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An Automated Approach to Discovering Software Refactorings by Comparing Successive Versions

IEEE Transactions on Software engineering 2025年第05期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： Software quality

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AR-Light: Enabling Fast and Lightweight Multi-user Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology TX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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An improved image compression framework in modern multimedia applications using tetrolet transformation

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Multimedia Tools and Applications 2025年第6期84卷 3305-3338页

作者： Yadav, Satya Prakash Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology U.P Gorakhpur India

The fast advancement of the multimedia era has led to an explosion in the use and technology of large amounts of digital snapshots. It has created a developing call for Image compression techniques that can reduce the dimensions of Image without compromising their perceptual excellence. Lossless picture compression techniques have emerged as a popular answer in this regard, as they maintain the precise statistics of the original Image. However, current lossless compression techniques face demanding situations in accomplishing high compression ratios without sacrificing the picture best. This paper proposes a more robust lossless Image compression framework based on the Tetrolet transformation. The framework introduces an adaptive quantization method, which adjusts the quantization step length based totally on the neighborhood Image characteristics, resulting in better compression overall performance for exclusive sorts of Image. Context modeling is also included in the framework to make the most of the inter-Image correlations inside the Image. This method uses context information from the Detroit-converted coefficients, which might be used to estimate the probability of prevalence for every Image value. The proposed model obtained 94.09% accuracy, 92.90% Precision, 95.25% Recall, 92.91% F1-Score and 93.87% Compression Duration. Using as it should be modeling these chances;the framework improves the entropy coding performance and ultimately results in better compression ratios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image compression

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Internet of things technology, research, and challenges: a survey

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Multimedia Tools and Applications 2025年第11期84卷 8455-8490页

作者： Vishwakarma, Amit Kumar Chaurasia, Soni Kumar, Kamal Singh, Yatindra Nath Chaurasia, Renu Management science and technology Khalifa University Abu Dhabi United Arab Emirates Computer science & Engineering SGT University Gurugram India Department of Information Technology IGDTUW New Delhi India Electrical Engineering IIT Kanpur Kanpur India Computer science & Engineering AIT Rooma Kanpur India

The world of digitization is growing exponentially;data optimization, security of a network, and energy efficiency are becoming more prominent. The Internet of Things (IoT) is the core technology of modern society. This paper is based on a survey of recent and past technologies used for IoT optimization models, such as IoT with Blockchain, IoT with WSN, IoT with ML, and IoT with big data analysis. Suppose anyone wants to start core research on IoT technologies, research opportunities, challenges, and solutions. In that case, this paper will help me understand all the basics, such as security, interoperability, standards, scalability, complexity, data management, and quality of service (QoS). This paper also discusses some recent technologies and the challenges in implementation. Finally, this paper discusses research possibilities in basic and applied IoT Domains. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Multi-channel mantis search spatial attention convolutional neural network based hyperspectral image change detection

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Multimedia Tools and Applications 2025年 1-22页

作者： Divya, V.M. Department of Computer Science and Engineering East West Institute of Technology Visvesvaraya Technological University Belagavi560018 India

Hyperspectral imaging has transformed remote sensing by offering detailed spectral data for monitoring environmental changes, like urban development and terrestrial cover variations. However, existing change detection techniques often face challenges like underfitting and overfitting. In this research, a novel approach is introduced for hyperspectral image change detection using a Multi-Channel Mantis Search (MCMS). This innovative methodology leverages the power of a Multi-Channel Convolutional Neural Network (MCNN) to process each spectral band independently, facilitating the capture of unique features within each band. The input hyperspectral images are gathered from the Pavia university Dataset and the Indian Pines Dataset. This approach leads to significantly improved feature extraction, enhancing the discrimination of different materials in hyperspectral images. To further enhance the performance of the change detection model, the Mantis Search Algorithm (MSA) is integrated. The MSA is employed to optimize the model's hyperparameters, fine-tuning its architecture and training parameters to achieve superior results. The combination of MCNN and MSA improves hyperspectral image change detection accuracy, achieving 98.21% and 99.31% with the Indian Pines and Pavia university datasets correspondingly. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Deep Reinforcement Learning-Based Continuous Workflows Scheduling in Heterogeneous Environments

