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Algorithms for online fault tolerance server consolidation

Digital Communications and Networks 2025年第2期11卷 514-523页

作者： Boyu Li Bin Wu Meng Shen Hao Peng Weisheng Li Hong Zhang Jie Gan Zhihong Tian Guangquan Xu College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Technology Zhejiang Normal UniversityJinhua 321004China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400054China School of Astronautics Beihang UniversityBeijing100191China School of Computer Science University of SydneySydneyNSW 20528Australia Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou 511370China

We study a novel replication mechanism to ensure service continuity against multiple simultaneous server *** this mechanism,each item represents a computing task and is replicated intoξ+1 servers for some integerξ≥1,with workloads specified by the amount of required *** one or more servers fail,the affected workloads can be redirected to other servers that host replicas associated with the same item,such that the service is not interrupted by the failure of up toξ*** requires that any feasible assignment algorithm must reserve some capacity in each server to accommodate the workload redirected from potential failed servers without overloading,and determining the optimal method for reserving capacity becomes a key *** existing algorithms that assume that no two servers share replicas of more than one item,we first formulate capacity reservation for a general arbitrary *** to the combinatorial nature of this problem,finding the optimal solution is *** this end,we propose a Generalized and Simple Calculating Reserved Capacity(GSCRC)algorithm,with a time complexity only related to the number of items packed in the *** conjunction with GSCRC,we propose a robust replica packing algorithm with capacity optimization(RobustPack),which aims to minimize the number of servers hosting replicas and tolerate multiple server *** theoretical analysis and experimental evaluations,we show that the RobustPack algorithm can achieve better performance.

关键词： Cloud computing Server consolidation Replica Fault tolerance

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A Novel Framework for Human Chronic Disease Prediction Using CNN and Adaptive Dragon Fly Optimization (ADFO) Algorithm

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SN computer science 2025年第4期6卷 1-18页

作者： Ragu, B. Perumal, S. Department of Computer Science School of Computing Sciences Technology and Advanced Studies (VISTAS) Vels Institute of Science Tamil Nadu Chennai 600117 India

Chronic diseases in human beings are long-lasting health disorders that will progress slowly over months or a lifetime. The chronic diseases can affect various parts or systems of the body and are non-acute in nature. The chronic disease does not occur suddenly and shall not be resolved quickly. However, the spreading of the disease to other parts of the body and the effectiveness of the disease shall be suppressed by providing proper medical treatment from the earlier stages. The World Health Organization has expressed the significance of the treatment of chronic disease, stating that around 41 million deaths, representing 71% of all deaths occurring every year due to chronic diseases. This necessitates the earlier detection of human chronic diseases like cancer, cardiovascular disease and diabetes mellitus. The major challenge associated with chronic disease is that it does not express valid symptoms at the earlier stage and needs advanced technology for the earlier detection process with high accuracy. The existing system of chronic disease predictions falls short in diagnosis and detection at the earlier stages, whereas capable of detecting at stage 3 or stage 4, which were considered the advanced stage of the chronic disease. To overcome this challenge, this proposed work introduces a novel framework encompassing the Convolutional Neural Networks (CNN) and the Adaptive Dragon Fly Optimization algorithm for the earlier prediction process. The proposed work was trained with datasets of various chronic diseases individually to prove the system’s adaptability. In addition, the performance of the proposed work is analyzed in terms of accuracy, Precision, recall and F score, which were compared with the performance of the existing state-of-the-art methodologies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive dragon fly optimization algorithm Cancer Cardiovascular disease Convolutional neural networks Human chronic disease

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The Past, Present, and Future of Storage Technologies (Part 1 of 2)

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ACM TRANSACTIONS ON STORAGE 2025年第1期21卷 1-3页

作者： Kuenning, Geoff Won, Youjip Zhao, Ming Computer Science Harvey Mudd College Claremont United States Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea (the Republic of) School of Computing and Information Sciences Arizona State University Tempe United States Computer Science Stony Brook University Stony Brook United States

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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

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Intelligent Automation & Soft computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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Multi-scale feature fusion for facial expression recognition

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Neural computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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An algorithm/hardware co-optimized method to accelerate CNNs with compressed convolutional weights on FPGA

An algorithm/hardware co-optimized method to accelerate CNNs...

