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

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Large Scale Study of Orphan Vulnerabilities in the software Supply Chain 19

Large Scale Study of Orphan Vulnerabilities in the Software ...

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19th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Reid, David Rahkema, Kristiina Walden, James Univ Tennessee Knoxville TN 37996 USA Univ Tartu Tartu Estonia Northern Kentucky Univ Highland Hts KY USA

ISBN: (纸本)9798400703751

the security of the software supply chain has become a critical issue in an era where the majority of software projects use open source software dependencies, exposing them to vulnerabilities in those dependencies. Awareness of this issue has led to the creation of dependency tracking tools that can identify and remediate such vulnerabilities. these tools rely on package manager metadata to identify dependencies, but open source developers often copy dependencies into their repositories manually without the use of a package manager. In order to understand the size and impact of this problem, we designed a large scale empirical study to investigate vulnerabilities propagated through copying of dependencies. Such vulnerabilities are called orphan vulnerabilities. We created a tool, VCAnalyzer, to find orphan vulnerabilities copied from an initial set of vulnerable files. Starting from an initial set of 3,615 vulnerable files from the CVEfixes dataset, we constructed a dataset of more than three million orphan vulnerabilities found in over seven hundred thousand open source projects. We found that 83.4% of the vulnerable files from the CVEfixes dataset were copied at least once. A majority (59.3%) of copied vulnerable files contained C source code. Only 1.3% of orphan vulnerabilities were ever remediated. Remediation took 469 days on average, with half of vulnerabilities in active projects requiring more than three years to fix. Our findings demonstrate that the number of orphan vulnerabilities not trackable by dependency managers is large and point to a need for improving how software supply chain tools identify dependencies. We make our VCAnalyzer tool and our dataset publicly available.

关键词： Orphan Vulnerabilities Copy-based Code Reuse software Supply Chain

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GM-YOLO: A Lightweight Small Target Detection Model 24

GM-YOLO: A Lightweight Small Target Detection Model

引用

6th international conference on Telecommunications and Communication engineering

作者： Xiao, Linsong Li, Wenzao Zhang, Xiaoqiang Jiang, Hong Wan, Bing Ren, Dehao Chengdu Univ Informat Technol Sch Commun Engn Chengdu Sichuan Peoples R China Sichuan GFS Informat Technol Co Ltd Chengdu Sichuan Peoples R China Chengdu Polytech Sch Software Chengdu Sichuan Peoples R China

ISBN: (纸本)9798400709630

Traditional target detection models struggle to maintain accuracy and improve inference speed for small target detection on computationally limited embedded devices. To solve this problem, this paper proposes an Unproved YOLOv5 model: GM-YOLO. First, the GC3 module is proposed to replace the C3 module. It reduces the number of parameters and computational complexity while improving the model's feature representation and nonlinear expression abilities. Secondly, a novel multi -scale channel and position attention (MSCP) is proposed to enhance small target feature extraction arid reduce feature information loss. Finally, a small -target detection layer is added to the neck section to enhance feature resolution. Experimental results on the VisDrone2019 dataset show that GM-YOLO outperforms other models. Compared to YOLOv5-n, GFLOPS is reduced by 11.90% and mAP(050 is improved by 6.9%.

关键词： small target detection YOLOv5 GC3 MSCP

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Enhancing Exercise Efficacy: A predictive EMG Analysis for Dumbbell Press Workouts 6

Enhancing Exercise Efficacy: A Predictive EMG Analysis for D...

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6th IEEE international conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Pattanashetty, Pranav T. Chaurasia, Shreya Upadhyaya, Sujatha R. Visweswara, Ramesh Debur Pes University Dept. of Computer Science and Engineering Bengaluru India Ramaiah Memorial Hospital Dept. of Physiotherapy Bengaluru India

ISBN: (纸本)9798350389692

the current paper describes a preliminary model that would help improve the dumbbell press exercise through the use of electromyography (EMG). the model recognizes the optimal and marginal repetitions in sets based on the analysis of the EMG data from the pectoral and deltoid muscles. the model provides features like load, rest, and technique hints for set modifications that will improve set quality and minimize non-optimal repetitions in order to provide safer and more efficient training. the findings of this study can be used to give customized training advice by presenting immediate feedback according to individual EMG patterns. the data used in this study is self-reported and obtained from two participants who have previous experience exercising, so they are aware of the movements done. Additionally, a positive feedback system was established, a formula for dynamic adjustment of the exercise plan, taking into account the fitness and experience of the participants was introduced, which improved the effectiveness of the exercises. the proposed paradigm helps to optimize training outcomes in strength and conditioning programs regarding the maximization of exercise performance and the minimization of the injury risk. the study entails three series of exercise tasks at varying angles with participants completing multiple sets of exercises and EMG data collected. A three-layers Artificial Neural Network, Random Forest Classifier, Logistic Regression and Long Short-Term Memory (LSTM) models were used, and it was observed that the models were highly accurate in the classification of exercise quality and the feedback generated could help in improving performance. © 2024 IEEE.

