Continuous and patterned FePt films (40 nm) were fabricated on silicon (100) substrates using deep ultraviolet lithography with the wavelength of 248 nm followed by sputter deposition or pulsed laser deposition at roo...
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Recent multi-modal fake news detection methods often use the consistency between textual and visual contents to determine the truth or fake of a news information. Higher levels of textual-visual consistency typically ...
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Recent multi-modal fake news detection methods often use the consistency between textual and visual contents to determine the truth or fake of a news information. Higher levels of textual-visual consistency typically lead to a greater likelihood of classifying a news item as real. However, a critical observation reveals that creators of most fake news intentionally select images that align with the textual content, thereby enhancing the credibility of the news. Consequently, high consistency between textual and visual contents alone cannot guarantee the authenticity of the information. To address this problem, we introduce a novel approach termed Multimodal Consistency-based Suppression Factor to modulate the significance of textual-visual consistency in information assessment. When the textual-visual matching is high, this suppression factor reduces the influence of consistency during the judgment process. Moreover, we use Contrastive Language-Image Pre-training (CLIP) model to extract features and measure the consistency level between modalities to guide multimodal fusion. In addition, we also use a method of compressing and fusing modal information based on Variational Autoencoder (VAE) to reconstruct CLIP features, learning the shared representation of different modal information of CLIP. Finally, extensive experiments were conducted on three publicly datasets, Weibo, Twitter and Weibo21, and the results confirmed that our method outperformed the state-of-the-art methods in the field, and had 0.8%, 2.6% and 4.1% effect improvement on the accuracy rate.
In the Internet of Things (IoT) era, the pervasive application of tremendous end devices puts forth an unprecedented demand for data processing. To address this challenge, the end-edge-cloud system has emerged as a so...
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In the Internet of Things (IoT) era, the pervasive application of tremendous end devices puts forth an unprecedented demand for data processing. To address this challenge, the end-edge-cloud system has emerged as a solution, where task offloading plays a crucial role in efficiently allocating computing resources. Meanwhile, driven by the growing social awareness of privacy, privacy-aware task offloading methods have attracted significant attention. However, existing privacy-aware task offloading methods face various limitations, such as being applicable to specific scenarios, poor transfer ability of offloading strategies, etc. This paper studies the privacy-aware task offloading problem in the end-edge-cloud system and proposes PATO, a Privacy-Aware Task Offloading strategy. PATO consists of two core modules. Specifically, a novel self-supervised feature mapping module transforms sensitive information via complex unidirectional mapping. Subsequently, a DRL-based decision-making module is trained to utilize transformed information to make task offloading decisions. Subtly combining the self-supervised feature mapping module and the DRL-based decision-making module, the proposed PATO addresses both privacy protection and task offloading challenges. Furthermore, PATO is designed as a general solution for task offloading problems and exhibits good transfer ability.
resting for applications. Great attention is devoted to novel devices with high accuracy, longevity and extended possibilities to work in wide temperature and pressure ranges, aggressive media, etc., which show improv...
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ISBN:
(数字)9783319560625
ISBN:
(纸本)9783319560618;9783319858180
resting for applications. Great attention is devoted to novel devices with high accuracy, longevity and extended possibilities to work in wide temperature and pressure ranges, aggressive media, etc., which show improved characteristics, defined by the developed materials and composites, opening new possibilities to study different physico-mechanical processes and phenomena.
Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived...
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Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived bias and then constrain the classification space, and (2) the use of general hallucination techniques based on global features fails to escape the limited classification space, resulting in suboptimal improvements. To solve these issues, this paper proposes an interventional feature generation (IFG) method. Specifically, we first use the relations of the categories or instances as interventional operations to implicitly constrain the feature representations (pre-trained knowledge) into different classification subsets. Then, we employ a parameter-free feature generation strategy to enrich each subset’s training samples of the support category. In other words, IFG provides a multi-subsets learning strategy to reduce the influence of perceived bias, enrich the diversity of generated features, and improve the robustness of the few-shot classifier. We apply our method to four benchmark datasets and observe state-of-the-art performance across all experiments. Specifically, compared to the baseline on the Mini-ImageNet dataset, our approach yields accuracy improvements of 6.03% and 3.46% for 1 and 5 support training samples, respectively. Furthermore, the proposed interventional feature generation technique can improve classifier performance in other FSL methods, demonstrating its versatility and potential for broader applications. The code is available at https://***/ShuoWangCS/IFG-FSL/.
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