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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2511 条记录，以下是131-140 订阅

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Enhancing Supply Chain Risk Management with Large Language models: software Prototyping and Interactive Visualization 20

Enhancing Supply Chain Risk Management with Large Language M...

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20th IEEE international conference on e-Business engineering, ICEBE 2024

作者： Zhao, Ming Hussain, Omar Zhang, Yu Saberi, Morteza Leshob, Abderrahmane School of Business Unsw Canberra Canberra Australia School of Computer Science University of Technology Sydney Sydney Australia University of Quebec in Montreal Department of Management Science Montreal Canada

ISBN: (纸本)9798350365856

the rapid evolution of risk landscapes in supply chains necessitates innovative management tools that enhance decision-making and process efficiency. Building on our previously proposed framework, this paper introduces a software prototype named LARD-SC (LLMs for Automated Risk Detection in Supply Chains). It utilizes Large Language models (LLMs) for automated risk identification and assessment of risks impacting on global supply chain and integrates the Cambridge Taxonomy of Business Risks (CTBR) to categorize risk events. Additionally, it employs Neo4j, a graph database, for dynamic risk visualization. this integrated approach aims to upgrade traditional Supply Chain Risk Management (SCRM) processes by providing supply chain professionals with an intuitive, interactive tool to manage risks more effectively, thereby increasing resilience and adaptability in complex supply environments. A case study testing the functionality and impact of the prototype in a real-world scenario with Apple Inc. demonstrates its efficacy, showing a significant improvement in risk detection and management efficiency. © 2024 IEEE.

关键词： Data Visualization Large Language models predictive Analytics in Supply Chain Supply Chain Risk Management

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Improving Performance and Explainability of Sensor-Based Human Activity Recognition 9

Improving Performance and Explainability of Sensor-Based Hum...

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Joint 9th international conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Significant progress has been made in developing systems that can automatically recognize human activities, largely thanks to deep learning models and sensor data. However, high performance and comprehensibility are often at odds in these systems. the aim of this research is to enhance both the accuracy and understandability of models that use sensors for human activity recognition (HAR). We present a new convolutional neural network design which takes input from accelerometers and gyroscopes across multiple data streams. this allows efficient capture of time and frequency characteristics. Additionally, we put forward an attention module that identifies informative patterns in the sensor signals to shed light on the model's predictions. Experiments were conducted on a benchmark dataset of common daily movements gathered from ubiquitous sensors. Our model achieves state-of-the-art accuracy exceeding prior deep learning techniques. the attention visuals provide transparency into which sensor segments are most relevant for accurate activity classification. this validates improved model interpretability. Our multi-channel architecture pushes sensor-based HAR forward through heightened predictive performance. We offer original perspectives to create reliable, precise systems for HAR. © 2024 IEEE.

关键词： Wearable sensors

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ChatGPT assisted Raft Based Consensus Algorithm and software Systems for Preventing Financial Accounting Fraud 9

ChatGPT assisted Raft Based Consensus Algorithm and Software...

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

作者： Sane, Parth Eviden Pvt. Ltd Software Engineering Pune India

ISBN: (纸本)9798350394474

Double entry bookkeeping is a widely used accounting method that provides a system for recording financial transactions. Recently, this method has been applied to the field of distributed consensus algorithms, particularly the raft consensus algorithm. Raft [1] is an innovative algorithm that is used to achieve consensus in a distributed system. the application of double entry bookkeeping to this algorithm aims to provide a more efficient and reliable method for tracking transactions and ensuring consistency. the use of double entry bookkeeping allows for improved fault tolerance, as well as better detection and prevention of data inconsistencies. Additionally, it enables the detection of malicious activities, such as double-spending or tampering with the transaction history. this application of double entry bookkeeping to raft consensus algorithm shows promise for improving the overall security and reliability of distributed systems. this paper presents Raft's application to distributed accounting ledgers with double entry accounting for a more transparent and fraud resistant transaction system. Double entry accounting is essential to modern commerce to rationalize transactions and prevent fraud. Using this concept of double entry with consensus can be used for preventing financial fraud. the killer 'feature' in this paper is also the process of comparing research that is envisioned by the author and further improved or enhanced by AI or more commonly known language models. In this case this language model product is ChatGPT [2] by the OpenAI Research and Development Organization. We will also compare the results to get interesting insights on generated results. © 2024 IEEE.

