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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Gulsoy, Muhammed Kokach, Vael Kocaçınar, Büşra Akbulut, Fatma Patlar Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Software Engineering Istanbul Kültür University Istanbul Turkey

ISBN: (纸本)9798350379433

This study proposes an AI-driven framework for generating coherent narratives and corresponding visuals tailored for children's stories. We produce textual material by using Long Short-Term Memory (LSTM) models, and visually appealing images by using Generative Adversarial Networks (GANs) and fine-tuned Stable Diffusion Models. Furthermore, we investigate various prefix-based story starting points to increase the variety and interest of the story. Our method includes a significant innovation: the Stable Diffusion model is fine-tuned for image generation, enabling the production of high-quality, contextually relevant images from particular story segments. We show by experiments that the Stable Diffusion model can be fine-tuned to greatly improve the quality and relevance of the produced images, and that the layered LSTM model with extended training produces the best text generation performance. Using these advanced models together improved the storytelling experience by fusing imaginative AI with rich, narrative-driven imagery. © 2024 IEEE.

关键词： Generative adversarial networks

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Sparse and Dense: Learning Confusion Representation Network for 3-D Action Recognition

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IEEE Internet of Things Journal 2025年第12期12卷 21240-21254页

作者： Hou, Xinran Geng, Pei Li, Tianchen Li, Yalin Li, Yan Lyu, Lei Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Management University School of Information Engineering Jinan250100 China Shandong Normal University School of Information Science and Engineering Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

In the field of the Internet of Medical Things (IoMT), the demand for Human action recognition (HAR) is growing. Due to the limitations of portability and privacy of traditional sensors, many endeavors have made significant progress in 3-D skeleton-based action recognition. However, existing methods ignore potential higher-order semantic information between joints and fail to perceive rapidly changing dynamic details, resulting in frequent confusion of actions with similar motion trajectories. To alleviate this issue, we propose a novel learning confusion representation network (LCR-Net). Specifically, a progressive feature enhancement module is first designed to utilize self-attention to gradually aggregate lower-order features to higher-order features, emphasizing the relative movement between body parts. Second, we design the enhanced spatio-temporal convolution to explore the potential spatio-temporal dependencies between joints by adding a mask matrix and an attention fusion mechanism. To further perceive the spatio-temporal relationships in subtle changes, we divide the interactive sparse-dense pathways at different spatio-temporal resolutions and enhance the complementary information between the two pathways through feature interaction. Finally, the frequency excitation learning module is proposed to efficiently learn the importance of different frequencies by cross-channel modeling, promoting the compactness of actions within classes and the separability of confusion actions. In addition, the lightweight LCR-Net+ is achieved through model compression optimization to meet the deployment requirements of IoT systems. Comprehensive experiments conducted on three public datasets (NTU-RGB+D60&120, NW-UCLA) demonstrate the superior performance of our model. © 2014 IEEE.

关键词： Joints (anatomy)

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A new fully convolutional neural network architecture with text kernel for offline handwritten chinese text recognition 3

A new fully convolutional neural network architecture with t...

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Li, Yuanyuan Yang, Xinguang Ren, Zhiwei School of Software Dalian Jiaotong University Dalian China School of Computer and Communication Engineering Dalian Jiaotong University Dalian China

ISBN: (纸本)9781510680531

It’s challenging to optimize offline handwritten Chinese recognition as a topic of pattern recognition as the consequence of the complex structures of Chinese characters. To solve this problem, many data pre-processing methods and neural network architectures have been optimized to improve the recognition accuracy. To this end, we propose a fully convolutional neural network architecture with text kernel for offline handwritten Chinese text recognition in this paper. To make the features of the image clearer, the text kernel which can align the characters of handwritten Chinese text is implemented. This novel fully convolutional neural network captures the feature maps in two branch and promote the efficiency of neural networks. Experimenting on the CASIA-HWDB 2.0-2.2 datasets, our proposed method achieves a competitive performance and the accuracy achieves 97.87% with language model. © 2024 SPIE.