关键词： Electromyography

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Elastic-plastic modeling method based on temporal convolutional network 6

Elastic-plastic modeling method based on temporal convolutio...

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

作者： Wang, Haibo Niu, Qiang School of Mechanical and Material Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9781510668249

Because the deformation of the material under load is strongly correlated with the loading path, the Temporal Convolutional Network (TCN), which can satisfy the time-series learning, is chosen to model the elastic-plastic intrinsic relationship of the material. the ABAQUS software is used to write scripts to construct the data set required for training. After training and learning the stress-strain states and their increments for different loading states, the constructed network model can well characterize the yielding process and the subsequent yielding evolution of the material. Compared with the traditional phenomenological model, it has better flexibility and computational efficiency, and provides a new method for constructing the constitutive model of materials. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Constitutive models

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CSECU-DSG @ Causal News Corpus 2023: Leveraging RoBERTa and DeBERTa Transformer Model with Contrastive Learning for Causal Event Classification 6

CSECU-DSG @ Causal News Corpus 2023: Leveraging RoBERTa and ...

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6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2023

作者： Hossain, Md. Akram Aziz, Abdul Chy, Abu Nowshed Department of Computer Science and Engineering University of Chittagong Chattogram4331 Bangladesh

ISBN: (纸本)9789544520892

Cause-effect relationships play a crucial role in human cognition, and distilling cause-effect relations from text helps in ameliorating causal networks for predictive tasks including natural language-based financial forecasting, text summarization, and question-answering. However, the lack of syntactic clues, the ambivalent semantic meaning of words, and complex sentence structures make it one of the challenging tasks in NLP. To address these challenges, CASE-2023 introduced a shared task 3 with two subtasks focusing on event causality identification with causal news corpus. In this paper, we demonstrate our participant systems for this task. We leverage two transformers models including DeBERTa and Twitter-RoBERTa along with the weighted average fusion technique to tackle the challenges of subtask 1 where we need to identify whether a text belongs to either causal or not. For subtask 2 where we need to identify the cause, effect, and signal tokens from the text, we proposed a unified neural network of DeBERTa and DistilRoBERTa transformer variants with contrastive learning techniques. the experimental results showed that our proposed method achieved competitive performance among the participants’ systems and achieved 4th and 3rd rank in subtasks 1 and 2 respectively. © CASE 2023 - proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, associated with 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023.

关键词： Natural language processing systems

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software Attribute Comprehensive Trust Measurement Model 24

Software Attribute Comprehensive Trust Measurement Model

引用

6th international conference on software engineering and Development, ICSED 2024

作者： Luo, Yihang Sun, Pengxuan Faculty Of Information Technology Beijing University Of Technology China

ISBN: (纸本)9798400718052

With the rapid development of the information age, software security has become a crucial component. In the realm of security, quantifying the trustworthiness of software has become a focal point, yielding a series of research outcomes. While much of this work focuses on studying the trustworthy attributes of software from a static perspective, there are also efforts in designing dynamic assessment models. However, a comprehensive consideration of the holistic security attributes of software and the complexity of evaluating software security has not been adequately addressed. To address these challenges, this paper proposes a comprehensive trust evaluation model tailored for software attribute security. In the computation of the direct trust model, time factor and reward-penalty factors are introduced to enhance the accuracy of the trust model. the recommended trust model measures the evaluative disparity among software entities to derive recommended trustworthiness. the comprehensive trust model combines direct trust and recommended trust, introducing a dynamic weight allocation function to enhance adaptability. Finally, through simulation experiments comparing various trust models, the effectiveness of this approach is validated. © 2024 ACM.

关键词： software Security

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Detecting Bitcoin CoinJoin Transactions Using Machine Learning 6

Detecting Bitcoin CoinJoin Transactions Using Machine Learni...

