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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Deng, Shaojun Liu, Wanwei Zhang, Miaomiao College of Computer Science and Technology National University of Defense Technology Changsha China School of Software Engineering Tongji Uniersity Shanghai China

ISBN: (纸本)9789819606016

Neural-symbolic systems (NSSs), which are typically cyber-physical systems integrated with artificial intelligence modules, have received much attention in both academic and industrial fields. However, thorough verification (such as model checking) of such systems in general leads to prohibitively high costs due to the scale of the state space, and this makes it barely accomplishable within desired time-span. Consequently, light-weight verification techniques are introduced to deal with such cases, e.g., runtime verification (RV, for short). In this paper, we investigate an RV framework for NSSs against signal temporal logic (STL) properties. To guarantee that the on-line monitoring could be accomplished in time, we utilize Euler-prediction to sample the system under scrutiny at discrete time-steps. Our framework supports both qualitative and quantitative on-line monitoring of STL specifications, and thus it terminates whenever a property is satisfied and/or violated by a finite prefix of the trajectory, and it can also provide the bounds of the quantitative satisfaction/violation w.r.t. the remaining execution. Notably, for the so-called piece-wise linear NSSs against affine STL formulas, our approach is exact. On top of it, a prototype tool is implemented, and is experimentally evaluated. The results demonstrate the feasibility of the presented runtime verification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Model checking

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Web applications testing techniques: a systematic mapping study

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International Journal of Web engineering and technology 2023年第4期17卷 372-412页

作者： Hanna, Samer Ahmad, Amro Al-Said Department of Software Engineering Faculty of Information Technology Philadelphia University Jordan School of Computing and Mathematics University of Keele United Kingdom

Due to the importance of web application testing techniques for detecting faults and assessing quality attributes, many research papers were published in this field. For this reason, it became essential to analyse, classify and summarise the research in the field. To achieve this goal, this research conducted a systematic mapping study on 98 research papers in the field of web applications testing published between 2008 and 2021. The results showed that the most commonly used web applications testing techniques in literature are model-based testing and security testing. Besides, the most commonly used models in model-based testing are finite-state machines. The most targeted vulnerability in security testing is SQL injection. Test automation is the most targeted testing goal in both model-based and security testing. For other web applications testing techniques, the main goals of testing were test automation, test coverage, and assessing security quality attributes. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Mapping

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Improved the Buddy System to Reduce the System Fargment Created 3

Improved the Buddy System to Reduce the System Fargment Crea...

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3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022

作者： Zhao, Wenbo Software Engineering Taiyuan University of Technology Shanxi Taiyuan030009 China

ISBN: (纸本)9781510664814

In the OS Kernal, how to allocate disk space is a difficult question. Buddy system is one of important way in various allocating disk space way. We can reduce the fragmentation by Buddy System. However, Buddy System cannot avoid the fragmentation created because the logic in algorithm. How to improve the algorithm is a important way to reduce the fargmentation created and increase the life about system. Because of the Buddy System always merge and split the memory page, fragmenation is created in the process and accumulate in system. With the passage of time, the space which can be used by system will less and less in disk. If we can reduce the number of times of the memory page's merged and spilted in the process of system in used, the fargmentation will be reduce in the system used. © 2023 SPIE.

关键词： Linux

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Recommendation systems-based software requirements elicitation process—a systematic literature review

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Journal of engineering and Applied Science 2024年第1期71卷 29页

作者： Akram, Faiz Ahmad, Tanvir Sadiq, Mohd. Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi110025 India Software Engineering Lab. Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions. © 2024, The Author(s).

关键词： Recommender systems

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Multi-scale ultrasound image denoising algorithm based on deep learning model for super-resolution reconstruction 23

Multi-scale ultrasound image denoising algorithm based on de...

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2023 4th International Conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Gan, Jianhong Wang, Liping Liu, Zhiming Wang, Jiayun School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798400708190

Effective suppression of speckle noise in ultrasound images is of great significance for improving image quality and clinical diagnostic analysis. The traditional edge enhancement nonlinear coherent diffusion (EENCD) and multi-scale filtering based on pyramid transformation are not obvious enough for the subtle features after the image is denoised. Since the super-resolution deep learning model SRGAN can restore more realistic texture details of the image, this attempt is made to improve the traditional Laplace pyramid, and the 2x magnification factor reconstruction image of the SRGAN model is used to replace the upsampling of the Laplace pyramid reconstruction stage, and the EENCD method is still used on the Laplace pyramid to suppress the image speckle. By comparing this method with the traditional method in the simulation image and the real ultrasound image, the experimental results show that the proposed method is superior to the original algorithm in terms of structure, edge protection and noise suppression. © 2023 ACM.