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IEEE Internet of Things Journal 2025年第10期12卷 14036-14050页

作者： Wang, Zhi Zhan, Wenhan Duan, Hancong Min, Geyong Huang, Hualong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Exeter College of Engineering Mathematics and Physical Sciences Department of Computer Science ExeterEX4 4QF United Kingdom

Workflow scheduling plays a critical role in optimizing completion time and throughput in distributed cloud environments, leveraging the parallelism of heterogeneous computing resources. However, existing workflow scheduling algorithms often fall short due to heuristic limitations and the challenges in adaptability within heterogeneous settings, leading to suboptimal scheduling solutions. In this paper, we present a novel deep reinforcement learning (DRL) framework tailored for continuous workflow scheduling in heterogeneous environments. First, we propose an intelligent scheduler that updates the policy network through interactions with a multi-tenant environment, triggered by scheduling events. Next, the framework incorporates a Graph Attention Network (GAT) and a self-attention MultiLayer Perceptron (MLP) to preprocess the workflow topology and embed dynamic features of ready tasks and available processors into the state input at each scheduling step. Additionally, a k-dimensional tree-based k-nearest neighbors (kNN) algorithm is employed to map the output action vector to a pair of executed ready task and processor, facilitating the transition from continuous to discrete action spaces and addressing challenges associated with dynamic action spaces. Experimental results demonstrate that our method converges effectively in continuous workflow scheduling scenarios and significantly outperforms the best-known methods in terms of average makespan and load balancing efficiency. © 2014 IEEE.

关键词： Deep reinforcement learning

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Deep Learning Approaches for Multi-target Weather Patterns Forecasting in Bangladesh 5th

Deep Learning Approaches for Multi-target Weather Patterns F...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Mumu, Sumaiya Siddiqua Sultana, Syeda Samia Adnan, Imranul Islam Waughfa, Mohammed Z. Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819791118

Weather forecasting in countries like Bangladesh poses unique challenges, given its diverse geographical features. The region experiences varying weather patterns influenced by factors such as monsoons, river systems, and coastal influences. The intricacies of high temperatures, sudden atmospheric changes, and complex circulations demand advanced forecasting methods. The research aimed to address the challenges in weather prediction for 8 cities in Bangladesh. The research used 3 deep learning models: CNN(Conv1D), LSTM, and GRU. Comparing these models to get the best results for temperature, rainfall, and humidity prediction. The performance of the models was evaluated using standard techniques, including the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In individual forecasts for Maximum Temperature and Rainfall, GRU shows the lowest RMSE for Rainfall, while LSTM excels in Maximum Temperature prediction. Conv1D proves highly effective in Humidity forecasting. Our investigation emphasizes GRU’s superior performance across eight cities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Lu, Benxue Gao, Kaizhou Duan, Peiyong Slowik, Adam School of Computer Science Liaocheng University Liaocheng252000 China Macau Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Information and Automation Engineering Qilu University of Technology Jinan250353 China Koszalin University of Technology Department of Electronics and Computer Science Koszalin75-453 Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption. First, the Bi-HFSP_CS is formalized, followed by the establishment of a mathematical model. Second, enhanced version of the artificial bee colony (ABC) algorithms is proposed for tackling the Bi-HFSP_CS. Then, fourteen local search operators are employed to search for better solutions. Two different O-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process. Finally, the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm, its three variants, and three effective algorithms in resolving 95 instances of 35 different problems. The experimental results and analysis showcase that the enhanced ABC algorithm combined with O-learning (QABC1) demonstrates as the top performer for solving concerned problems. This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength, offering beneficial perspectives for exploration and research in relevant domains. © 2021 TUP.