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6th international conference on Blockchain Computing and Applications, BCCA 2024

作者： Dekhil, Oumayma Tonga-Naha, Rodrigue Mayeli-Feridani, Mikaeil Najjar, Fatma Zhang, Kaiwen Department of Software and IT Engineering ÉTS Montréal QC Canada

ISBN: (纸本)9798350351538

Anonymity is one of the characteristics that makes Bitcoin mainstream. However, various approaches have been proposed to deanonymize Bitcoin to reveal the identities of the people behind the addresses. In order to enhance anonymity, Bitcoin mixing services were developed to obscure links between Bitcoin addresses. CoinJoin transactions method is one of the most used mixing approaches. the CoinJoin technique is a concept where multiple users merge their transactions into one larger transaction [16]. In this article, we propose a new system of detecting CoinJoin transactions using machine learning. Firstly, we developed a new algorithm to extract connected transactions and trace them back to the Coinbase transaction. Afterwards, we conducted a rigorous analysis of the data and performed an ablation study to identify the most relevant features of our machine learning models. then, we implemented and fine-tuned them using an automated tool called OPTUNA, after which we trained the models and evaluated their performances, including accuracy, precision, and F1 score. © 2024 IEEE.

关键词： Anonymity

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CAVDL:A Comparative Analysis Of Diverse DL models For Enhancing COVID-19 Images 6

CAVDL:A Comparative Analysis Of Diverse DL Models For Enhanc...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Jain, Arti Kaliyar, Rohit Kumar Sharma, Shallu School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India

ISBN: (纸本)9798350369748

the global health crisis has brought to light the critical role that medical imaging, particularly X-rays, play in the diagnosis and surveillance of COVID-19 infections. the capacity to increase X-ray picture quality and diagnostic accuracy has greatly improved with the development of sophisticated image enhancing methods. the objective of this research is to assess and compare the efficacy of several image enhancement models in improving the visual quality, feature preservation, and diagnostic usefulness of COVID-19 X-ray pictures. To give a comprehensive understanding of each model’s limitations and robustness, we use a strict evaluation process that includes both quantitative indicators and qualitative assessments. We employed a variety of evaluation criteria, such as PSNR (peak signal-to-noise ratio) and structural similarity index metrics (SSIM), to do the comparison. In this study, using a carefully chosen COVID-19 X-ray dataset, we thoroughly compared different state-of-the-art models with existing classification models. © 2024 IEEE.

关键词： COVID-19

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Research on AUV Navigation State Prediction Method Using MultiHead Attention Mechanism in a CNN-BiLSTM Model 7

Research on AUV Navigation State Prediction Method Using Mul...

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7th international conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2024

作者： Pan, Wei Lv, Bangjun Peng, Likun School of Power Engineering Naval University of Engineering Wuhan430030 China

ISBN: (纸本)9781510681866

In response to the issue of low accuracy in predicting the navigation state of existing AUV motion models, a predictive model for AUV navigation state based on a combination of a time-domain convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) recurrent neural network enhanced by a multi-head attention mechanism is proposed. Initially, the CNN extracts the feature sequence of the AUV navigation state. Subsequently, the BiLSTM network is utilized to learn the trajectory sequences of preceding and following conditions to extract dependencies within each navigation state in the sequence. the multi-head attention mechanism is then introduced to adjust the weights of different attribute features, prioritizing those with a greater impact on the navigation state. this ultimately facilitates the accurate prediction of the AUV navigation state. A comparative experiment using AUV lake test data demonstrated that the CNN-MABiLSTM model achieves higher prediction accuracy and better fitting than the CNN-BiLSTM model, thus verifying the effectiveness and practicality of the designed CNN-MAiLSTM model in predicting AUV navigation states. © 2024 SPIE.

关键词： Convolutional neural networks

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Improving Cybersecurity Named Entity Recognition with Large Language models 6

Improving Cybersecurity Named Entity Recognition with Large ...

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6th international conference on software engineering and Computer Science, CSECS 2023

作者： Qiao, Zhe Zhang, Chen Du, Gang Information Security Management and Operation Center China Mobile Ltd Beijing10053 China China Mobile Group Design Institute Co. Ltd Beijing100080 China

ISBN: (纸本)9798350306378

A lot of attention has been paid to cybersecurity threat intelligence analysis based on security knowledge graphs because they can evaluate multi-source threat intelligence data at a fine-grained level. the primary effort in building knowledge graphs is named entity recognition (NER). However, the retrieved features from classic NER algorithms are insufficient to identify novel security entities in the cybersecurity field. Currently, the natural language processing(NLP) field has made unprecedented progress, driven by large language models(LLMs). In this paper, we present a BERT- BiLSTM-CRF cybersecurity NER method based on BiLSTM-CRF that combines the pre-trained large language model BERT. the preprocessed annotated corpus is fed into the Bi-LSTM and CRF models, which employ the word vectors to extract contextual features and annotate 9 different categories of named entities. the proposed cyberecurity NER method is effective in identifying cybersecurity entities on large-scale cybersecurity datasets, and the relevant evaluation indexes are superior to other algorithms, according to comparative experiments carried out within the cyberspace security corpus. © 2023 IEEE.

关键词： Cybersecurity

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