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3D reconstruction of spherical images:a review of techniques,applications,and prospects

地球空间信息科学学报（英文版） 2024年第6期27卷中插13,1959-1988页

作者： San Jiang Kan You Yaxin Li Duojie Weng Wu Chen School of Computer Science China University of GeosciencesWuhanChina Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina cHubei Luojia Laboratory Wuhan UniversityWuhanChina School of Computer Science China University of GeosciencesWuhanChina Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

3D reconstruction plays an increasingly important role in modern photogrammetric *** satellite or aerial-based remote sensing(RS)platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and *** with low-altitude Unmanned Aerial Vehicles(UAVs),3D reconstruction in complicated situations,such as urban canyons and indoor scenes,is challenging due to frequent tracking failures between camera frames and high data collection ***,spherical images have been extensively used due to the capability of recording surrounding environments from one *** contrast to perspective images with limited Field of View(FOV),spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex *** the rapid evolution and extensive use of professional and con-sumer-grade spherical cameras,spherical images show great potential for the 3D modeling of urban and indoor *** 3D reconstruction pipelines,however,cannot be directly used for spherical ***,there exist few software packages that are designed for the 3D reconstruction from spherical *** a result,this research provides a thorough survey of the state-of-the-art for 3D reconstruction from spherical images in terms of data acquisition,feature detection and matching,image orientation,and dense matching as well as presenting promising applications and discussing potential *** anticipate that this study offers insightful clues to direct future research.

关键词： Spherical image equirectangular projection 3D reconstruction structure from motion simultaneous localization and mapping dense matching image matching

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An Investigation on the Use of Heterogeneous Ensemble Machine Learning in Predicting Two Measures of Software Maintainability

An Investigation on the Use of Heterogeneous Ensemble Machin...

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2023 International Conference on Decision Aid sciences and Applications, DASA 2023

作者： Chouaib, S.I.D. Nora, Bounour Hanene, Cherait Badji Mokhtar-Annaba University Lisco Laboratory Computer Science Department Annaba Algeria SoukAhras University Lisco Laboratory Computer Science Department Annaba Algeria

ISBN: (纸本)9798350342055

The software maintainability is considered as an indispensable factor to acclaim the quality of a particular software. This attribute indicates the amount of work required to correct or modify software. The prediction of the maintainability of software systems makes it possible to give an answer in terms of effort for software developers. To date, many works in the literature have used several machine learning techniques to predict the maintainability. The maintainability is often measured via the change metric which calculates the number of lines changed per class in an object oriented system. In this paper, we focus our investigation on the use of heterogeneous ensemble learning techniques (Stacking and Voting) for the prediction of two maintainability metrics: the change and the maintainability index. We propose a model that combines the predictions of three individual models that we have chosen in order to improve the accuracy calculated via Mean Absolute Error (MAE), and Mean Magnitude of Relative Error (MMRE). Our study has shown that Stacking is the best model to use to predict maintainability. © 2023 IEEE.

关键词： Forecasting

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A New Hyperchaotic System with Exponential Function Non Linearity: Dynamical Properties, Control Hyperchaos and Complete Synchronization Study

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1329-1335页

作者： Meskine, Rayene Abir Kaouache, Smail Aybar, Orhan Ozgur Laboratory of Mathematics and Their Interactions Institute of Mathematics and Computer Science Abdelhafid Boussouf University Center Mila43000 Algeria Department of Mathematics Institute of Mathematics and Computer Science Abdelhafid Boussouf University Center Mila43000 Algeria Department of Mathematics Piri Reis University Istanbul Tuzla34940 Turkey

In this paper, we construct a new 4 − D hyperchaotic system with a nonlinear term in the form of an exponential function. The system is derived from a modified 3 − D Lü system. Firstly, we discuss the qualitative properties of the proposed system, using tools such as hyperchaotic attractors, symmetry, dissipation, equilibrium points, and Lyapunov spectrum for analysis. Next, we present an adaptive controller for stability analysis of the system. An active controller is designed to achieve complete synchronization between two identical systems. All stability results are established using Lyapunov stability theory. Finally, numerical examples and computer simulations are provided to illustrate the main results. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Lyapunov functions

