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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3339 条记录，以下是771-780 订阅

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Load Characterization and Fatigue Reliability Prediction of Large Aerospace Planetary Gear

Load Characterization and Fatigue Reliability Prediction of ...

引用

Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Ming Li Wanyu Jiang Bo Fan Liyang Xie School of Mechatronics Engineering Shenyang Aerospace University Shenyang China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

the reliability level of large aviation planetary mechanisms, serving as the cornerstone and essential component for various heavy aircraft transmission systems, significantly impacts aircraft affordability and service safety. Before predicting the fatigue performance of the gear transmission system, it is necessary to develop a comprehensive simulation model of the gear system and calculate the stress values for failure modes such as tooth contact fatigue and tooth root bending fatigue. In addition, By using hierarchical finite element technology, the coupling effect of the global elastic behavior of the system on the fatigue load history of the gear teeth is analyzed in depth, and accurate calculations are carried out based on this to fit the probability fatigue strength of the gear teeth. this provides both economical and efficient load and strength input variables for the construction of the system reliability prediction model, and innovatively establishes a new series system reliability model. Finally, the computational findings from the new model is contrasted with the traditional one to show the superiority of the proposed model.

关键词： Gears Computational modeling Interference predictive models Fatigue Finite element analysis software reliability Reliability Stress Load modeling

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BOAformer: Object Detection Network Based on Background-Object Attention Transformer

BOAformer: Object Detection Network Based on Background-Obje...

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Yong Yang Chenbin Liang Shuying Huang Xiaozheng Wang School of Computer Science and Technology Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many prediction boxes and use the same prediction operation on them. Although these methods can obtain higher predictive values for high certainty objects, they may identify objects with lower certainty, such as small or fuzzy objects, as negative samples or miss them. To address this issue, this paper presents an object detection network based on background-object attention transformer (BOAformer), which improves the accuracy of object detection by establishing the relationship between background and object features. Firstly, the existing classification model is employed to obtain scores of object prediction boxes. On this basis, a scoring mechanism is designed to filter out high scoring object prediction boxes to reduce the computational complexity of the later network. then, to improve the accuracy of object detection, BOAformer is constructed, which obtains auxiliary classification values of objects by learning the relationship between background and object features, and adds them to the initial detection results. Finally, many experiments are conducted to verify the effectiveness of BOAformer. the experimental results show that the designed network improves the accuracy of object detection and can detect small or lost objects in the background.

关键词： Training Visualization Accuracy Computational modeling Semantic segmentation Object detection predictive models

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Using Multiple Code Representations to Prioritize Static Analysis Warnings 14

Using Multiple Code Representations to Prioritize Static Ana...

引用

14th international conference on Knowledge and Systems engineering, KSE 2022

作者： Vu, thanh Trong Vo, Hieu Dinh Vnu University of Engineering and Technology Faculty of Electronics and Telecommunications Hanoi Viet Nam Vnu University of Engineering and Technology Faculty of Information Technology Hanoi Viet Nam

ISBN: (数字)9781665452816

ISBN: (纸本)9781665452809

In order to ensure the quality of software and prevent attacks from hackers on critical systems, static analysis tools are frequently utilized to detect vulnerabilities in the early development phase. However, these tools often report a large number of warnings with a high false-positive rate, which causes many difficulties for developers. In this paper, we introduce VULRG, a novel approach to address this problem. Specifically, VuLRG predicts and ranks the warnings based on their likelihoods to be true positives. To predict these likelihoods, VuLRG combines two deep learning models CNN and BiGRU to capture the context of each warning in terms of program syntax, control flow, and program dependence. Our experimental results on a real-world dataset of 6,620 warnings show that VuLRG's Recall at Top-50% is 90%. this means that using VuLRG, 90% of the vulnerabilities can be found by examining only 50% of the warnings. Moreover, at Top-5%, VULRG can improve the state-of-the-art approach by +30% in both Precision and Recall. © 2022 IEEE.

关键词： Static analysis

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Trajectory Prediction for V2X Collision Warning Using Pruned Transformer Model

Trajectory Prediction for V2X Collision Warning Using Pruned...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), international conference on

作者： Jinquan Tan Haoran Li Qiang Zhang China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

this study explores the use of a pruned Transformer model for vehicle trajectory prediction in V2X collision warning systems, focusing on balancing accuracy and computational efficiency. By employing weight pruning, the model significantly reduces parameters and inference time while maintaining competitive performance with traditional LSTM models. Real-world experiments across diverse traffic scenarios demonstrate the pruned Transformer’s strengths in trajectory prediction error and false warning rates. However, increased missed warnings in dynamic scenarios and longer prediction times highlight areas for further optimization. these findings establish the pruned Transformer as a promising candidate for real-time V2X applications under resource constraints.

