Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

COMPUTER SCIENCE (1305 journals)

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The Journal of Supercomputing
Journal Prestige (SJR): 0.407
Citation Impact (citeScore): 2
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-0484 - ISSN (Online) 0920-8542
Published by Springer-Verlag Homepage  [2468 journals]
  • Publisher Correction: Improving query processing in blockchain systems by
           using a multi-level sharding mechanism

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      PubDate: 2024-08-01
       
  • Retraction Note: Robust adversarial uncertainty quantification for deep
           learning fine-tuning

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      PubDate: 2024-08-01
       
  • Retraction Note: Mobile client data security storage protocol based on
           multifactor node evaluation

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      PubDate: 2024-08-01
       
  • Correction to: A heuristic hybrid instance reduction approach based on
           adaptive relative distance and k-means clustering

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      PubDate: 2024-08-01
       
  • Retraction Note: Efficient hybrid algorithm based on genetic with weighted
           fuzzy rule for developing a decision support system in prediction of heart
           diseases

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      PubDate: 2024-08-01
       
  • Performance evaluation of Word2vec accelerators exploiting spatial and
           temporal parallelism on DDR/HBM-based FPGAs

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      Abstract: Abstract Word embedding is a technique for representing words as vectors in a way that captures their semantic and syntactic relationships. The processing time of one of the most popular word embedding technique Word2vec is very large due to the huge data size. We evaluate the performance of a power-efficient FPGA-based accelerator designed using OpenCL. We achieved up to 18.7 times speed-up compared to single-core CPU implementation with the same accuracy. The proposed accelerator consumes less than 83 W of power and it is the most power-efficient one compared to many top-end CPU and GPU-based accelerators.
      PubDate: 2024-08-01
       
  • A learning-based efficient query model for blockchain in internet of
           medical things

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      Abstract: Abstract This paper proposes a learning-based model for the resource-constrained edge nodes in the blockchain-enabled Internet of Medical Things (IoMT) systems to realize efficient querying. Three layers are designed in the new model: data evaluation layer, data storage layer and data distribution layer. The data evaluation layer extracts the features from medical data and evaluates their values based on the Extreme Learning Machine (ELM) method. Then, in the data storage layer, according to the value of medical data, a novelty data structure called Merkle–Huffman tree (M-H tree) is established. Compared with the Merkle tree, high-value data (frequently accessed data) in M-H tree is saved closer to the root node and can be found faster. In the data distribution layer, the sharding-based blockchain model is adopted to increase the storage scalability of the IoMT system. Finally, the experimental results show that the new learning-based model can effectively improve the query speed of the blockchain-enabled medical system by about 3.5% and free up large amounts of storage space on IoMT devices.
      PubDate: 2024-08-01
       
  • MS-HRNet: multi-scale high-resolution network for human pose estimation

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      Abstract: Abstract Human pose estimation has important applications in medical diagnosis (such as early diagnosis of autism in children and assisting with the diagnosis of Parkinson’s disease), human-computer interaction, animation, and other fields. Currently, many human pose estimation algorithms are based on deep learning. However, most research focuses only on increasing the depth and width of the network model. This approach overlooks that merely enlarging the network’s depth and width results in excessive parameterization, without enhancing the model’s effective receptive field or its ability to extract multi-scale features. Hence, this paper constructs a network model, named MS-HRNet (Multi-Scale High-Resolution Network), for human pose estimation. Specifically, we propose a more concise and efficient version of HRNet framework as the backbone network of MS-HRNet. This addresses the challenges of HRNet complex structure and large number of parameters that cause training difficulties, and its inadequacy in handling multi-scale information. Additionally, we designed a multi-scale convolutional kernel parallel module named MSBlock (Multi-Scale Block) as the basic block of MS-HRNet. By introducing coordinate attention modules and ASFF (Adaptive Spatial Feature Fusion ) modules, the model’s ability to extract information is effectively increased, and the issue of feature conflict during the fusion of features with different resolutions is resolved, with only a small increase in the number of model parameters. To evaluate the effectiveness of the proposed model, we conducted comparison experiment and ablation experiments using popular human pose estimation datasets, including COCO2017 and MPII, against multiple existing human pose estimation models.On the COCO 2017 dataset, the number of MS-HRNet parameters are decreased by 41% than the baseline model HRNet, the computational complexity by 59%, and the detection accuracies(mAP) are increased by 2.4 point.
      PubDate: 2024-08-01
       