关键词： Speckle

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AROhI: An Interactive Toolkit for Estimating ROI of Data Analytics 15

AROhI: An Interactive Toolkit for Estimating ROI of Data Ana...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Zambare, Noopur Idoko, Jacob Acharya, Jagrit Ginde, Gouri Indian Institute of Technology Dept. of Mechanical Engineering Jodhpur India University of Calgary Dept. of Electrical and Software Engineering Canada

ISBN: (纸本)9798331507695

The cost of adopting new technology is rarely analyzed and discussed;however, it is vital for any technological investment because of the cost and effort associated with it. Thus, it is crucial to consider Return On Investment (ROI) when performing data analytics. Decisions on 'How much analytics is needed?' are hard to answer. ROI could guide decision support on the What?, How?, and How Much? Analytics for a given problem. This work details a comprehensive tool that provides conventional and advanced ML approaches for demonstration using software requirements dependency extraction and their ROI analysis as a use case. Utilizing advanced ML techniques such as Active Learning, Transfer Learning and primitive Large language model: BERT (Bidirectional Encoder Representations from Transformers) as its various components for automating dependency extraction, the tool outcomes demonstrate a mechanism to compute the ROI of ML algorithms to present trade-offs between the cost and benefits of a technology investment. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on Chemical Accident News Classification Based on CNN and BiLSTM-Self-Attention

Research on Chemical Accident News Classification Based on C...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zong, Hui Zhao, Yun Zhou, Heng Zhang, Le Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Chemical accident news data encompasses essential information such as news headlines, news content, and news sources, with the context of news content playing a crucial role. To enhance the accuracy of text feature extraction and improve the efficiency of chemical accident news classification, this paper introduces a feature fusion-based classification approach. The proposed model employs a multi-layer convolutional neural network (CNN) to extract local features from the text of chemical accident news. Additionally, a Bidirectional Long Short-Term Memory (BiLSTM) network is utilized to capture global features, supplemented by the integration of a Self-Attention mechanism behind the BiLSTM network to assign weights to the features and reduce noise. The local and global features are then fused to enrich the semantic information. Furthermore, the feature fusion information undergoes maximum pooling and average pooling to reduce dimensionality and enhance the training speed. Finally, the information is fed into a Softmax layer for classification. Experimental results demonstrate that the proposed neural network model, namely ABLSACNN (Add-CNN-BiLSTM-Self-Attention), outperforms the CNN-Self-Attention model. The ABLSACNN model exhibits an improvement of1.59% in accuracy, 2.46% in recall rate, and 1.93% in F1 score on the chemical accident news dataset, thereby showcasing its superiority. © 2023 IEEE.

关键词： Convolutional neural networks

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Retinal Vessel Image Segmentation based on Attention Mechanism and U-net Model 5

Retinal Vessel Image Segmentation based on Attention Mechani...

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5th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2023

作者： Shen, Xueli Wang, Kunpeng Liaoning University of Engineering Technology Software Academy Liaoning Huludao China

ISBN: (纸本)9798350333442

A new retinal blood vessel segmentation method combining SimAM (a simple, parameter free attention module) and U-net model based on encoding decoding structure was proposed. A new non-parametric attention mechanism SimAM is introduced in the sampling process of U-net model. A network model is built through training, and test samples are segmented using the trained network model. The experimental results show that this deep learning method can effectively improve the accuracy and performance of retinal vessel segmentation. © 2023 IEEE.

关键词： Image segmentation

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Air Quality Index Prediction Based on CNN-LSTM-Attention Hybrid Modeling

Air Quality Index Prediction Based on CNN-LSTM-Attention Hyb...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Li, Yinyin Jiang, Tao Gu, Hanzhu Lu, Weibing Wu, Qingguo Yu, Yongtao Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Air Quality Index (AQI) is an important indicator for determining good or bad air quality. The accurate and efficient prediction of AQI plays a positive role in promoting the management of air pollution. However, current algorithms for predicting AQI usually do not comprehensively consider the effects of pollutant factors and meteorological factors on the prediction performance. Therefore, taking pollutant factors and meteorological factors as the basis of the model study, a CNNLSTM-Attention hybrid model is proposed. The CNN-LSTM module is used to obtain the air quality-related features, and the attention mechanism is introduced to weigh and sum the output of the LSTM in order to obtain the final attention-weighted features. The results show that the model has better performance than the single model for the prediction of the air quality index. © 2023 IEEE.

关键词： Air quality

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Joint classification of hyperspectral and lidar using multi-scale spatial adaptive calibration 6

Joint classification of hyperspectral and lidar using multi-...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Yang, Zhichao Hao, Siyuan Qingdao University of Technology College of information and control engineering Qingdao China Beijing Jiaotong University College of software engineering Beijing China

ISBN: (纸本)9781510688018

In the field of land cover classification, the multimodal fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data has become a key research direction in remote sensing. Hyperspectral imagery provides rich spectral information, enabling the identification of subtle material differences, while LiDAR data offers high-precision spatial geometric structures. However, the complementarity between these two data modalities faces challenges related to spatial resolution discrepancies and feature inconsistency. To address these challenges, this paper proposes a Multiscale Spatial Adaptive Calibration Network (MSACNet) for joint classification of hyperspectral and LiDAR data. The network extracts multiscale convolutional features and introduces a Spatial Adaptive Calibration Module (SACM) at each scale to adaptively calibrate the fused multimodal features, adjusting for discrepancies between spectral and spatial geometric feature distributions. Experimental results on the Houston2013 dataset demonstrate that the proposed method achieves superior classification performance compared to existing classification models. © 2025 SPIE.

关键词： Image resolution

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