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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Public Datasets for Cloud Computing: A Comprehensive Survey

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ACM Computing Surveys 2025年第8期57卷 1-38页

作者： Liu, Guozhi Lin, Weiwei Zhang, Haotong Lin, Jianpeng Peng, Shaoliang Li, Keqin School of Computer Science and Technology South China University of Technology Guangzhou China South China University of Technology Guangzhou China Pengcheng Laboratory Shenzhen China Hunan University Changsha China Department of Computer Science State University of New York New Paltz United States

Publicly available datasets are vital to researchers because they permit the testing of new algorithms under a variety of conditions and ensure the verifiability and reproducibility of scientific experiments. In cloud computing research, there is a particular dependence on obtaining load traces and network traces from real cloud computing clusters, which are used for designing energy efficiency prediction, workload analysis, and anomaly detection solutions. To address the current lack of a comprehensive overview and thorough analysis of cloud computing datasets and to gain insight into their current status and future trends, in this article, we provide a comprehensive survey of existing publicly cloud computing datasets. First, we utilize a systematic mapping approach to analyze 968 scientific papers from 6 scientific databases, resulting in the retrieval of 42 datasets related to cloud computing. Second, we categorize these datasets based on 11 characteristics to assist researchers in quickly finding datasets suitable for their specific needs. Third, we provide detailed descriptions of each dataset to assist researchers in gaining a clearer understanding of their characteristics. Fourth, we select 12 mainstream datasets and conduct a comprehensive analysis and comparison of their characteristics. Finally, we discuss the weaknesses of existing datasets, identify challenges, provide recommendations for long-term dataset maintenance and updates, and outline directions for the future creation of new cloud computing datasets. Related resources are available at https://***/ACAT-SCUT/Awesome-CloudComputing-Datasets. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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BiCSA-PUL: binary crow search algorithm for enhancing positive and unlabeled learning

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1729-1743页

作者： Azizi, Nabil Ben Othmane, Mohamed Hamouma, Moumen Siam, Abderrahim Haouassi, Hichem Ledmi, Makhlouf Hamdi-Cherif, Aboubekeur Department of Computer Science Mostefa Ben Boulaid University Batna 2 Batna 05001 Algeria Department of Computer Science Abbes Laghrour University Khenchela 40004 Algeria Knowledge Engineering and IT Security Laboratory (ICOSI) Abbes Laghrour University Khenchela 40004 Algeria Department of Computer Science Ferhat Abbas University of Setif 1 Setif 19137 Algeria

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Classification Machine learning Metaheuristic optimization Positive and unlabeled learning Raw search algorithm Two-step algorithms

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Enhancing Breast Mass Cancer Detection Through Hybrid ViT-Based Image Segmentation Model 6th

Enhancing Breast Mass Cancer Detection Through Hybrid ViT-Ba...

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6th conference on Computing Systems and Applications, CSA 2024

作者： Touazi, Faycal Gaceb, Djamel Boudissa, Nesrine Assas, Siham LIMOS Laboratory Computer Science Department University of Boumerdès Boumerdès Algeria

ISBN: (纸本)9783031718472

Breast cancer, primarily affecting women, is characterized by uncontrolled cell growth in breast tissue, leading to the formation of tumors. Although its exact causes remain elusive, factors such as age, genetics, and hormonal influences increase susceptibility. Early diagnosis plays a pivotal role in reducing mortality rates, emphasizing the importance of intelligent screening, diagnosis, and treatment methods. In recent years, artificial intelligence, particularly deep learning, has gained prominence in various industries, including medicine, due to its remarkable success in solving complex problems. This study focuses on leveraging the power of deep learning to accurately segment abnormal breast regions, a critical step in breast cancer diagnosis. We explore and compare the performance of two distinct deep learning architectures: A-UNet and Trans-UNet. These architectures have demonstrated impressive potential in improving breast cancer detection. A-UNet achieved a Dice coefficient of 90.21% and 82.11% of IoU. Notably, Trans-UNet outperformed the A-UNet with a Dice coefficient of 94.13% and an IoU of 88.92%. These results showcase the promising capabilities of deep learning models in enhancing breast cancer detection, underscoring their potential to contribute significantly to early diagnosis and improved patient outcomes in the battle against this challenging disease. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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Attention Mechanisms in Deep Learning : Towards Explainable Artificial Intelligence 6

