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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Ramzan, Hafiz Arslan Ramzan, Sadia Kalsum, Tehmina Institute of Computer and Software Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering and Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

In the context of small software development teams, this research article gives a thorough investigation of the adoption of test-driven development (TDD) approaches. It aims to highlight the benefits that TDD offers, such as improved code quality through modularization and proactive defect spotting which results in effective debugging and development processes. It also discusses the complex issues that arise when TDD is implemented in smaller teams, such as the learning curve and resource constraints. This study significantly advances the understanding of how TDD can be used to optimize software development techniques in organizations or software houses having small development teams. It also explores the possible advantages and challenges. © 2024 IEEE.

关键词： Modular construction

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Prediction of landslide tsunami run-up on a plane beach through feature selected MLP-based model

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Journal of Ocean engineering and Science 2024年第3期9卷 222-231页

作者： Baran Aydın Savas Yaguzluk Mustafa Açıkkar Department of Civil Engineering Adana Alparslan Turkes Science and Technology UniversityAdana 01250Turkey Department of Nanotechnology and Engineering Sciences Adana Alparslan Turkes Science and Technology UniversityAdana 01250Turkey Department of Software Engineering Adana Alparslan Turkes Science and Technology UniversityAdana 01250Turkey

We proposed new prediction models based on multilayer perceptron(MLP)which successfully predict the maximum run-up of landslide-generated tsunami waves and assess the role of parameters affecting *** input is approximately 55,000 rows of data generated through an analytical solution employing slide’s cross section,initial submergence,vertical thickness,horizontal length,beach slope angle and the maximum run-up itself,along with its occurrence *** parameters are first ranked through a feature selection algorithm and six models are constructed for a 9,000-row randomly sampled *** MLP-based models led predictions with a minimum Mean Absolute Percentage Error of 1.1%and revealed that vertical slide thickness has the largest impact on the maximum tsunami run-up,whereas beach slope angle has minimal *** parison with existing literature showed the reliability and applicability of the offered *** methodology introduced here can be suggested as fast and flexible method for prediction of landslide-induced tsunami run-up.

关键词： Landslide tsunami Maximum run-up Artificial neural networks Feature selection

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6DFLRNet: 6D rotation representation for head pose estimation based on facial landmarks and regression

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Multimedia Tools and Applications 2024年第26期83卷 68605-68624页

作者： Zhao, Na Ma, Yaofei Li, Xiaopeng Lee, Shin-Jye Wang, Jian Engineering Research Center of Cyberspace School of Software Yunnan University Yunnan Kunming650504 China Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming650091 China Institute of Management of Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China

Head pose estimation methods can be generally classified into two categories: model-based and appearance-based methods. The model-based approach relies on facial landmarks for three-dimensional reconstruction, aiming to achieve high-precision results. However, this method is heavily dependent on the accuracy of these landmarks. The appearance-based approach utilizes images as input and employs feature extraction and calculations to generate outcomes. While the appearance-based method boasts greater robustness, its accuracy falls short of the former. In this paper, a new and effective hybrid method is proposed. This hybrid approach combines the strengths of both methods. Unlike the conventional model-based methods, the proposed method regards the facial landmarks in 2D images as a sequence of neural network inputs and then obtains the head pose estimation results for users by neural network regression. The proposed method solves the fuzzy rotation labeling problem by using a rotation matrix representation, introducing a 6D rotation matrix representation as an intermediate state of the rotation matrix to achieve effective direct regression. Introducing face processing enhances the robustness of the model in cross-dataset scenarios. The proposed method achieves remarkable results based on imprecise face recognition and a simplistic model. The proposed method can be divided into three parts. First, the proposed method applies face processing on the input image;second, the method detects facial landmarks;and third, it converts these facial landmarks into sequences and obtains the 6D rotation representation of the head pose by regression. Extensive experiments on the publicly available BIWI, PRIMA, and DrivFace datasets show that this method is functional and performs better than other state-of-the-art methods. Compared to other methods, this approach demonstrates an average performance improvement of at least 10% across the dataset. © The Author(s), under exclusive licen

关键词： Rotation

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Competition for gradient-free tuning of large language models:approaches, results, current challenges and future directions

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National Science Review 2023年第6期10卷 16-19页

作者： Tingfeng Cao Liang Chen Dixiang Zhang Tianxiang Sun Zhengfu He Xipeng Qiu Xing Xu Hai Zhang School of Software Engineering South China University of Technology School of Computer Science Fudan University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural language processing tasks,but also captured widespread attention from the public due to their great potential in a variety of real-world applications (***,search engines,writing assistants,etc.)through providing general-purpose intelligent services.A few of the LLMs are becoming foundation models,an analogy to infrastructure,that empower hundreds of downstream applications.

