The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received c...
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The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing
From the perspective of resource-theoretic approach,this study explores the quantification of imaginary in quantum *** propose a well defined measure of imaginarity,the geometric-like measure of *** with the usual geo...
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From the perspective of resource-theoretic approach,this study explores the quantification of imaginary in quantum *** propose a well defined measure of imaginarity,the geometric-like measure of *** with the usual geometric imaginarity measure,this geometric-like measure of imaginarity exhibits smaller decay difference under quantum noisy channels and higher *** applications,we show that both the optimal probability of state transformations from a pure state to an arbitrary mixed state via real operations,and the maximal probability of stochastic-approximate state transformations from a pure state to an arbitrary mixed state via real operations with a given fidelity f,are given by the geometric-like measure of imaginarity.
ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential sec...
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ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.
With the rapid development of web technology, Social Networks(SNs) have become one of the most popular platforms for users to exchange views and to express their emotions. More and more people are used to commenting o...
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With the rapid development of web technology, Social Networks(SNs) have become one of the most popular platforms for users to exchange views and to express their emotions. More and more people are used to commenting on a certain hot spot in SNs, resulting in a large amount of texts containing emotions. Textual Emotion Cause Extraction(TECE) aims to automatically extract causes for a certain emotion in texts, which is an important research issue in natural language processing. It is different from the previous tasks of emotion recognition and emotion classification. In addition, it is not limited to the shallow-level emotion classification of text, but to trace the emotion source. In this paper, we provide a survey for TECE. First, we introduce the development process and classification of TECE. Then, we discuss the existing methods and key factors for TECE. Finally, we enumerate the challenges and developing trend for TECE.
Multi-View Stereo (MVS) is a long-standing and fundamental task in computer vision, which aims to reconstruct the 3D geometry of a scene from a set of overlapping images. With known camera parameters, MVS matches pixe...
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A knowledge graph (KG) is a form of representing knowledge of the objective world. With the expansion of knowledge, KGs frequently incorporate new entities, which often possess limited associated data, known as few-sh...
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In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors...
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1 Introduction Recently,multiple synthetic and real-world datasets have been built to facilitate the training of deep single-image reflection removal(SIRR)***,diverse testing sets are also provided with different type...
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1 Introduction Recently,multiple synthetic and real-world datasets have been built to facilitate the training of deep single-image reflection removal(SIRR)***,diverse testing sets are also provided with different types of reflections and ***,the non-negligible domain gaps between training and testing sets make it difficult to learn deep models generalizing well to testing *** diversity of reflections and scenes further makes it a mission impossible to learn a single model being effective for all testing sets and real-world *** this paper,we tackle these issues by learning SIRR models from a domain generalization perspective.
Traditional instruction selection methods fail to fully exploit the very long instruction word (VLIW) architecture’s efficient scalar instructions. We propose an optimized instruction selection method based on classi...
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The Cuckoo Search algorithm (CS) still has some shortcomings, such as insufficient global search capability, challenges in exploring values beyond local optima, and underutilization of effective information within pop...
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