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.
Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic mod...
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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 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.
Social media growth was fast because many people used it to express their feelings, share information, and interact with others. With the growth of social media, many researchers are interested in using social media d...
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
Driver behavior has a major role in many of the unpleasant things that happen when driving, such as crashes or accidents, heavy traffic, abrupt braking, and acceleration and deceleration. Numerous investigations have ...
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Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theo...
Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.
Video watermarking plays a crucial role in protecting intellectual property rights and ensuring content *** study delves into the integration of Galois Field(GF)multiplication tables,especially GF(2^(4)),and their int...
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Video watermarking plays a crucial role in protecting intellectual property rights and ensuring content *** study delves into the integration of Galois Field(GF)multiplication tables,especially GF(2^(4)),and their interaction with distinct irreducible *** primary aim is to enhance watermarking techniques for achieving imperceptibility,robustness,and efficient execution *** research employs scene selection and adaptive thresholding techniques to streamline the watermarking *** selection is used strategically to embed watermarks in the most vital frames of the video,while adaptive thresholding methods ensure that the watermarking process adheres to imperceptibility criteria,maintaining the video's visual ***,careful consideration is given to execution time,crucial in real-world scenarios,to balance efficiency and *** Peak Signal-to-Noise Ratio(PSNR)serves as a pivotal metric to gauge the watermark's imperceptibility and video *** study explores various irreducible polynomials,navigating the trade-offs between computational efficiency and watermark *** parallel,the study pays careful attention to the execution time,a paramount consideration in real-world scenarios,to strike a balance between efficiency and *** comprehensive analysis provides valuable insights into the interplay of GF multiplication tables,diverse irreducible polynomials,scene selection,adaptive thresholding,imperceptibility,and execution *** evaluation of the proposed algorithm's robustness was conducted using PSNR and NC metrics,and it was subjected to assessment under the impact of five distinct attack *** findings contribute to the development of watermarking strategies that balance imperceptibility,robustness,and processing efficiency,enhancing the field's practicality and effectiveness.
The detection of community structures in complex networks has garnered significant attention in recent years. Given its NP-hardness, numerous evolutionary optimization-based approaches have been proposed. However, the...
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