关键词： Fault tolerance

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Evaluating LLM's Code Reading Abilities in Big Data Contexts using Metamorphic Testing 9

Evaluating LLM's Code Reading Abilities in Big Data Contexts...

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9th international conference on Big Data and Information Analytics, BigDIA 2023

作者： Li, Ziyu Li, Zhendu Xiao, Kaiming Li, Xuan University of Sheffield United Kingdom National University of Defense Technology College of Systems Engineering China National University of Defense Technology Laboratory for Big Data and Decision China

ISBN: (纸本)9798350330076

With the explosive growth of Big Data, understanding complex data-centering algorithms and software has become essential. Large Language models (LLMs) especially ChatGPT models have been increasingly deployed in Big Data environments to improve workflow in various tasks, including code reading. the current testing method on LLMs' code reading ability focuses more on code structural understanding and sentiment understanding, all tests are conducted on different prompts with different assumptions. this paper analyzes current LLM code-reading testing methods and presents an innovative evaluation of Metamorphic Testing to evaluate LLMs' code-reading abilities. We proposed two new metamorphic relations specified f or code reading challenges and evaluated the ChatGPT-3.5 on its code understanding capabilities. Our study offers insights into LLMs' capabilities to correctly understand diverse code bases and maintain validity. © 2023 IEEE.

关键词： Big data

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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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DPCNN-based models for Text Classification 10

DPCNN-based Models for Text Classification

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10th IEEE international conference on Cyber Security and Cloud Computing and 9th IEEE international conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, MeiJiao Pang, Jiacheng Cai, Jiahong Huo, Yingzi Yang, Ce Xiong, Huixuan QuanZhou University College of Information Engineering Software QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

In recent years, with the wide application of CNN in the field of deep learning, the related model of CNN, the Deep Pyramid Convolutional Neural Networks for Text Categorization (DPCNN) model, has emerged, and by the idea of deepening the depth of the network to obtain the best accuracy, DPCNN has made breakthroughs in related fields, especially in the field of text categorization, and its concrete applications in solving practical problems have achieved good results. this paper first introduces the text classification system, then introduces the mainstream model CNN for text classification, after that this paper focuses on the analysis of the DPCNN model, introduces its background and its principle analysis, and introduces the application of DPCNN in specific examples, and finally summarizes and outlooks on DPCNN, emphasizes its application advantages and builds suitable application scenarios. © 2023 IEEE.

关键词： Convolutional neural networks

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Development of Multiphysics models for the Study of Airflow and thermal Effects During the Use of Filtering Facepiece Respirators 9

Development of Multiphysics Models for the Study of Airflow ...

引用

9th international conference on Computer Science and engineering, UBMK 2024

作者： Topilko, Barthelemy Marchais, Geoffrey Arbane, Mohamed Brousseau, Jean Bahloul, Ali Maldague, Xavier Brochot, Clothilde Yaddaden, Yacine Université du Québec a Rimouski Rimouski Canada Institut de recherche Rebert-Sauvé en santé et en Séecurité du travail Montréal Canada Université Laval QC Canada