Attention Mechanisms in Deep Learning : Towards Explainable ...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： El Houda Dehimi, Nour Tolba, Zakaria University of Oum El Bouaghi Liaoa Laboratory Department of Mathematics And Computer Science Algeria

ISBN: (纸本)9798350350265

Attention mechanisms have revolutionized Machine Learning (ML), particularly in Natural Language Processing (NLP). These mechanisms enable models to selectively focus on crucial parts of the input data, improving performance across tasks like machine translation and sentiment analysis. However, complex ML architectures often remain opaque. This paper explores how attention mechanisms offer a unique path towards Explainable Artificial Intelligence (XAI). By visualizing and analyzing where a model "attends", we can gain insights into which features or data components were most impactful for its predictions. This understanding facilitates model debugging, bias detection, and the development of more transparent AI systems. We discuss different types of attention mechanisms and their potential for explainability in various ML domains. © 2024 IEEE.

关键词： Sentiment analysis

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Distributed Training Based on Horizontal Scaling for Food Image Classification 4

Distributed Training Based on Horizontal Scaling for Food Im...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： El Amin, Gasbaoui Mohammed Soumia, Benkrama Mostefa, Bendjima Sofiane, Abden Ikram, Kourtiche Tahri Mohammed University Laboratory of Tit Mathematics and Computer Science Department Bechar Algeria

ISBN: (纸本)9798331519872

Deep learning models for food image classification rely on vast amounts of data to effectively recognize and differentiate between various food items. However, training these models on such extensive datasets presents significant computational challenges. Big data and distributed training are inherently complementary in addressing these challenges. Big data involves large volumes of information that traditional single-machine processing cannot handle efficiently. Distributed training offers a solution by enabling machine learning models to scale across multiple machines or nodes, thereby more effectively processing and learning from extensive datasets. By partitioning tasks into smaller chunks processed in parallel, distributed training optimizes computational resources, ensuring efficient analysis of big data while maintaining fault tolerance and performance scalability. This paper explores various strategies for distributed training and communication between GPUs. We compare the performance and utilization of a single P100 GPU versus two T4 GPUs on the Food-11 dataset, focusing on accuracy and training time. Our results indicate that using two GPUs yields slightly better performance than a single GPU, reducing the training time by 125 seconds, decreasing the validation dataset loss by 0.02, and increasing the accuracy by 1%. The overall accuracy achieved is 92%, with a corresponding loss of 0.28. © 2024 IEEE.

关键词： big data data distribution deep learning food images GPUs parallel processing PyTorch

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Subjective Quality Assessment of Foveated Omnidirectional Images in Virtual Reality

IEEE Open Journal on Immersive Displays

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IEEE Open Journal on Immersive Displays 2025年 2卷 5-16页

作者： Bozorgian, Ali Pedersen, Marius Thomas, Jean-Baptiste Larabi, Mohamed-Chaker NTNU—Norwegian University of Science and Technology Colourlab Department of Computer Science Gjøvik7034 Norway Universite de Poitiers Laboratory XLIM Department SIC Poitiers86000 France

This study presents a novel dataset called "Foveated Omnidirectional Image Quality Assessment" (FOIQA) for the subjective quality evaluation of foveated 2D omnidirectional images. This dataset addresses the limitations of existing datasets by leveraging a high-resolution head-mounted display and a gaze-contingent evaluation approach. We provide individual opinion scores, mean opinion scores, and gaze data associated with both the test and reference images. The utility of our dataset is validated by benchmarking two existing objective foveated image quality metrics. Our results demonstrate that incorporating gaze data into the evaluation framework improves the accuracy of one of the tested objective metrics. © 2024 IEEE.

关键词： Virtual environments

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