检索结果-内蒙古大学图书馆

DeepVec: State-Vector Aware Test Case Selection for Enhancing Recurrent Neural Network

IEEE Transactions on software engineering 2025年

作者： Jiang, Zhonghao Yan, Meng Huang, Li Sun, Weifeng Liu, Chao Sun, Song Lo, David Chongqing University School of Big Data and Software Engineering China Chongqing Normal University School of Computer and Information Science China Singapore Management University School of Information Systems Singapore

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test cases without compromising testing performance. Test case selection based on neuron coverage (NC) or uncertainty metrics has achieved significant success in Convolutional Neural Networks (CNN) testing. However, it is challenging to transfer these methods to Recurrent Neural Networks (RNN), which excel at text tasks, due to the mismatch in model output formats and the reliance on image-specific characteristics. What’s more, balancing the execution cost and performance of the algorithm is also indispensable. In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces the cost of data labeling and saves computing resources and balances the execution cost and performance. DeepVec selects data using uncertainty metric based on the norm of the output vector at each time step (i.e., state-vector), and similarity metric based on the direction angle of the state-vector. Because test cases with smaller state-vector norms often possess greater information entropy and similar changes of state-vector direction angle indicate similar RNN internal states. These metrics can be calculated with just a single inference, which gives it strong bug detection and model improvement capabilities. We evaluate DeepVec on five popular datasets, containing images and texts as well as commonl

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

F2NAS: Flexible Federated Neural Architecture Search in Green Edge Computing

F2NAS: Flexible Federated Neural Architecture Search in Gree...

引用

IEEE International Conference on Communications (ICC)

作者： Zebo Zhao Chao Qiu Yunfeng Zhao Xiaofei Wang Haipeng Yao Xiuhua Li F. Richard Yu College of Intelligence and Computing Tianjin University Tianjin China Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The rapid growth of edge computing calls for fine-tuned deep neural network (DNN) deployment that emphasizes energy-efficient implementation, due to the resource constraints of edge devices. Traditional Federated Learning-based Neural Architecture Search (FL-based NAS) has been instrumental in the complexities of this deployment, particularly in addressing constraints posed by device heterogeneity, limited resources, and privacy preservation. However, it is hindered by issues such as suboptimal aggregation of homogeneous neural blocks, significant knowledge waste in disregarding heterogeneous neural blocks, and excessive communication energy consumption. This paper introduces F 2 NAS in green edge computing, a novel energy-efficient approach that addresses these limitations by ensuring flexible and energy-efficient model design and training for edge devices. Firstly, F 2 NAS introduces an innovative aggregation strategy that enhances the integration of homogeneous neural blocks by using inter-block distances to optimize weight allocation. Further, it employs a unique parameter extraction technique that recaptures valuable insights from previously overlooked heterogeneous neural blocks. Finally, F 2 NAS meticulously calibrates communication energy consumption by balancing loss function and model interaction, setting and refining an upper limit for model communication. Experimental results reveal F 2 NAS enhances model accuracy by 2.8% to 4.7%, simultaneously reducing the energy consumption by nearly 50% through optimizing the communication cost.

关键词： Training Energy consumption Privacy Accuracy Computational modeling Refining Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Eliciting Requirements from Stakeholders’ Responses Using Natural Language Processing

引用

Computer Modeling in engineering & Sciences 2021年第4期127卷 99-116页

作者： Mohammed Lafi Bilal Hawashin Shadi AlZu’bi Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Information Systems Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan

Most software systems have different stakeholders with a variety of *** process of collecting requirements from a large number of stakeholders is vital but *** propose an efficient,automatic approach to collecting requirements from different stakeholders’responses to a specific *** use natural language processing techniques to get the stakeholder response that represents most other stakeholders’*** study improves existing practices in three ways:Firstly,it reduces the human effort needed to collect the requirements;secondly,it reduces the time required to carry out this task with a large number of stakeholders;thirdly,it underlines the importance of using of data mining techniques in various software engineering *** approach uses tokenization,stop word removal,and word lemmatization to create a list of frequently accruing *** then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest *** experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.

关键词： software requirements requirements elicitation natural language processing

来源：评论

学校读者我要写书评

暂无评论

Faster R-CNN assessment for air bubbles detection in the conformal coating application

Faster R-CNN assessment for air bubbles detection in the con...

