evaluation metrics on the COVID-19 dataset, notably achieving an F1 score of 0.98, precision and specificity of 1.0, demonstrating robust diagnostic capabilities. © 2024 IEEE.

关键词： COVID-19

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