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41st Computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Application of Artificial Intelligence in the Supply Chain: A Systematic Literature Review 6th

Application of Artificial Intelligence in the Supply Chain: ...

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6th International Conference on Big Data Cloud and Internet of Things, BDIoT 2022

作者： Kriouich, Mohamed Sarir, Hicham Mahboub, Oussama Advanced Science and Technology Team ENSA Tetouan Abdelmalek Esaadi University Tetouan Morocco Information System and Software Engineering ENSA Tetouan Abdelmalek Esaadi University Tetouan Morocco

ISBN: (纸本)9783031283864

This research presents a systematic literature review of using artificial intelligence (AI) in the supply chain (SC). We examine the works done so far in relation to AI research, which still needs to be investigated in light of the potential benefits of implementing AI in supply chain management. Dynamic supply chain processes require artificial intelligence techniques capable of dealing with their increasing complexity. Artificial intelligence techniques such as machine learning, Deep learning, natural language processing, expert systems, knowledge representation, Fuzzy logic and models, social intelligence, Artificial neural networks, Support vector machines, Gaussian models, robotics, Ant colony, and computer vision can transform and optimize the supply chain. In order to classify the study material, we looked at 50 research papers from the Scopus database that were published between 2002 and 2022 and assigned each one a grade based on two fundamental structural factors are as follows: AI techniques and sector of application. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum Chemistry software Bug Detection 23

CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum...

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Qiu, Feng Ji, Pu Hua, Baojian Wang, Yang School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China The Johns Hopkins University Computer Science Department United States

ISBN: (纸本)9798350359398

Quantum chemistry software implements the first principle quantum computation and is indispensable in both scientific research and chemical industries. Any bugs in such software will lead to serious consequences, thus defeating its trustworthiness and reliability. However, bug detection techniques for such software have not been fully investigated. In this paper, to fill this gap, we propose a novel approach to fuzz quantum chemistry software with the aid of Large Language Models (LLMs). Our basic idea is utilize LLMs to mutate and generate syntactic and semantic valid input files from seed inputs, by proving valuable domain-specific knowledge of chemistry. With this basic idea, we have designed and implemented CHEMFuzz, a fully automatic fuzzing framework to fuzz quantum chemistry software for bugs. Our evaluation of CHEMFUZZ leverages popular LLMs including GPT3.5, Claude-2, and Bart as test oracles to generate parameters to mutate inputs and analyze computation results. CHEMFUZZ detected 40 unique bugs, which have been classified and reported to developers, with a code coverage of 17.4%. © 2023 IEEE.

关键词： Quantum chemistry

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Toward actionable testing of deep learning models

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Science China(Information Sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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Computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Establishing Pattern Sequences Using Artificial Neural Networks with an Application to Organizational Patterns 10

Establishing Pattern Sequences Using Artificial Neural Netwo...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Matovič, Viktor Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava84216 4 Slovakia

Explicit relationships included in textual description of patterns are insufficient to establish pattern sequences. This article brings a new method of establishing pattern sequences using artificial neural networks. The method is based on extracting implicit relationships between organizational patterns using the softmax regression model by Zhang et al., which are learned by the artificial neural network and encoded in its weights. The implementation of the method is based on the mini-batch stochastic gradient descent method by Zhang et al. and the Keras application programming interface of the TensorFlow framework. To evaluate the method, we conducted four experiments on selected organizational patterns documented by Coplien and Harrison. Two pattern sequences were established in each experiment. For each pattern sequence, a pattern story was created in order to check its meaningfulness. © 2023 Copyright for this paper by its authors.

关键词： artificial neural networks organizational pattern pattern relationships pattern sequence softmax regression

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Design and implementation of Linux system for SAR imaging equipment 25

Design and implementation of Linux system for SAR imaging eq...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control engineering

作者： Qianqian Zhang Zhangqin Huang Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9798400712647

To meet the lightweight, real-time, and high-reliability requirements of UAV-borne SAR imaging systems, this paper presents a Zynq UltraScale+ and Linux-based solution for UAV-borne SAR imaging. An embedded Linux system startup process was designed and implemented on a SAR imaging device based on the Zynq UltraScale+ series chip xczu47dr. The paper provides a detailed description of the overall architecture design of the UAV-borne SAR imaging system, the process of booting the Linux operating system on the Zynq chip, and the resource configuration for both the PL and PS sides. Additionally, it outlines the steps involved in configuring and optimizing the Linux system using Petalinux tools. Finally, the correctness and stability of the embedded Linux system design were validated through experiments conducted on a custom hardware platform based on the xczu47dr chip.

关键词： FPGA

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Towards Modeling Functional Requirements From Tacit Knowledge

Towards Modeling Functional Requirements From Tacit Knowledg...

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2023 Congress in Computer Science, Computer engineering, and Applied Computing, CSCE 2023

作者： Jafar, Rameen Almufareh, Maram Fahaad Ashraf, Sundas Khan, Bushra Butt, Wasi Haider Humayun, Mamoona National University of Sciences and Technology Department of Computer and Software Engineering Pakistan College of Computer and Information Sciences Jouf University Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350327595

Requirements Elicitation is the process of identifying system needs by talking with stakeholders who have a direct or indirect effect on the requirements. Requirements may be derived from several sources and are one of the most important phases of requirement engineering. We need to perform different elicitation techniques to find out the user's needs. Pre-requirements tracing is the process of determining the origins of a specific demand. Typically, certain needs arise that have no apparent source, but stakeholders will testify to their importance. Such criteria, however, are most likely dependent on tacit or tacit-like information entrenched in the issue domain. Tacit knowledge is one of the most common challenges for requirement analysts. This study contributes to constructing a systematic literature review for exploring multiple ways to model functional requirements using tacit knowledge. We have identified and analyzed 10 studies. In these research papers, different authors have discussed different methods to gather functional requirements from tacit knowledge. © 2023 IEEE.

关键词： Elicitation Techniques Requirement engineering Tacit-knowledge

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MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers 4th

MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Dise...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Loba, Jannatul Mia, Md. Rajib Mahmud, Imran Mahi, Md. Julkar Nayeen Whaiduzzaman, Md. Ahmed, Kawsar Department of Software Engineering Daffodil International University Dhaka Bangladesh School of Information Systems Queensland University of Technology Brisbane Australia Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9783031423161

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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