  • FCNet: a deep neural network based on multi-channel feature cascading for
           image denoising

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      Abstract: Abstract A lot of current work based on convolutional neural networks (CNNs) has fetched good visual results on AWGN (additive white Gaussian noise) removal. However, ordinary neural networks are unable to recover detailed information for complex tasks, and the application of a single Gaussian denoising model is greatly limited. To improve the practicality of the denoising algorithm, we trained a DCNN (deep convolutional neural network) to perform multiple denoising tasks, including Gaussian denoising and blind Gaussian denoising. The proposed CNN denoising model with a residual structure and apply feature attention to exploit channel dependency. The network structure mainly consists of sparse block (SB), feature fusion block (FFB), feature compression block (FCB), information interaction block (IIB) and reconstruction block (RB). The SB with sparse mechanism obtains global and local features by alternating between dilated convolution and common convolution. The FFB collects and fuses global and local features to provide additional information for the latter network. The FCB refines the extracted information and compresses the network. The IIB is used for feature integration and dimensionality reduction. Finally, the RB is used to reconstruct the denoised image. A channel attention mechanism is added to the network, and a trade-off is made between the denoising effect and the complexity of the network. A large number of experiments are conducted on five datasets, and the results showed that the proposed method achieves highly competitive performance in both objective evaluation indicators and subjective visual effects.
      PubDate: 2024-08-01
       
  • DDSC-SMOTE: an imbalanced data oversampling algorithm based on data
           distribution and spectral clustering

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      Abstract: Abstract Imbalanced data poses a significant challenge in machine learning, as conventional classification algorithms often prioritize majority class samples, while accurately classifying minority class samples is more crucial. The synthetic minority oversampling technique (SMOTE) represents one of the most renowned methods for handling imbalanced data. However, both SMOTE and its variants have limitations due to their insufficient consideration of data distribution, leading to the generation of incorrect and unnecessary samples. This paper, therefore, introduces a novel oversampling algorithm called data distribution and spectral clustering-based SMOTE (DDSC-SMOTE). This algorithm addresses the shortcomings of SMOTE by introducing three innovative data distribution-based improvement strategies: adaptive allocation of synthetic sample quantities strategy, seed sample adaptive selection strategy, and synthetic sample improvement strategy. First, we use the k-nearest neighbor sample labels and the local outlier factor algorithm to remove noisy and outlier samples. Next, we leverage spectral clustering to identify clusters within the minority class and propose a dual-weight factor that considers inter-cluster and intra-cluster distances to allocate the number of synthetic samples effectively, addressing interclass and intraclass imbalances. Furthermore, we introduce a relative position weight coefficient to determine the probability of selecting seed samples within the subcluster, ensuring that important minority samples have higher chances of being sampled. Finally, we improve the SMOTE sample synthesis formula for safer generation. Extensive comparisons on real datasets from the UCI repository demonstrate that DDSC-SMOTE outperforms seven state-of-the-art oversampling algorithms significantly in terms of G-mean and F1-score, presenting a data distribution-focused solution for addressing imbalanced data challenges.
      PubDate: 2024-08-01
       
  • Service placement in fog–cloud computing environments: a
           comprehensive literature review