关键词： Adaptation models Computational modeling predictive models Transformers Real-time systems Robustness Trajectory Vehicle dynamics Vehicle-to-everything Long short term memory

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Evaluating the Efficacy of Feature Selection Methods in Cardiovascular Disease Prediction with Machine Learning

Evaluating the Efficacy of Feature Selection Methods in Card...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Rezwanul Haque S. M. Mahedy Hasan Azmain Yakin Srizon Anwar Hossain Efat Md. Farukuzzaman Faruk Mahjabin Oishe Nahrin Jannat Mostarina Mitu Department of Computer Science and Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

Cardiovascular disease (CVD) remains a primary contributor to global mortality, necessitating refined predictive models. Machine learning (ML) has emerged as a pivotal tool in augmenting clinical decisions, particularly in CVD prediction. Nonetheless, the performance of ML classifiers is substantially influenced by the judicious selection of relevant features. this manuscript presents a focused comparative analysis of feature selection methodologies in CVD prediction through ML classifiers. Utilizing a clinical dataset, the study assesses the efficacy of various classifiers based on distinct feature subsets. Empirical results indicate that the Random Forest classifier, with a mere five selected features, attains a remarkable 99.70% accuracy, thus qualifying as an optimal choice for clinical settings. the research underscores the necessity of scrupulously evaluating diverse combinations of feature selection and learning algorithms to derive the most effective model. Collectively, this work provides pivotal insights into optimal feature selection for precise CVD prediction, thereby contributing to the development of more efficient predictive strategies.

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XGBoost Regression Algorithms for Efficient Predictions on Inventory Sales and Management

XGBoost Regression Algorithms for Efficient Predictions on I...

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Electronics and Electrical engineering Technology (EEET), international conference on

作者： Doreen Ying Ying Sim Zhenyang Wei School of Computer Science Faculty of Science and Engineering University of Nottingham (Malaysia campus) Selangor Malaysia College of Engineering Computing and Cybernetic Australian National University Canberra Australia

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

A combined implementation of machine learning(ML) based prediction models on the stock market using mainly Extreme Gradient Boosting (XGBoost 1 ) regression algorithms has been researched upon. there have been existing implementations of the ML-based models in the prediction of other business models. the ML-based model called XGBoost 1 regression algorithm is experimented as the preferred model to predict the sale amount in diverse business areas with significant efforts to testify the performance in prediction accuracy of the results. After applying the XGBoost regression algorithm to the assigned datasets on the past sales data of market for predicting the future inventory stock sales, the result has been verified as highly accurate in the test data output in the test data environment. Evaluation on the coefficient of determination (R square) shows that majority of the test results is satisfactory, which more than half of the predicted results having extremely low error rate. the deviation rate of the majority of the remaining results is also within the acceptable range. Only a few portions have a significantly large unexplainable difference which is within our expectations. 1 Extreme Gradient Boost (XGBoost) is a strong classifier, i.e. an ensemble technique in Machine Learning classification

关键词： Training Machine learning algorithms Accuracy software algorithms predictive models Prediction algorithms software

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Minimizing User Connectivity Costs and Latency Between Controllers and Switch-Controllers for software Defined Networking 19th

Minimizing User Connectivity Costs and Latency Between Cont...

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Mobile Web and Intelligent Information Systems - 19th international conference, MobiWIS 2023, proceedings

作者： Viveros, Andres Adasme, Pablo Firoozabadi, Ali Dehghan Department of Electrical Engineering Universidad de Santiago de Chile Avenida Víctor Jara No 3519 Santiago9170124 Chile Department of Electricity Universidad Tecnológica Metropolitana Av. Jose Pedro Alessandri 1242 Santiago7800002 Chile

ISBN: (纸本)9783031397639

In this paper, we consider the problem of assigning users to switches and controllers at minimum connectivity costs. We simultaneously minimize the existing latency between controllers and switch controllers for software-defined networks (SDN). In particular, we propose two mixed-integer quadratic programming formulations and their standard linearization counterparts. All the proposed models are solved with the CPLEX solver. More precisely, the quadratic models are solved with the branch and cut (B &C) algorithm of the solver. Whilst the linear ones are solved with both, the B &C and with the automatic Bender’s decomposition algorithmic option of the CPLEX solver. To the best of our knowledge, this work constitutes a first attempt to propose mathematical formulations while including user connectivity to a backbone software-defined network. Notice that we consider which is the best strategy for connecting users to the backbone, either connecting them to switches or to the controller nodes of the network. Our preliminary numerical results indicate that for most of the benchmark instances considered the second strategy which connects users to switches has a better performance in terms of objective function values. On the other hand, we observe that the linear models offer better performance in terms of CPU times to get the optimal or best objective values. Finally, we see that the linear models can be solved faster with the branch and cut algorithm than using Bender’s decomposition approach. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Controllers

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PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Time 25

PreNeT: Leveraging Computational Features to Predict Deep Ne...

