With the increasing usage of cloud computing in many fields, concerns about the secrecy of data storage in the cloud have been growing. Many types of data are stored in cloud computing, such as text, images, audio, vi...
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Spark performs excellently in large-scale data-parallel computing and iterative ***,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demands of resource u...
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Spark performs excellently in large-scale data-parallel computing and iterative ***,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demands of resource utilization and performance *** strategy optimization,as a key direction for improving Spark’s execution efficiency,has attracted widespread *** paper first introduces the basic theories of Spark,compares several default scheduling strategies,and discusses common scheduling performance evaluation indicators and factors affecting scheduling ***,existing scheduling optimization schemes are summarized based on three scheduling modes:load characteristics,cluster characteristics,and matching of both,and representative algorithms are analyzed in terms of performance indicators and applicable scenarios,comparing the advantages and disadvantages of different scheduling *** article also explores in detail the integration of Spark scheduling strategies with specific application scenarios and the challenges in production ***,the limitations of the existing schemes are analyzed,and prospects are envisioned.
Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD di...
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Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class *** study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature *** study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and *** proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD *** ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.
Image denoising is an important area of computer vision. Rudin-Osher-Fatemi model based on a gradient is one of the simplest models used in image denoising to solve the problem of restoring the clear image. The challe...
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Mining subgraphs with interesting structural properties from networks (or graphs) is a computationally challenging task. In this paper, we propose two algorithms for enumerating all connected induced subgraphs of a gi...
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Mining subgraphs with interesting structural properties from networks (or graphs) is a computationally challenging task. In this paper, we propose two algorithms for enumerating all connected induced subgraphs of a given cardinality from networks (or connected undirected graphs in networks). The first algorithm is a variant of a previous wellknown algorithm. The algorithm enumerates all connected induced subgraphs of cardinality k in a bottom-up manner. Thedata structures that lead to unit time element checking and linear space are presented. Different from previous algorithmsthat work in either a bottom-up manner or a reverse search manner, an algorithm that enumerates all connected inducedsubgraphs of cardinality k in a top-down manner is proposed. The correctness and complexity of the top-down algorithmare theoretically analyzed and proven. In the experiments, we evaluate the efficiency of the algorithms using a set of realworld networks from various fields. Experimental results show that the variant bottom-up algorithm outperforms thestate-of-the-art algorithms for enumerating connected induced subgraphs of small cardinality, and the top-down algorithmcan achieve an order of magnitude speedup over the state-of-the-art algorithms for enumerating connected induced subgraphs of large cardinality.
Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but th...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.
Polysemy is a common phenomenon in linguistics. Quantum-inspired complex word embeddings based on Semantic Hilbert Space play an important role in natural language processing, which may accurately define a genuine pro...
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Polysemy is a common phenomenon in linguistics. Quantum-inspired complex word embeddings based on Semantic Hilbert Space play an important role in natural language processing, which may accurately define a genuine probability distribution over the word space. The existing quantum-inspired works manipulate on the real-valued vectors to compose the complex-valued word embeddings, which lack direct complex-valued pre-trained word representations. Motivated by quantum-inspired complex word embeddings, we propose a complex-valued pre-trained word embedding based on density matrices, called Word2State. Unlike the existing static word embeddings, our proposed model can provide non-linear semantic composition in the form of amplitude and phase, which also defines an authentic probabilistic distribution. We evaluate this model on twelve datasets from the word similarity task and six datasets from the relevant downstream tasks. The experimental results on different tasks demonstrate that our proposed pre-trained word embedding can capture richer semantic information and exhibit greater flexibility in expressing uncertainty.
In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mo...
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In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.
Biometric systems are a continuously evolving and promising technological domain that can be used in automatic systems for the unique and efficient identification and authentication of individuals without necessitatin...
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Biometric systems are a continuously evolving and promising technological domain that can be used in automatic systems for the unique and efficient identification and authentication of individuals without necessitating users to carry or remember any physical tokens or passwords, in contrast to traditional methods such as password IDs. Biometrics are biological measurements or physical characteristics that can be used to ascertain and validate the identity of individuals. Recently, considerable interest has emerged in exploiting brain activity as a biometric identifier in automatic recognition systems, particularly focusing on data acquired through electroencephalography (EEG). Multiple research endeavors have indeed confirmed the presence of discriminative characteristics within brain signals recorded while performing specific cognitive tasks. However, EEG signals are inherently complex due to their nonstationary and high-dimensional properties, thus demanding careful consideration during both the feature extraction and classification processes. This study applied a hybridization technique integrating a pretrained convolutional neural network (CNN) with a classical classifier and the short-time Fourier transform (STFT) spectrum. We used a hybrid model to decode two-class motor imagery (MI) signals for mobile biometric authentication tasks, which include subject identification and lock and unlock classification. To this purpose, nine potential classifiers (mostly classification algorithms) were utilized to build nine distinct hybrid models, with the ultimate goal of selecting the most effective one. Practically, six experiments were conducted in the experimental part of this study. The first experiment aims to develop a hybrid model for biometric authentication tasks. To do this, nine possible classifiers (mostly classification algorithms) were used to build nine hybrid models. It can be seen that the RF-VGG model achieved better performance compared with other model
Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shorte...
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Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shortest distances from the edge to all the other *** betweenness centrality ranks which edges are significant based on the fraction of all-pairs shortest paths that pass through the ***,extensive research efforts go into centrality computation over general graphs that omit time ***,numerous real-world networks are modeled as temporal graphs,where the nodes are related to each other at different time *** temporal property is important and should not be neglected because it guides the flow of information in the *** state of affairs motivates the paper’s study of edge centrality computation methods on temporal *** introduce the concepts of the label,and label dominance relation,and then propose multi-thread parallel labeling-based methods on OpenMP to efficiently compute edge closeness and betweenness centralities *** types of optimal temporal *** edge closeness centrality computation,a time segmentation strategy and two observations are presented to aggregate some related temporal edges for uniform *** edge betweenness centrality computation,to improve efficiency,temporal edge dependency formulas,a labeling-based forward-backward scanning strategy,and a compression-based optimization method are further proposed to iteratively accumulate centrality *** experiments using 13 real temporal graphs are conducted to provide detailed insights into the efficiency and effectiveness of the proposed *** with state-ofthe-art methods,labeling-based methods are capable of up to two orders of magnitude speedup.
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