Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome the...
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Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performa...
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Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new ...
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Detection of road networks using high-resolution aerial or remote sensing imagery constitutes a significant focus within modern research efforts. Currently, deep learning models demonstrate efficiency to a certain deg...
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computer vision is witnessing a surge of interest in machines accurately recognizing and interpreting human emotions through facial expression analysis. However, variations in image properties such as brightness, cont...
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Acute myocardial infarction remains the leading cause of mortality in both developed and developing countries. This severe medical condition requires prompt hospitalization and often requires immediate diagnosis and t...
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Pre-training on a large dataset such as ImageNet followed by supervised fine-tuning has brought success in various deep learning-based tasks. However, the modalities of natural images and ultrasound images have consid...
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A key component of behavior analysis and human-computer interaction (HCI) is facial expression detection, which helps systems understand and react to human emotions more effectively and nuancedly. While previous resea...
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In the realm of cloud computing, querying on secured information has emerged as a technology of critical importance. These kinds of searches make it possible for the owner of the data to search through encrypted data ...
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ISBN:
(纸本)9798350361780
In the realm of cloud computing, querying on secured information has emerged as a technology of critical importance. These kinds of searches make it possible for the owner of the data to search through encrypted data that is kept in the cloud without revealing any pertinent information. Numerous strategies, the most of which concentrate on conjunctive and disjunctive keyword searches, have been presented by researchers in an effort to improve the quality of the search experience. On the other hand, a disjunction will provide an excessive number of results, but a conjunction of all the keywords could only produce a small number of results. It is tough to customize the relevance of the keywords given the present schemes, which makes it difficult to acquire the results that are required. For the purpose of resolving these issues, we offer a unique technique that we name the Secured Multiple Keyword Searching Scheme (SMKSS). This scheme allows for the search to be conducted with the user specifying the amount of keywords that are included in the search result. A cross-validation is performed between the proposed scheme and the traditional Ranked Keyword Searching Model (RKSM) in order to determine how effective the novel scheme is. This particular quantity, n, can be utilized to personalize the relevance of the term. Furthermore, as a consequence of this, the owner of the data might acquire the desired search results that include any n terms from a keyword collection. Not only does the proposed system allow the conventional disjunctive and conjunctive keyword searches, but it also supports these searches when n equals one or the size of the keyword collection, respectively. There is a possibility that the keyword will be critical. Initially, we provide a formal definition of its security, and then we demonstrate that the proposed scheme is safe against the adaptive selected keyword attack in the standard model, and that it is also capable of providing some degree of def
Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary...
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Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.
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