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A Population Initialization Method Based on Similarity and Mutual Information in Evolutionary Algorithm for Bi-Objective Feature Selection

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-21页

作者： Cai, Xu Xue, Yu Artificial Intelligence Research Institute School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Software Nanjing University of Information Science and Technology Nanjing China

Feature selection (FS) is an important data pre-processing technique in classification. It aims to remove redundant and irrelevant features from the data, which reduces the dimensionality of data and improves the performance of the classifier. Thus, FS is a bi-objective optimization problem, and evolutionary algorithms (EAs) have been proven to be effective in solving bi-objective FS problems. EA is a population-based metaheuristic algorithm, and the quality of the initial population is an important factor affecting the performance of EA. An improper initial population may negatively affect the convergence speed of the EA and even make the algorithm fall into the local optimum. In this article, we propose a similarity and mutual information-based initialization method, named SMII, to improve the quality of the initial population. This method determines the distribution of initial solutions based on similarity and shields features with high correlation to the selected features according to mutual information. In the experiment, we embed SMII, the latest four initialization methods, and a traditional random initialization method into NSGA-II and compared their performance on 15 public datasets. The experimental results show that SMII performs best on most datasets and can effectively improve the performance of the algorithm. Moreover, we compare the performance of two other EAs before and after embedding SMII on 15 datasets, and the results further prove that the proposed method can effectively improve the search capability of the EA for FS. © 2024 Copyright held by the owner/author(s).

关键词： Evolutionary algorithms

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Automata Learning for React Web Applications 18

Automata Learning for React Web Applications

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18th IEEE International Conference on software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Grubelnik, Peter Wotawa, Franz Graz University of Technology Graz Austria Graz University of Technology Institute of Software Engineering and Artificial Intelligence Graz Austria

ISBN: (纸本)9798331534677

Testing is an inevitable part of any software engineering process to ensure quality and reliability. Model-based testing is a successful approach for the automated generation of test cases but requires a model of the system under test. Constructing such models can take time and effort. Hence, there is even a need to automate model generation, where automata learning is a promising approach. In this paper, we address the application of automata learning for React web forms used in a wide range of web applications. We provide information regarding a tool we developed to couple web forms to automata learning for obtaining a model. In addition, we discuss the application of the tool, limitations, challenges, and solutions for reducing the state space and making the approach feasible for practical applications. © 2025 IEEE.

关键词： software testing

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A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

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Computer Modeling in engineering & Sciences 2024年第7期140卷 255-273页

作者： Zhong Qu Guoqing Mu Bin Yuan School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr

关键词： Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection

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Dialog generation for Arabic chatbot

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International Journal of Information technology (Singapore) 2024年第2期16卷 881-890页

作者： Youness, Farida Madkour, Mohamed Ashraf Elshenawy, Ayman Computers and Systems Department Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

Dialogue generation systems (DGS) is an important topic in the field of Natural Language Processing (NLP). It enables a wide range of real-world applications to interact with humans in various languages naturally and intelligently. Due to the complexity of the Arabic language, the development of an Arabic DGS is a challenge. In this paper, an Arabic DGS is developed using a sequence-to-sequence (Seq2Seq) framework comprised of Long-Short Term Memory (LSTM). A dataset of 12k conversations is translated from English to Arabic for the development process. The proposed DGS is analyzed, and its effectiveness is assessed using the Bilingual Evaluation Understudy (BLEU) score. The TensorFlow deep learning framework is used to test the proposed DGS. Also, the proposed DGS is tested and compared with a recently developed DGS. The Experiments revealed that the proposed Arabic DGS is effective in eliciting positive responses, with a BLEU score of 0.5988. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Artificial intelligence Chatbot Deep learning Dialogue generation Natural language preprocessing Seq2Seq

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Constructing uniform design tables based on restart discrete dynamical evolutionary algorithm

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Soft Computing 2024年第19期28卷 11515-11534页

作者： Zhao, Yuelin Wu, Feng Yang, Yuxiang Wei, Xindi Hu, Zhaohui Yan, Jun Zhong, Wanxie State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics Faculty of Vehicle Engineering and Mechanics Dalian University of Technology Dalian116023 China

Generating uniform design tables (UDTs) is the first step to experimenting efficiently and effectively, and is also one of the most critical steps. Thus, the construction of uniform design tables has received much attention over the past decades. This paper presents a new algorithm for constructing uniform design tables: restart discrete dynamical evolutionary algorithm (RDDE). This algorithm is based on a well-designed dynamical evolutionary algorithm and utilizes discrete rounding technology to convert continuous variables into discrete variables. Considering the optimization of UDT is a multi-objective optimization problem, RDDE uses Friedman rank to select the optimal solution with better comprehensive comparison ranking. RDDE also utilizes a simulated annealing-based restart technology to select control parameters, thereby increasing the algorithm's ability to jump out of local optima. Comparisons with state-of-the-art UDTs and two practical engineering examples are presented to verify the uniformity of the design table constructed by RDDE. Numerical results indicate that RDDE can indeed construct UDTs with excellent uniformity at different levels, factors, and runs. Especially, RDDE can flexibly construct UDTs with unequal intervals of factors that cannot be directly processed by other designs of experiment. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Simulated annealing

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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Science China(Information Sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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