引用

2021 International Conference on Microelectronics, ICM 2021

作者： Zouhri, Nizar El Mourabit, Aimad El Abidine, Alaoui Ismaili Zine Ensa. Abdelmalek Essaâdi University System Data Engineering Team Tangier Morocco University Mohammed v Information Communication and Embedded Systems Team Rabat Morocco

ISBN: (纸本)9781665408394

The detection of defects in Printed Circuit Boards (PCB) during the assembly process is an important quality requirement for the electronic *** present paper emphasizes the assessment of computer vision implying Faster-RCNN object detection architecture, with the aim of air bubbles defects detection in PCB following the conformal coating *** image configurations have been used to increase artificially the training model's performance, such as air bubbles size, location (on a flat PCB's surface, between components leads) and illumination, *** reaching this cap, we used a random pattern of choice regarding the images with different configurations to properly evaluate its performance, especially the accuracy, precision and *** results showed that Faster-RCNN delivers the lowest detection performance for the air bubbles located between components leads compared to the ones located on flat PCB *** paper shows the accuracy, precision and sensitivity performance results by applying the Faster-RCNN object detection architecture for air bubbles defect detection in the conformal coating application, with aim of providing reference for future research in the field of air bubbles detection. © 2021 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Uzbek traffic sign dataset for traffic sign detection and recognition systems

Uzbek traffic sign dataset for traffic sign detection and re...

引用

2022 International Conference on information Science and Communications Technologies, ICISCT 2022

作者： Khamdamov, Utkir Umarov, Mukhriddin Elov, Jamshid Khalilov, Sirojiddin Narzullayev, Inomjon Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Samarkand Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Software Engineering Samarkand Uzbekistan Nurafshan Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Information Technologies Tashkent Uzbekistan

ISBN: (纸本)9781665472296

Currently, deep learning algorithms are developing in machine learning and deep neural networks. However, deep learning algorithms still have pressing problems to solve. That is, the data set is insufficient and of low quality for machine learning. One of the ways to solve this problem is to data augmentation the dataset. There are two types of image data sets: labeled data sets and unlabeled data sets. These annotations are important for object recognition. Deep learning algorithms typically use an annotated dataset. Similarly, the You Only Look Once (YOLOv5) network model also requires an annotated dataset. In this article, we manually created annotations for each image using the labelImg software tool. Different countries have different Traffic signs. The dataset of traffic signs of Uzbekistan (UTSD) has not yet been developed. Therefore, in this work, we developed a UTSD dataset for use in a traffic sign detection and recognition system (TSDR). The UTSD dataset contains 3957 Traffic sign images belonging to 56 classes. We improved the UTSD dataset by data augmentation. © 2022 IEEE.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

Overview and Assessment Approaches of Energy Consumption and GHG Emissions Through the Development and Application of Artificial Intelligence - An Example from the Agricultural Sector

Overview and Assessment Approaches of Energy Consumption and...

引用

2024 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2024

作者： Rilling, Stefan Henningsen, Jens Milde, Katharina Martini, Daniel Wickert, Lorenz Kelbert, Patricia Fraunhofer Institute for Intelligent Analysis and Information Systems Iais Adaptive Reflective Teams Sankt Augustin Germany Fraunhofer Institute for Experimental Software Engineering Iese Data Science Kaiserslautern Germany Advisory Board for Technology and Construction in Agriculture Ktbl Team Digital Technologies Darmstadt Germany

ISBN: (纸本)9798350355444

This article looks at the sustainability of various artificial intelligence (AI) methods. In recent months, AI methods have received a great deal of public attention and the benefits for sustainability have been emphasised. However, the ecological impacts of AI methods must also be considered in this debate. This article presents an overview of various approaches to sustainability assessment and addresses issues relating to energy consumption and the associated Carbon dioxide (CO2) emissions, and a Life Cycle Assessment (LCA) calculation for a concrete agricultural AI-use case. © 2024 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Overview and Assessment Approaches of Energy Consumption and GHG Emissions Through the Development and Application of Artificial Intelligence - An Example from the Agricultural Sector

Overview and Assessment Approaches of Energy Consumption and...

引用

IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Stefan Rilling Jens Henningsen Katharina Milde Daniel Martini Lorenz Wickert Patricia Kelbert Adaptive Reflective Teams Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Sankt Augustin Germany Data Science Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany Team Digital Technologies Advisory Board for Technology and Construction in Agriculture KTBL Darmstadt Germany

ISBN: (数字)9798350355444

ISBN: (纸本)9798350355451

关键词： Life cycle assessment Energy consumption Conferences Forestry Carbon dioxide Metrology Agriculture Artificial intelligence Sustainable development Carbon emissions

来源：评论

学校读者我要写书评

暂无评论

Towards Evidence-Based Cybersecurity Assessment of Programmable systems to Ensure the Protection of Critical IT Infrastructure 12

Towards Evidence-Based Cybersecurity Assessment of Programma...