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      Abstract: Abstract With the rapid expansion of the Internet of Things and the surge in the volume of data exchanged in it, cloud computing became more significant. To face the challenges of the cloud, the idea of fog computing was formed. The heterogeneity of nodes, distribution, and limitation of their resources in fog computing in turn led to the formation of the service placement problem. In service placement, we are looking for the mapping of the requested services to the available nodes so that a set of Quality-of-Service objectives are satisfied. Since the problem is NP-hard, various methods have been proposed to solve it, each of which has its advantages and shortcomings. In this survey paper, while reviewing the most prominent state-of-the-art service placement methods by presenting a taxonomy based on their optimization strategy, the advantages, disadvantages, and applications of each category of methods are discussed. Consequently, recommendations for future works are presented.
      PubDate: 2024-08-01
       
  • Gradient scaling and segmented SoftMax Regression Federated Learning
           (GDS-SRFFL): a novel methodology for attack detection in industrial
           internet of things (IIoT) networks

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      Abstract: Abstract Industrial internet of things (IIoT) is considered as large-scale IoT-based network comprising of sensors, communication channels, and security protocols used in Industry 4.0 for diverse real-time operations. Industrial IoT (IIoT) networks are vulnerable to diverse cyber threats and attacks. Attack detection is the biggest security issue in the IIoT. Various traditional attack detection methods are proposed by several researchers but all are insufficient to protect privacy and security. To address the issue, a novel Gradient Descent Scaling and Segmented Regression Fine-tuned Federated Learning (GDS-SRFFL) method is introduced for IIoT network attack detection. The aim of the GDS-SRFFL method is to enhance the security of an IIoT network. Initially, the novelty of Gradient Descent Scaling-based preprocessing is applied to the raw dataset for obtaining feature feature-scaled preprocessed network sample. Then, the unwanted intrusions are discovered by using a Segmented Regression Fine-tuned Mini-batch Federated Learning model to ensure the protection of IoT networks with the novelty of SoftMax Regression. In order to validate the proposed methodology, experimentations were conducted on different parameters, namely accuracy, precision, recall, specificity, and attack detection time, and the results concluded that proposed GDS-SRFFL has improved accuracy by 10%, precision by 13%, recall by 10%, specificity by 11% as well as minimum attack detection time by 28% as compared to existing techniques like CNN + LSTM (Altunay and Albayrak in Eng Sci Technol Int J 38:101322, 2023, https://doi.org/10.1016/j.jestch.2022.101322), Enhanced Deep and Ensemble learning in SCADA-based IIoT network (Khan et al. in IEEE Trans Ind Inf 19(1):1030–1038, https://doi.org/10.1109/TII.2022.3190352), RNN (Ullah and Mahmoud in IEEE Access 10:62722–62750, 2022, https://doi.org/10.1109/ACCESS.2022.3176317), and other CNN methods. The proposed method “GDS-SRFFL” has overall accuracy of 89.42% as compared to other existing methods.
      PubDate: 2024-08-01
       
  • PnP-UGCSuperGlue: deep learning drone image matching algorithm for visual
           localization

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      Abstract: Abstract In response to the significant positioning errors that arise in visual localization algorithms for unmanned aerial vehicles (UAVs) when relying on drone image matching in areas devoid of satellite signals, we propose a deep learning-based algorithm named PnP-UGCSuperGlue. This algorithm employs a convolutional neural network (CNN) that is enhanced with a graph encoding module. The resulting enriched features contain vital information that refines the feature map and improves the overall accuracy of the visual localization process. The PnP-UGCSuperGlue framework initiates with the semantic feature extraction from both the real-time drone image and the geo-referenced image. This extraction process is facilitated by a CNN-based feature extractor. In the subsequent phase, a graph encoding module is integrated to aggregate the extracted features. This integration significantly enhances the quality of the generated feature keypoints and descriptors. Following this, a graph matching network is applied to leverage the generated descriptors, thereby facilitating a more precise feature point matching and filtering process. Ultimately, the perspective-n-point (PnP) method is utilized to calculate the rotation matrix and translation vector. This calculation is based on the results of the feature matching phase, as well as the camera intrinsic parameters and distortion coefficients. The proposed algorithm’s efficacy is validated through experimental evaluation, which demonstrates a mean absolute error of 0.0005 during the drone’s hovering state and 0.0083 during movement. These values indicate a significant reduction of 0.0010 and 0.0028, respectively, compared to the USuperGlue network.
      PubDate: 2024-08-01
       
