In application software development, memory defects are difficult to detect. Traditional memory defect detection tools generally face issues of high performance overhead and excessive memory consumption, which limits ...
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With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource ***,applying this tec...
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With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource ***,applying this technique to multimodal knowledge transfer introduces a significant challenge:ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task *** paper introduces UniTrans,a framework aimed at facilitating efficient knowledge transfer across multiple *** leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter *** further enhance modality alignment,we introduce two key components:the Multimodal Consistency Alignment Module and the Query-Augmentation Side Network,specifically optimized for scenarios with extremely limited trainable *** evaluations on various cross-modal downstream tasks demonstrate that our approach surpasses state-of-the-art methods while using just 5%of their trainable ***,it achieves superior performance compared to fully fine-tuned models on certain benchmarks.
Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that c...
Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that conflict with reality due to the unreliable distribution of facts within their training data, which is particularly critical for applications requiring high credibility and accuracy [3].
The Russia-Ukraine conflict represents the first regional military confrontation between major (and external) powers that is thoroughly interspersed with cyber operations throughout the main military theater. In essen...
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This paper primarily analyzes the impact of tester capabilities on the quality of aerospace software testing and elaborates on how to assess tester abilities from both technical and cognitive perspectives. The researc...
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Model compression frequently leverages model pruning, an approach aimed at minimizing neural network size without compromising performance. Prior research has demonstrated the effectiveness of iterative pruning combin...
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The Tyche Embedded Operating System is a highly customized real-time embedded operating system, widely applied in fields such as aerospace, medical devices, and industrial automation. Renowned for its robust support f...
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Flying Ad-Hoc Networks (FANETs) enable the deployment of Unmanned aerial vehicles (UAVs) in a clustered manner to execute complex missions, yet their inherent openness poses significant challenges to communication sec...
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The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted r...
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With the development of society and the advancement of technology, security and defense issues have become increasingly prominent. Traditional disposal methods are no longer able to meet the current complex and ever-c...
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