Reducing the production makespan of modern small-batch, multi-variety jobs can be abstracted into a flexible job shop scheduling problem (FJSP). The organization can greatly improve production efficiency by scheduling...
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Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying nece...
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Blockchain is experiencing the transition from the first generation to the second generation, and smart contract is the symbol of the second generation blockchain. Under the background of the explosive growth of the s...
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Coherence is a crucial aspect of evaluating text readability and can be assessed through two primary factors when evaluating an essay in a scoring scenario. The first factor is logical coherence, characterized by the ...
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We propose a novel smart contract re-entry vulnerability detection model based on BiGAS. The model combines a BiGRU neural network that introduces an attention mechanism with an SVM. We start from the data features of...
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The indicator-based multi-objective optimization evolutionary algorithm has gained significant attention for its strong performance across various optimization problems. We draw on the advantages and try to make impro...
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Many-objective optimization problems are challenging for the majority of extant multi-objective evolutionary algorithms to solve because they are unable to strike a balance between diversity and convergence in the hig...
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Visible-infrared person re-identification (VI-ReID) is a cross-modality fine-grained classification task. Existing approaches for VI-ReID mainly explore modality-invariant features for person retrieval. However, modal...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Visible-infrared person re-identification (VI-ReID) is a cross-modality fine-grained classification task. Existing approaches for VI-ReID mainly explore modality-invariant features for person retrieval. However, modality-invariant features pay more attention to global contexts, due to the lack of texture information in infrared images. This leads to a person with similar silhouette often being misidentified. Targeting this problem, this paper innovatively introduces natural language specification to learn global-local contexts for VI-ReID. Specifically, our framework jointly optimizes visible-infrared alignment (VIA) and visual-textual reasoning (VTR). VIA achieves cross-modal between RGB and IR. It can explicitly utilize designed modality-guided alignment and relationship-reinforced fusion to explore the potential of local cues in global features. VTR proposes the pooling selection and dual-level reasoning mechanisms to force the image encoder to pay attention to significant regions based on textual descriptions. Extensive experimental results on the popular SYSU-MM01 and RegDB datasets show that the proposed method significantly outperforms state-of-the-art approaches.
Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses sub...
With the arrival of the big data era,the phenomenon of information overload is becoming increasingly *** response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering ...
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With the arrival of the big data era,the phenomenon of information overload is becoming increasingly *** response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this ***,a profile labeling system was constructed based on user *** study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user ***-IDF algorithm was used to determine the weights of user-item feature ***,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these ***,personalized recommendations were generated using Top-N *** with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).
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