引用

12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Illiashenko, Oleg Kharchenko, Vyacheslav Odarushchenko, Oleg National Aerospace University 'KhAI' Department of Computer Systems and Networks Kharkiv61070 Ukraine Institute of Information Science and Technologies 'Alessandro Faedo' - Isti Cnr Software Engineering & Dependable Computing Laboratory Pisa56124 Italy Research and Production Enterprise Rpc Radics Llc Kropyvnytskyi25009 Ukraine

ISBN: (纸本)9798350358056

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature - physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems. © 2023 IEEE.

关键词： critical IT infrastructure cybersecurity FPGA functional safety programmable systems of critical applications

来源：评论

学校读者我要写书评

暂无评论

Prediction of Dental Fluorosis Infection based on Particle Optimized Scored KNN Approach 1

Prediction of Dental Fluorosis Infection based on Particle O...

引用

1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Montasser, Reem Kadry Ismael, Osama El-Kholy, Moataz October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt October University for Modern Sciences and Arts Faculty of Dentistry Oral Pathology Department Giza Egypt

ISBN: (纸本)9798350335569

Due to its simplicity of usage across a variety of applications, the K-Nearest Neighbor algorithm is usually utilized as a classification approach. The K-Nearest Neighbor algorithm's accuracy is greatly impacted by the occurrence of multidimensional and outlier data. This research uses a novel hybrid technique known as Particle Optimized Scored K-Nearest Neighbor (POSKNN) to build a model to predict the emergence of dental fluorosis. The proposed novel approach is implemented in two phases: the first phase helps in the resolution of multidimensional data by performing selection of feature using the Modified Particle Swarm Optimization algorithm, and the second phase helps in the resolution of the presence of outliers by making use of the results of the first phase and applying the newly proposed scored KNN technique to them. The suggested approach was tested using a real-world dataset acquired from three dentistry clinics in Egypt's El Baharia Oasis, which has 56 attributes, two classes, and 328 records. 10-fold cross validation was implemented to the model. The experimental evaluation was divided into two parts;evaluation for modified particle swarm optimization, evaluation for scored K nearest neighbor, and the hybrid particle optimized scored K nearest neighbor. The experiment's results demonstrated that the suggested approach outperformed the traditional KNN and the modified KNN. POSKNN achieved an accuracy of 94.5% when K value is 11. © 2023 IEEE.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Unmasking Fake News with Emotional Cues: A Comparative Study of Sentiment Reasoning Models

Unmasking Fake News with Emotional Cues: A Comparative Study...

引用

Advanced Research in Computing (ICARC), International Conference on

作者： Vadivel Abishethvarman Kuhaneswaran Banujan B.T.G.S Kumara Nirubikaa Ravikumar Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Australia Department of Software Engineering Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

Discerning truth from deception requires innovative tools in an era besieged by misinformation. Fake news is a form of misleading information delivered as reliable news. It is mainly made up of intentionally manipulating public opinion, decision bias, and exploiting general credulity. Various supervised learning-based fake news detection and classifications only focus on the semantic values due to their subjectivity in the current context. Sentiment analysis is also a common interpretation mechanism of information mining. This research explores the role of sentiment analysis in the context of fake news detection. Sentiment analysis, a facet of natural language processing, delves into the nuanced sentiments within the text, unveiling hidden narratives and differentiating between authentic reporting and carefully crafted deception. The study employs various sentiment reasoning models, including VADER, TextBlob, and Roberta, focusing on comparative analysis. The VADER model, complemented by a Bag of Words approach, emerges as the most effective, outperforming, compared to TextBlob and RoBERTa. Especially BERT sentiment reasoner model underperformed with uneven distribution in this fake news domain. The best VADER sentiments are taken for the transitions of sequential neural network architectures such as Simple Neural Network (SNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) featuring Long Short-Term Memory (LSTM). The LSTM model captures intricate patterns, dependencies, and contextual information. Evaluation metrics, encompassing accuracy, precision, recall, and F1 score, provide a comprehensive understanding of models' performance. The RNN LSTM performed well compared to the other sequential models. It primely emphasised the multifaceted landscape of sentiment analysis, presenting a robust strategy for combating the proliferation of fake news in an information-abundant age. Combining Sentiment Reasoning in VADER and RNN LSTM presents a rob

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：