ISBN: (纸本)9798350365887

To provide good protection, a Filtering Facepice Respirator (FFR) must be properly adjusted to ensure a satisfactory seal against the external working environment. None of the tests currently in use can accurately locate potential air leaks and determine a fit factor. T he aim o f t he project is to develop an analysis station for assessing these quantities using infrared imaging methods. the development of a multiphysics model of the different situation will become a tool for better understanding phenomena by simulating situations that are difficult t o verify experimentally. this paper proposes the development of multiple multiphysics models using COMSOL Multiphysics® software to simulate the thermal effects of air leaks during the use of FFR. the construction of various geometries, transitioning from a simple 2D model to a 3D model closely resembling real-life situations, is detailed as follows. the implementation of diverse physics and meshing techniques related to the problem, including thermal dynamics and fluid mechanics h as been thoroughly studied. A pivotal aspect considered in this context is the respiratory cycle. the preliminary results show that 1) the impact of humidity on heat exchange is low and negligible to a first approximation, 2) the variation of the flow rate at the leak is linked to its size, and, in particular, 3) the temperature evolution in zones proximate to the leaks as calculated by the model are compared with the experimental results obtained with an infrared camera. As part of a broader project, these models will be instrumental in comparing and validating forthcoming experimental results. the simulation results obtained will also be used to feed a database that will be used in artificial intelligence model asp art o f t he development of the analysis station. © 2024 IEEE.

关键词： Industrial hygiene

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Nonlinear restoring force modelling using Gaussian processes and model predictive control 30

Nonlinear restoring force modelling using Gaussian processes...

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30th international conference on Noise and Vibration engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Floren, M. Noël, J.-P. KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium

ISBN: (纸本)9789082893151

Nonlinear mechanical systems can often be represented as linear time-invariant systems with a static, typically unknown, nonlinear function in the feedback path. the aim of this paper is to develop an effective framework that (i) recovers and (ii) models the nonlinearity, whilst exploiting minimal prior system knowledge. Model predictive control is used as a data processing step to recover the nonlinearity in the time domain, after which its functional form is modelled as a Gaussian process. the efficacy of the proposed method is demonstrated both in simulation, and on the Silverbox benchmark system. the overall methodology shows a high degree of performance and, despite the data-driven nature of the approach, high physical interpretability. © 2022 proceedings of ISMA 2022 - international conference on Noise and Vibration engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Model predictive control

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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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models Are Codes: Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs 24

Models Are Codes: Towards Measuring Malicious Code Poisoning...

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39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Zhao, Jian Wang, Shenao Zhao, Yanjie Hou, Xinyi Wang, Kailong Gao, Peiming Zhang, Yuanchao Wei, Chen Wang, Haoyu Huazhong Univ Sci & Technol Hubei Key Lab Distributed Syst Secur Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan Peoples R China Ant Grp MYbank Hangzhou Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

the proliferation of pre-trained models (PTMs) and datasets has led to the emergence of centralized model hubs like Hugging Face, which facilitate collaborative development and reuse. However, recent security reports have uncovered vulnerabilities and instances of malicious attacks within these platforms, highlighting growing security concerns. this paper presents the first systematic study of malicious code poisoning attacks on pre-trained model hubs, focusing on the Hugging Face platform. We conduct a comprehensive threat analysis, develop a taxonomy of model formats, and perform root cause analysis of vulnerable formats. While existing tools like Fickling and modelscan offer some protection, they face limitations in semantic-level analysis and comprehensive threat detection. To address these challenges, we propose MalHug, an end-to-end pipeline tailored for Hugging Face that combines dataset loading script extraction, model deserialization, in-depth taint analysis, and heuristic pattern matching to detect and classify malicious code poisoning attacks in datasets and models. In collaboration with Ant Group, a leading financial technology company, we have implemented and deployed MalHug on a mirrored Hugging Face instance within their infrastructure, where it has been operational for over three months. During this period, MalHug has monitored more than 705K models and 176K datasets, uncovering 91 malicious models and 9 malicious dataset loading scripts. these findings reveal a range of security threats, including reverse shell, browser credential theft, and system reconnaissance. this work not only bridges a critical gap in understanding the security of the PTM supply chain but also provides a practical, industry-tested solution for enhancing the security of pre-trained model hubs.

关键词： Pre-trained Model Hub Code Poisoning Attacks LLM Supply Chain

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