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作者： Shang, Jiangwei Zhang, Zhan Zhang, Kun Li, Chuanyou Qian, Lei Liu, Hongwei School of Computer Science and Technology Harbin Institute of Technology Harbin China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China School of Computer Science and Engineering Southeast University Nanjing China

Convolutional neural networks (CNNs) have shown remarkable advantages in a wide range of domains at the expense of huge parameters and computations. Modern CNNs still tend to be more complex and larger to achieve better inference accuracy. However, the complex and large structures of CNNs could slow down the inference speed. Recently, Compressing the convolutional weights to be sparse by pruning the unimportant parameters has been demonstrated as an efficient way to reduce the computations of CNNs. On the other hand, field-programmable gate arrays (FPGAs) have been a popular hardware platform to accelerate CNN inference. In this paper, we propose an algorithm/hardware co-optimized method for accelerating CNN inference on FPGAs. For the algorithm, we take advantage of unstructured and structured parameter sparsifying methods to achieve high sparsity and keep the regularity of convolutional weights. Correspondingly, hardware-friendly index representations of sparse convolutional weights are proposed. For the hardware architecture, we propose row-wise input-stationary dataflow, which is tightly coupled with the algorithm. A row-wise computing engine (RConv Engine) is proposed, which is based on the dataflow. Inside the RConv Engine, the scalar-vector structure is applied to implement the basic processing elements (PEs). To flexibly calculate the feature map with various sizes, the PEs are organized in a 2D structure with two work modes. The experimental results demonstrate that our co-optimized method implements high sparsity of convolutional weights, and the computing engine achieves high computation efficiency. Compared with other accelerators, our co-optimized method implements a 10.9 (Formula presented.) speedup on FPS at most with the highest sparsity of convolutional weights and negligible accuracy loss. © 2024 John Wiley & Sons Ltd.

关键词： Engines

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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A Survey on Hypergraph Mining: Patterns, Tools, and Generators

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ACM computing Surveys 2025年第8期57卷 1-36页

作者： Lee, Geon Bu, Fanchen Eliassi-Rad, Tina Shin, Kijung Kim Jaechul Graduate School of Ai Korea Advanced Institute of Science and Technology Seoul Korea Republic of School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Khoury College of Computer Sciences Northeastern University Boston United States

Hypergraphs, which belong to the family of higher-order networks, are a natural and powerful choice for modeling group interactions in the real world. For example, when modeling collaboration networks, which may involve not just two but three or more people, the use of hypergraphs allows us to explore beyond pairwise (dyadic) patterns and capture groupwise (polyadic) patterns. The mathematical complexity of hypergraphs offers both opportunities and challenges for hypergraph mining. The goal of hypergraph mining is to find structural properties recurring in real-world hypergraphs across different domains, which we call patterns. To find patterns, we need tools. We divide hypergraph mining tools into three categories: (1) null models (which help test the significance of observed patterns), (2) structural elements (i.e., substructures in a hypergraph such as open and closed triangles), and (3) structural quantities (i.e., numerical tools for computing hypergraph patterns such as transitivity). There are also hypergraph generators, whose objective is to produce synthetic hypergraphs that are a faithful representation of real-world hypergraphs. In this survey, we provide a comprehensive overview of the current landscape of hypergraph mining, covering patterns, tools, and generators. We provide comprehensive taxonomies for each and offer in-depth discussions for future research on hypergraph mining. © 2025 Copyright held by the owner/author(s).

关键词： Taxonomies

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An Improved Robust Sparse Convex Clustering

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Tsinghua science and technology 2023年第6期28卷 989-998页

作者： Jinyao Ma Haibin Zhang Shanshan Yang Jiaojiao Jiang Gaidi Li Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China School of Computer Science and Engineering University of New South WalesSydney 2052Australia

Convex clustering,turning clustering into a convex optimization problem,has drawn wide *** overcomes the shortcomings of traditional clustering methods such as K-means,Density-Based Spatial Clustring of Applications with Noise(DBSCAN)and hierarchical clustering that can easily fall into the local optimal ***,convex clustering is vulnerable to the occurrence of outlier features,as it uses the Frobenius norm to measure the distance between data points and their corresponding cluster centers and evaluate *** accurately identify outlier features,this paper decomposes data into a clustering structure component and a normalized component that captures outlier *** from existing convex clustering evaluating features with the exact measurement,the proposed model can overcome the vast difference in the magnitude of different features and the outlier features can be efficiently identified and *** solve the proposed model,we design an efficient algorithm and prove the global convergence of the *** on both synthetic datasets and UCI datasets demonstrate that the proposed method outperforms the compared approaches in convex clustering.

关键词： convex clustering outlier features block coordinate descent Newton’s method

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

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