关键词： Convolutional neural networks

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Optimization of Lane-changing Advisory in Mixed Traffic of Connected Vehicles and Human-driven Vehicles at Expressway Bottlenecks

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-10页

作者： Lin, Jingmin Yu, Chunhui Wang, Ling Liu, Guanjun Wang, Jiacun Ma, Wanjing Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai P.R. China Department of Computer Science and Technology Tongji University Shanghai P.R. China Computer Science and Software Engineering Department Monmouth University USA

Lane closure due to events such as accidents creates bottlenecks on expressways. The mandatory lane changes of merging vehicles lead to congestion. As the cyber-physical system (CPS) develops, mixed traffic consisting of connected vehicles (CVs) and human-driven vehicles (HVs) has emerged. CVs can receive lane-changing (LC) advisories and complete merging to alleviate congestion. Most existing studies assume CVs can timely and exactly follow LC advisories, which is not realistic with human drivers. This study develops an LC advisory model for CVs, whose response time and compliance degrees are considered, at an expressway bottleneck with lane closure under mixed traffic of CVs and HVs. The LC strategies are optimized for CVs to minimize the total delay of CVs and HVs approaching the bottleneck. The constraints include the domains of decision variables, vehicle passing states at the bottleneck, vehicle kinematics, implementation of LC advisories, LC safety, the maximum number of LC manoeuvres, the minimum time interval between LC advisories, the potential LC CVs, and the evolution of vehicle states. The simulation-based method is applied to predict vehicle delay, which is formulated as an implicit function of the LC strategies for CVs. Genetic Algorithm (GA) is designed for solutions. The numerical studies validate the advantages of the proposed model. The sensitivity analysis shows that: 1) the critical CV penetration rate is 60%, below which the marginal benefits are significant with increasing CV penetration rates;and 2) the consideration of the response time and the compliance degree of CVs makes a great difference. IEEE

关键词： Vehicles

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Towards 360∘ image compression for machines via modulating pixel significance

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Multimedia Tools and Applications 2024年第42期83卷 90271-90288页

作者： Zheng, Silin Shen, Xuelin Zhang, Qiudan Chen, Zhuo Yang, Wenhan Wang, Xu College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen51800 China Guangdong Shenzhen51800 China Peng Cheng Laboratory Guangdong Shenzhen51800 China

The rapid growth of computer vision-based applications, including smart cities and autonomous driving, has created a pressing demand for efficient 360∘ image compression and computer vision analytics. In most circumstances, 360∘ image compression and computer vision face challenges arising from the oversampling inherent in the Equirectangular Projection (ERP). However, these two fields often employ divergent technological approaches. Since image compression aims to reduce redundancy, computer vision analytics attempts to compensate for the semantic distortion caused by the projection process, resulting in a potential conflict between the two objectives. This paper explores a potential route, i.e.360∘ Image Coding for Machine (360-ICM), which offers an image processing framework that addresses both object deformation and oversampling redundancy within a unified framework. The key innovation lies in inferring a pixel-wise significant map by jointly considering the requirements of redundancy removal and object deformation offsetting. The significance map would be subsequently fed to a deformation-aware image compression network, guiding the bit allocation process as an external condition. More specifically, we employ a deformation-aware image compression network that is characterized by the Spatial Feature Transform (SFT) layer, which is capable of performing complex affine transformations of high-level semantic features, to be essential in dealing with the deformation. The image compression network and significance inference network are jointly trained under the supervision of a 360∘ image-specified object detection network, obtaining a compact representation that is both analytics-oriented and deformation-aware. Extensive experimental results have demonstrated the superiority of the proposed method over existing state-of-the-art image codecs in terms of rate-analytics performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part

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Application of Static Virus Spread Algorithm in Base-Balanced DNA Fragment Optimization

Application of Static Virus Spread Algorithm in Base-Balance...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yao, Yao Zheng, Yanfen Zhang, Xun Liu, Xin Zhang, Xiaokang Wang, Bin Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University School of Software Engineering Dalian China

ISBN: (纸本)9798350386226

DNA has found applications in a diverse array of fields such as computing, medical diagnosis, and circuits. Designing DNA fragments that meet specific requirements is crucial for ensuring the smooth execution of tasks in these domains. However, the conventional approach of combining constraints with evolutionary algorithms often encounters challenges like orthogonality and thermodynamic instability. This poses risks to the control of reaction processes. To tackle these challenges, we have undertaken two tasks in this study: expanding constraint sets and innovating evolutionary algorithms. Firstly, we propose a base balance strategy aimed at enhancing the thermodynamic properties within DNA fragment groups by diversifying neighboring base combinations. The addition of this strategy achieves a minimum variance of 0.03 for the melting temperature while ensuring orthogonality. It represents a significant improvement compared to previous results and reduces the complexity of controlling reactions. Secondly, our static virus spread algorithm optimizes target DNA fragments by base mutations and virus amplification. Simultaneously,it demonstrates good performance across 23 benchmark functions, highlighting its optimization potential. This work is expected to further refine theoretical shortcomings and offer a convenient tool for DNA fragment optimization. © 2024 IEEE.

