Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understa...
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In the era of big data, data redundancy has become an obstacle to deep reading. The objective of linked data as a new data organization model is to transform data into structured data following unified standards. The ...
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In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ...
In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks.
Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With t...
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Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit...
Metrics have emerged as an important tool for quantitatively evaluating researchers from a variety of perspectives,including research impact,research quality,interdisciplinarity,and *** in the field of library and inf...
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Metrics have emerged as an important tool for quantitatively evaluating researchers from a variety of perspectives,including research impact,research quality,interdisciplinarity,and *** in the field of library and information science,many previous studies have highlighted the characteristics of researchers in this ***,only a minority of the studies address the aspect of diversity in research *** purpose of this study is to(1)evaluate the topic diversity of researchers in library and information science and(2)examine the relationships between the researcher topic diversity and research *** propose an indicator to quantify author topic diversity,which we refer to as author topic diversity(ATD).Latent Dirichlet Allocation(LDA)is used to detect topics in the field,while cosine similarity is used to calculate the diversity of research topics in a given researcher’s *** results show that topic diversity in the field of library and information science varies greatly from author to *** addition,weak positive correlations are found between the ATD and citation indicators,suggesting that engaging in diversified topics may lead to higher research impact.
Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation. However, when applied to Text-to-SQL datasets, it often fails to improve performance an...
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Stereo Image Super-Resolution (SSR) holds great promise in improving the quality of stereo images by exploiting the complementary information between left and right views. Most SSR methods primarily focus on the inter...
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Stereo Image Super-Resolution (SSR) holds great promise in improving the quality of stereo images by exploiting the complementary information between left and right views. Most SSR methods primarily focus on the inter-view correspondences in low-resolution (LR) space. The potential of referencing a high-quality SR image of one view benefits the SR for the other is often overlooked, while those with abundant textures contribute to accurate correspondences. Therefore, we propose Reference-based Iterative Interaction (RIISSR), which utilizes reference-based iterative pixel-wise and patch-wise matching, dubbed $P^{2}$ -Matching, to establish cross-view and cross-resolution correspondences for SSR. Specifically, we first design the information perception block (IPB) cascaded in parallel to extract hierarchical contextualized features for different views. Pixel-wise matching is embedded between two parallel IPBs to exploit cross-view interaction in LR space. Iterative patch-wise matching is then executed by utilizing the SR stereo pair as another mutual reference, capitalizing on the cross-scale patch recurrence property to learn high-resolution (HR) correspondences for SSR performance. Moreover, we introduce the supervised side-out modulator (SSOM) to re-weight local intra-view features and produce intermediate SR images, which seamlessly bridge two matching mechanisms. Experimental results demonstrate the superiority of RIISSR against existing state-of-the-art methods.
Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modificati...
Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper in...
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