This research paper addresses the enhancement of data security and privacy in cloud storage through diverse encryption methods, such as one-to-many encryption, data integrity, resilient data deletion, and privacy-pres...
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Presently, with the quantity of data developing dramatically, the judicious utilization of large data has become the focal point of ventures to serve the future and settle on better choices. Utilizing Machine Leaning ...
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Requirements form the basis for defining software systems’ obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perform acceptance tests. However, despit...
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Omission and reference frequently occur in dialogues, which complicate the semantic structure of the dialogue and hinder the understanding of dialogue systems. Facing the challenge, researchers propose a new task, Inc...
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Omission and reference frequently occur in dialogues, which complicate the semantic structure of the dialogue and hinder the understanding of dialogue systems. Facing the challenge, researchers propose a new task, Incomplete Utterance Rewriting (IUR). IUR systems supplement information for utterances in a dialogue based on the previous context. By utilizing the substitutive nature of IUR, action-based models have recently led to much development on IUR compared to conventional sequence-to-sequence models. The current state-of-the-art method models IUR in a connected region locating scenario. As a result, the heavy burden for training and decoding hinders the efficiency significantly, especially when the dialogue context length increases. Thus, towards faster and more accurate IUR, this task should be modeled more naturally in a pair locating form where pairwise scorers locate pairs representing span and substitution relations. Following this idea, we propose a Cross Scorer Sharing (XSS) model to initially support pair locating. We experiment with XSS on both English and Chinese IUR datasets, and results have shown that our model leads to comparable or better performance than the previous state-of-the-art. For efficiency, our method is 132% faster when training and 85.1% faster when predicting. IEEE
Even if more and more high-quality public datasets are available, one of the biggest problems with deep learning for skin lesion diagnosis is the scarcity of training samples. Deep Convolutional Neural Networks (CNNs)...
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To enhance the capabilities of advanced video coding for emerging applications, the AVS3 standard has been introduced to double the coding efficiency compared to its predecessor, the AVS2 standard. It incorporates sop...
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Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable an...
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Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** thi...
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Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and *** Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data *** this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare *** experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)*** experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.
Monocular depth estimation (MDE) is an important task in computer vision, it enables a range of applications like robotic navigation, augmented reality and also used in surgical guidance. This paper shows the use of V...
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Unsupervised domain adaptation (UDA), aimed at improving the segmentation performance of deep models on un-labeled data, has attracted considerable attention. Recently, the Segment Anything Model (SAM) has gained wide...
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