关键词： Energy utilization

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Ancient Cultural Models from Informal Learning Structures That Can Transform Developments in Africa: This Ndịgbo Discuss

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Journal of Entrepreneurship and Innovation in Emerging Economies 2025年第1期11卷 49-67页

作者： Ikpo, C.V. Akowuah, E.K. Boateng, K.O. Kponyo, J.J. Nunoo-Mensah, H. Appiah, Y.S. Department of Computer Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Telecommunication Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana

The innovation for entrepreneurial systems and the advocacy to enact policies that institutionalise it had recently flooded the literature. Every system has fundamental principles responsible for their state, progress and change;such is the impact of the Igbo apprenticeship scheme on the entrepreneurial system. This has recently resulted in many scholars calling for government involvement through enacting policies that will make the scheme all-inclusive for sustainable economic growth in developing economies. Amongst the huge body of literature in its advocacy, none has addressed the question of the underlying principles for reproducing the relations in terms of the mathematical phenomena that the system embodies. This work shows that the success of the Ndịgbo apprenticeship hinges on certain defined underlying metaphorical mathematical functional patterns responsible for the strategic informal management behavioural model. The symbolic characteristics derived from the informal discipleship learning mode are activated for commerce, by young males and their hands-on in trading, with persistent cooperation, integrity, communication and collective purposeful attempts to succeed. The evidence of the model described is extracted from the explorative and exploitative trajectories that emerged from their goal-pursuing dedications to satisfy customer needs, with optimal methods for managing resources and building economies of scale, emerge. This is known as Ịgba-ọsọ-ahịa. This work reveals that Ịgba-ọsọ-ahịa embodies attributes of models that have shaped world-class productive systems such as (a) the geometrical Mandelbrot model, (b) Markov Chain variant with a backoff process, (c) stochastic nonlinear models, (d) network spatial model and (e) system computational model. Finally, a simplistic novel stochastic model of the socio-economic processes is developed to solve complex engineering decisions and hyperparameter optimisation problems in this work. © 2024 SAGE Publicatio

关键词： behaviour discipleship Entrepreneurial minimisation socio-economic and cultural Ịgba-ọsọ-ahịa

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Optimized deep learning-oriented strategy for predicting the location of delamination and detecting their shapes in composite structures

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Iran Journal of computer science 2025年 1-16页

作者： Demircioğlu, Ufuk Bakır, Halit Mechanical Engineering Department Sivas University of Science and Technology Sivas Türkiye Computer Engineering Department Sivas University of Science and Technology Sivas Türkiye

In this paper, the possibilities of deep learning approaches for sandwich structure delamination detection and prediction have been investigated. The research was divided into three parts: a validation study, a classification analysis of delamination shape, and delamination location forecasting. The best mesh configuration in terms of computational time and accuracy was chosen during the validation study by comparing a COMSOL MULTIPHYSICS model’s results with those obtained in a previously conducted related study. The natural frequencies of sandwich beams with various delamination forms and locations were collected from COMSOL MULTIPHYSICS and used in the classification and regression studies. Deep learning (DL) models were trained to predict the location of delamination and detect the shape of delamination using the obtained dataset. To improve accuracy and generalizability, we proposed tuning the hyperparameters of the model to choose the best DL structures for achieving both regression and shape detection tasks with as high performance as possible. The Bayesian algorithm was used to select the best combination of values for multiple hyperparameters used in the regression and classification models. Furthermore, several optimization phases have been held to explore the hyperparameter space carefully. In the optimization study, various regression and classification metrics were used to determine the optimum model. The outcomes demonstrate that both delamination location prediction and shape detection were accomplished with excellent performance by the optimized deep learning models. Particularly, a mean squared error value of 0.0000043303 was achieved, which allows regression analysis to identify the location of the delamination accurately. Likewise, 0.9944 of accuracy value has been achieved for the shape detection analysis, which allows the model to identify delamination shapes precisely. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2

关键词： Deep learning Finite element method Hyperparameter tuning Sandwich structures delamination

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