this paper presents a novel Convolutional based Temporal Attention (CBTA) module that improves the performance of temporal convolutional networks (TCN) in lipreading tasks without requiring any additional data. Our CB...
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the widespread application of OTA technology has promoted the process of software-defined vehicles. In order to achieve the development of OTA cloud platform and vehicle terminal communication links and meet the authe...
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the use of piles for the construction of structures is a common practice to provide greater support. the issues that typically arise when employing this type of deep foundations stem from the soil-structure interactio...
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the proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inco...
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Shell programming is widely used to accomplish various tasks in Unix and Linux platforms. However, the large number of shell commands available, e.g., 50,000+ commands are documented in the Ubuntu Manual Pages (MPs), ...
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ISBN:
(纸本)9798350322637
Shell programming is widely used to accomplish various tasks in Unix and Linux platforms. However, the large number of shell commands available, e.g., 50,000+ commands are documented in the Ubuntu Manual Pages (MPs), makes it a big challenge for programmers to find appropriate commands for a task. Although there are some tutorials (e.g., TLDR) with examples manually created to address the challenge, the tutorials only cover a limited number of frequently used commands for shell beginners and provide limited support for users to search commands by a task. In this paper, we introduce a novel web-based tool, ShellFusion, which can automatically generate comprehensive answers (including relevant commands, scripts, and explanations) for shell programming tasks by fusing multi-source knowledge mined from Q&A posts, Ubuntu MPs, and TLDR tutorials. Our evaluation on 434 shell programming tasks shows that ShellFusion significantly outperforms the state-of-the-art approaches by at least 179.6% in terms of MRR@K and MAP@K. A user study conducted with 20 shell programmers further shows that ShellFusion can help users address programming tasks more efficiently and accurately.
Compared to single-energy system, multi-energy system has the advantages of high energy utilization and environmental friendliness. Multi-energy microgrid becomes a more advanced form of microgrid, consisting of elect...
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this paper explores the integration of Multi-Agent Systems (MAS) and Large Language Models (LLMs) for auto-matic code generation, addressing the limitations of traditional manual coding. By conducting a comprehensive ...
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To address the limitations of existing code defect detection methods, which only analyze code defects from a single perspective and produce uninterpretable results, we introduce a new approach known as Defect Detectio...
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the increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers mod...
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ISBN:
(纸本)9798400716379
the increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's performance while taking crucial features and specific requirements into account still poses a significant challenge. Existing methods have focused on worker-task assignments based on crowdsourcing, they neglect the scenario-dataset-model assignment problem. To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a heterogeneous information framework that can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. To comprehensively evaluate models, a new score function that utilizes multi-head attention mechanisms is proposed. Moreover, a novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching. Six popular traffic scenarios are selected as study cases and extensive experiments are conducted on a dataset to verify the effectiveness and efficiency of SMAP and the score function.
the rise of ARM-based applications and technologies has revolutionized the computing world, with its energy-efficient processors and flexible design. However, their acceptance is still hindered by several challenges, ...
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