关键词： Diagnosis

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A survey of Detecting Flakiness in Automated Test Regression Suite 21

A survey of Detecting Flakiness in Automated Test Regression...

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21st International Learning and Technology Conference, L and T 2024

作者： Elgazzar, Mohamed Hossny, Eman Omara, Fatma A. Faculty of Computers and Informaiton Software Engineering Dept Cairo Egypt Faculty of Computers and Informaiton Computer Science Dept Cairo Egypt

ISBN: (纸本)9798350393569

Recently, Flaky Tests have gotten the attention of the research community. These tests not only waste time and resources, but also undermine the reliability of the automated tests and hence the software systems are affected. So, the detection of Flaky Tests is very important and has a wide audience from both industry and academia. In addition, the research work is very promising having a great potential for the contribution that maps to a true value added to the up-to-date industry projects. Although lots of effort has been done to detect Flakiness in the software systems, the majority of the existed works focuses on different techniques in isolation. The aim of the work in this paper is to overlook the existed flakiness detection techniques, try to analyze them, and hold a comparison, indicating the proper context for each one. Most of the existed flakiness detection techniques are done on unit tests and library/technical projects. According to the comparative study among these techniques, it is found gaps and wide potential for contribution. On the other hand, flakiness detection techniques can be clustered under three main headers;First, study the phenomena under different testing levels by trying to focus on projects with integration, system, and end-toend tests. Second, study the phenomena under different Project types and data sets by studying business projects with user interfaces and mobile applications and so on. Third, study the phenomena toward a faster delivery cycle by studying flake tests in the context of parallel execution and Continuous Integration and Continuous Deployment (CI/CD). We can conclude that not all detection techniques have the same potential where some techniques are very competent while others still struggle to achieve considerable efficiency. © 2024 IEEE.

关键词： User interfaces

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HTDcr:a job execution framework for high-throughput computing on supercomputers

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Science China(Information Sciences) 2024年第1期67卷 69-85页

作者： Jiazhi JIANG Dan HUANG Hu CHEN Yutong LU Xiangke LIAO School of Computer Science and Engineering Sun Yat-sen University School of Software Engineering South China University of Technology

High-throughput computing(HTC) is a computing paradigm that aims to accomplish jobs by easily breaking them into smaller,independent ***,it requires a large amount of computing power for a long *** existing HTC frameworks are job-oriented without support for coscheduling with hardware architecture and task-level ***,most of the frameworks reach a limited scale,and their usability needs further ***,we present HTDcr,a job execution framework for the HTC on *** study aims to improve the throughput,task dispatching,and usability of the *** detail,the throughput optimizations include a sophisticated designed task management system,a hierarchical scheduler,and the co-optimization of the task-scheduling strategy with the application and hardware *** optimizations for usability include a programable execution workflow,mechanisms for more robust and reliable service qualities,and a fine-grained resource allocation system for the colocation of multiple *** to our evaluations,HTDcr can achieve outstanding scalability and high throughput on large-scale clusters for the HTC *** evaluate HTDcr with several microbenchmarks and real-world applications on Tianhe-2 and Sunway TaihuLight to demonstrate its effects on existing design *** instance,the task scheduling for two real-world applications integrated with the application and hardware characteristics achieves 1.7× and 1.9× speedups over the basic task-scheduling strategy.

关键词： high-throughput computing supercomputer task scheduling middleware password guessing

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User story clustering in agile development:a framework and an empirical study

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Frontiers of computer Science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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Enhancing Code Generation for Dataflow Programming: Fine-Tuning Large Language Models with the DFCPP Dataset 22

Enhancing Code Generation for Dataflow Programming: Fine-Tun...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Luo, Qiuming Ma, Xi Wei, Xuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China Tianjin Yuyi Kaihong Intelligent Technology Tianjin China

ISBN: (纸本)9798331509712

In recent years, large language models (LLMs) based on the Transformer architecture have demonstrated excellent performance in code generation, but there have been fewer studies on data flow languages. This study proposes a scheme for fine-tuning large language models based on the DFCPP dataset. We demonstrate the model's ability to generate dataflow graph (DAG) topologies and achieve significant performance improvements. Experimental results show that the BLEU score of the fine-tuned model in the DFCPP code generation task reaches 0.193, which is an increase of 112.1% compared to the non-fine-tuned model (0.091). This demonstrates the effectiveness of fine-tuning techniques in domain-specific code generation. © 2024 IEEE.

关键词： Data flow analysis

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