  • Anomalies resolution and semantification of tabular data

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      Abstract: Abstract The fast growth of the web generates a significant amount of heterogeneous information such as images, text, audio, and video through various applications. These applications use different layouts to represent significant information. The layouts of table information are overloaded with anomalies that have given rise to intensive research into the semantification of web content and organizing tabular data for knowledge sharing and acquisition. Moreover, there are many anomalies present in tabular layouts that lead to the lack of semantic representation in tabular form and new challenges in data modeling. In this paper, we have discussed the various anomalies present in the tabular data that pertain to ontology learning and population tasks and provide the semantification of tabular data. To complete this task, (1) we provide the list of anomalies that pertain to semantification and provide the resolution to anomalies along with the semantification of tabular data, and (2) we have established the algorithm to interpret the table structure into a formal representation to analyze anomalies and provide the resolution. Furthermore, the proposed approach has been compared with existing approaches using ontology elements, the ability to resolve the anomalies, and the time complexity of the ontology population.
      PubDate: 2024-08-01
       
  • M2BIST-SPNet: RUL prediction for railway signaling electromechanical
           devices

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      Abstract: Abstract Railway signaling electromechanical devices (RSEDs) play a pivotal role in the railway industry. Normal wear and tear of these devices occur during day-and-night operation and even progressively develop into failures. Hence, it is imperative to predict the remaining useful life (RUL) for reliable services. However, there are three existing challenges in addressing the issue. To overcome these challenges, we introduce M \(^2\) BIST-SPNet for RSEDs RUL prediction. The model incorporates advanced spatiotemporal feature extraction mechanisms, including a spatial-temporal attention-based convolution networks (STACN), multiple branches with multi-scale multifrequency module (MBM), and communication module (CM). Extensive experiments conducted on three typical object datasets (i.e., railway safety relay, EMU contactor, switch circuit controller) shows that the proposed approach has demonstrated state-of-the-art performance in long-term multiparameter prediction and RUL prediction.
      PubDate: 2024-08-01
       
  • FGCF: fault-aware green computing framework in software-defined social
           internet of vehicle

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      Abstract: The social internet of vehicle (SIoV) is a specialized network combining intelligent sensing devices and vehicular communications to address traffic monitoring and resource management challenges in smart cities. Ensuring efficient and sustainable green computing with global network stability is crucial, especially in the dynamic environment of vehicular mobility. The software-defined-SIoV (SD-SIoV) architecture separates control and forwarding planes for centralized management. The architecture addresses green traffic data dissemination with heterogeneous traffic data by formulating control plane nodes’ election as an NP-Hard optimization problem, considering parameters, e.g., transmission distance, node’s residual energy, load imbalance, and mobility factor. The architecture incorporates the random way-point mobility (RWPM) model for simulating nodes’ mobility. The proposed improved energy-efficient gray wolf optimization (IEEGWO) algorithm enhances energy-efficiency by intelligently electing and re-electing optimal control plane nodes, jointly addressing load imbalance and fault-tolerance issues, ultimately improving green computing and communication performance in SD-SIoV. Comparative analysis with state-of-the-art demonstrates that IEEGWO provides significant green computing benefits in a real-time SIoV scenario Graphical
      PubDate: 2024-08-01
       
  • Order structure analysis of node importance based on the temporal
           inter-layer neighborhood homogeneity rate of the dynamic network

