Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the ...
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Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They...
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Discovering associations between circular RNAs (circRNAs) and cellular drug sensitivity is essential for understanding drug efficacy and therapeutic resistance. Traditional experimental methods to verify such associat...
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In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the pow...
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In this study, we introduce and evaluate a novel extractive text summarization methodology, "SummarEyes," based on the visual interaction of the user with the text, using eye-tracking data, as opposed to the...
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In this study, we introduce and evaluate a novel extractive text summarization methodology, "SummarEyes," based on the visual interaction of the user with the text, using eye-tracking data, as opposed to the traditional approaches based on analysis of textual content only. We conducted a large-scale user study aiming to collect eye-tracking data while reading the text to be summarized. We utilized various user's implicit attention metrics to generate novel eye-tracking-based text summarization models and compared them both to eye-tracking models typically using only a single feature of the gaze duration and to traditional, as well as state-of-the-art summarization methods, based solely on textual features. The models' quality was evaluated in terms of ROUGE scores using intrinsic evaluation on the datasets we had generated, relating gaze behavior to personalized and DUC gold-standard summaries. The experimental results showed that "SummarEyes" significantly outperformed the other summarizers in predicting both the user's personalized summarization and the generic gold standard summaries. With the increasing availability of eye-tracking technology, this research can lead to a new generation of effective user-centric text summarization tools.
The development of wireless technology has triggered wireless sensing. Most WiFi sensing methods are data driven and learning based. They face two major drawbacks: 1) a large number of labeled samples are required to ...
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The development of wireless technology has triggered wireless sensing. Most WiFi sensing methods are data driven and learning based. They face two major drawbacks: 1) a large number of labeled samples are required to train the sensing model;and 2) the sensing model depends on the training environment and degrades dramatically in a different environment. To mitigate these problems, we propose a domain adaptation method to achieve environment robustness for channel-state-information-based activity recognition with sparsely labeled samples. The method, named cross-domain activity recognition (CDAR), consists of iterative soft labeling, domain alignment, and activity classification. To reduce the number of labeled samples, CDAR adopts dynamic time warping to measure the similarity between the samples, based on which the unlabeled samples are pseudo-labeled iteratively and progressively. To tolerate false labels, the pseudo-labels take the form of soft labels. To reduce the data distribution discrepancy, the domains are aligned by minimizing the intraclass distance and maximizing the interclass distance, using maximum mean discrepancy as the metric. The activities are finally classified by integrating convolutional neural network and bidirectional long short-term memory. Extensive experiments demonstrate the effectiveness of the method CDAR on activity recognition across people, locations, environmental dynamics, and rooms.
Due to a strong heterogeneity between two signals, it is often a challenging problem to obtain an analytical model between brain signals and joint movements. This paper proposes an approach to predicting joint movemen...
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Audio classification aims to discriminate between different audio signal types, and it has received intensive attention due to its wide applications. In deep learning-based audio classification methods, researchers us...
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Audio classification aims to discriminate between different audio signal types, and it has received intensive attention due to its wide applications. In deep learning-based audio classification methods, researchers usually transform the raw signal of audios into different feature representations (such as Short Time Fourier Transform and Mel Frequency Cepstral Coefficients) as the inputs of networks. However, selecting the feature representation requires expert knowledge and extensive experimental verification. Besides, using a single type of feature representation may cause suboptimal results as the information implied in different kinds of feature representations may be complementary. Previous works show that ensembling the networks trained on different representations can greatly boost classification performance. However, making inferences using multiple networks is cumbersome and computation expensive. In this paper, we propose a novel end-to-end collaborative training framework for the audio classification task. The framework takes multiple representations as inputs to train the networks jointly with a knowledge distillation method. Consequently, our framework significantly promotes the performance of networks without increasing the computational overhead in the inference stage. Extensive experimental results demonstrate that the proposed approach improves classification performance and achieves competitive results on both acoustic scene classification tasks and general audio tagging tasks.
The eyes are the windows to the soul and are crucial for studying human behavior. Therefore, gaze estimation has attracted much attention in the field of computer vision. In recent years, convolutional neural networks...
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Currently, Bangladesh is among the countries with the highest levels of air pollution in the world. Major cities of Bangladesh experience severe air pollution, significantly impacting public health and the environment...
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