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2025 international conference on Performance engineering-ICPE-Annual

作者： Pourali, Alireza Boukani, Arian Khazaei, Hamzeh York Univ Toronto ON Canada

ISBN: (纸本)9798400710735

Training deep learning models, particularly Transformer-based architectures such as Large Language models (LLMs), demands substantial computational resources and extended training periods. While optimal configuration and infrastructure selection can significantly reduce associated costs, this optimization requires preliminary analysis tools. this paper introduces PreNeT, a novel predictive framework designed to address this optimization challenge. PreNeT facilitates training optimization by integrating comprehensive computational metrics, including layer-specific parameters, arithmetic operations and memory utilization. A key feature of PreNeT is its capacity to accurately predict training duration on previously unexamined hardware infrastructures, including novel accelerator architectures. this framework employs a sophisticated approach to capturing and analyzing the distinct characteristics of various neural network layers, thereby enhancing existing prediction methodologies. through proactive implementation of PreNeT, researchers and practitioners can determine optimal configurations, parameter settings, and hardware specifications to maximize cost-efficiency and minimize training duration. Experimental results demonstrate that PreNeT achieves up to 72% improvement in prediction accuracy compared to contemporary state-of-the-art frameworks.

关键词： Training time prediction Computational complexity Large Language models

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A CNN-Transformer Knowledge Tracking Model Combining with Forgotten Factors

A CNN-Transformer Knowledge Tracking Model Combining with Fo...

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Computer Science and Technologies in Education (CSTE), international conference on

作者： Xiaosheng Huang Yin Wu Yixin Luo School of Software East China Jiaotong University NanChang China

ISBN: (数字)9798350351804

ISBN: (纸本)9798350351811

Knowledge tracking plays a crucial role in intelligent education, contributing significantly to promoting educational equity and enhancing educational quality. To address the limitations of the Transformer-based knowledge tracking model in terms of forgetting characteristics and local modeling, this paper proposes a knowledge tracking approach that integrates convolutional neural network (CNN) and Transformer, drawing on the Ebbinghaus forgetting curve theory and memory trace decay theory. this paper incorporates three forgetting factors into the Transformer model: the time interval of learning, the number of times a learner revisits the same knowledge points, and the learner's mastery of knowledge points to address its deficiencies in forgetting characteristics. Simultaneously, one-dimensional convolution is used to model the learner's historical interaction sequence to address the relative shortcomings of the Transformer model in local modeling. Finally, the outputs of the global modeling Transformer layer and the local modeling convolution layer are concatenated to obtain the learner's knowledge state level, thus enabling predictions of the learner's future performance. Experiments on four main datasets show that compared with BKT, DKT, DKVMN, SAKT and SAINT, the proposed model has achieved a significant improvement in AUC performance and running time.

关键词： Knowledge engineering Convolution Education Psychology predictive models Transformers Market research

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Enhancing Source Code Vulnerability Detection Using Flattened Code Graph Structures

Enhancing Source Code Vulnerability Detection Using Flattene...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Zhequ Sun Ke Liu Yu Yang School of Computer Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Source code vulnerability detection is essential for software security, but identifying subtle vulnerabilities in code is challenging. While Transformer-based pre-trained models have achieved success in this area, they typically treat code as a sequence of text, neglecting its structural characteristics. To address this, we propose a novel approach that uses a flattened code graph structure, which effectively captures both semantic and syntactic relationships in the code. By transforming the code into a structured sequence, this method allows the model to explicitly learn from both semantic and structural information. We combine the flattened code graph structure tokens with pre-trained models and fine-tune them to enhance performance in vulnerability detection. Experimental results demonstrate significant improvements in recall, particularly in detecting vulnerabilities in complex code. this approach provides an efficient and robust solution for source code vulnerability detection, advancing the field of software security.

关键词： Deep learning Codes Source coding Semantics Syntactics Feature extraction Transformers software Robustness software reliability

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