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      Abstract: Abstract The analysis of node order structure in dynamic temporal networks is significant for network propagation control. To further accurately characterize the inter-layer coupling relationship of dynamic temporal networks, this paper firstly defines the node neighborhood structure homogeneity rate and node neighborhood location heterogeneity rate based on the node neighborhood structure evolution feature information and node neighborhood location evolution feature information, and integrates the influence of the change in both neighborhood structure and neighborhood location on the node importance in the process of node temporal evolution. Secondly, a Supra-Adjacency Matrix based on Neighborhood Structure (NSAM) temporal network node importance order structure modeling method is proposed by combining the local structure and overall structure evolution information of nodes during the temporal evolution process. Finally, the node importance order structure of the dynamic temporal network is obtained by combining the eigenvector centrality to represent the node importance attribute value. Simulations show that compared with the classical hierarchical temporal network model, the NSAM model can improve the identification accuracy by 38.2% and 7.8%, respectively, and can identify the important nodes in the dynamic temporal network more effectively.
      PubDate: 2024-08-01
       
  • HyperTuner: a cross-layer multi-objective hyperparameter auto-tuning
           framework for data analytic services

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      Abstract: Abstract Hyperparameters optimization (HPO) is vital for machine learning models. Besides model accuracy, other tuning intentions such as model training time and energy consumption are also worthy of attention from data analytic service providers. Therefore, it is essential to take both model hyperparameters and system parameters into consideration to execute cross-layer multi-objective hyperparameter auto-tuning. Toward this challenging target, we propose HyperTuner in this paper which leverages a well-designed ADUMBO algorithm to find the Pareto-optimal configuration set. Compared with vanilla Bayesian optimization-based methods, ADUMBO selects the most promising configuration from the generated Pareto candidate set during each iteration via maximizing a novel adaptive uncertainty metric. We evaluate HyperTuner on our local distributed TensorFlow cluster, and experimental results show that it is always able to find a better Pareto configuration front superior in both convergence and diversity compared with the other four baseline algorithms. Besides, experiments with different training datasets, different optimization objectives, and different machine learning platforms verify that HyperTuner can well adapt to various data analytic service scenarios.
      PubDate: 2024-08-01
       
  • Intelligent detection method of microparticle virus in silkworm based on
           YOLOv8 improved algorithm

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      Abstract: Abstract The presence of microparticle viruses significantly impacts the quality of silkworm seeds for domestic sericulture, making their exclusion from detection in silkworm seed production crucial. Traditional methods for detecting microparticle viruses in silkworms, such as manual microscopic observation, molecular biology, and immunological approaches, are cumbersome and unable to achieve intelligent, batch real-time detection. To address this challenge, we employ the YOLOv8 algorithm in this paper. Firstly, NAM attention is introduced in the original algorithm’s Backbone component, allowing the model to extract more generic feature information. Secondly, ODConv replaces Conv in the Head component of the original algorithm, enhancing the model’s ability to identify microparticle viruses. Finally, NWD-LOSS modifies the CIoU loss of the original algorithm to obtain a more accurate prediction box. Experimental results demonstrate that the NN-YOLOv8 model outperforms mainstream detection algorithms in detecting silkworm microparticle diseases. With an average detection time of 22.6 milliseconds per image, the model shows promising prospects for future applications. This model improvement enhances detection efficiency and reduces human resource costs, effectively realizing detection intelligence.
      PubDate: 2024-08-01
       
  • Enhancing Grover’s search algorithm: a modified approach to increase the
           probability of good states

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      Abstract: Abstract This article introduces an enhancement to the Grover search algorithm to speed up computing the probability of finding good states. It suggests incorporating a rotation phase angle determined mathematically from the derivative of the model during the initial iteration. At each iteration, a new phase angle is computed and used in a rotation gate around \(y+z\) axis in the diffusion operator. The computed phase angles are optimized through an adaptive adjustment based on the estimated increasing ratio of the consecutive amplitudes. The findings indicate an average decrease of 28% in the required number of iterations resulting in a faster overall process and fewer number of quantum gates. For large search space, this improvement rises to 29.58%. Given the computational capabilities of the computer utilized for the simulation, the approach is applied to instances with up to 12 qubits or 4096 possible combination of search entries.
      PubDate: 2024-08-01
       
 
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  Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

COMPUTER SCIENCE (1305 journals)

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School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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