This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL di...
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One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state sp...
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The proliferation of Internet of Things (IoT) and the adoption of Edge Computing paradigms have led to a substantial increase in data volume. Data imbalance is inevitable in distributed environment composed of edge no...
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The proliferation of Internet of Things (IoT) and the adoption of Edge Computing paradigms have led to a substantial increase in data volume. Data imbalance is inevitable in distributed environment composed of edge nodes, due to inherent diversity in processing capabilities, network connectivity, and geographical distribution. In specific highway domain, such edge derived data makes predictive precision of crucial traffic flow hard to guarantee. First, data imbalance would deteriorate the predictive performance at certain locations. Second, various spatial dependencies existed in highway network have not been sufficiently explored. To address these challenges in highway domain, we propose a novel method for daily traffic flow prediction on imbalanced data. On the one hand, a pre-processing strategy is presented to normalize traffic flow data in long-tail distribution. The data is collected from highway electronics through edge nodes. On the other hand, a multi-graph convolution model is designed to capture spatio-temporal features in distinct physical, statistical and latent perspectives. Incorporating external characteristics like meteorological and calendar features, prediction can be achieved precisely. Through extensive experiments and case studies conducted on a Chinese provincial highway, our method demonstrates a significant improvement of predictive accuracy than baselines and outstanding effects in a practical project. IEEE
We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a power-line communication (PLC) based Internet-of-Things system in the presence of an eavesdropper. We consider a pi...
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Radiology reports are crucial for bridging the expertise of radiologists and other clinicians. Machine Learning models trained on these reports have shown promising performance in various downstream clinical tasks, su...
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In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective...
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In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)
Corneal endothelial cell segmentation of the microscope image is critical for clinical parameters quantification. However, the low-contrast regions are ambiguous and hard to segment. The uncertainty has been proved to...
Corneal endothelial cell segmentation of the microscope image is critical for clinical parameters quantification. However, the low-contrast regions are ambiguous and hard to segment. The uncertainty has been proved to be effective for detecting the ambiguous regions. Besides, spatial attention can guide the model to focus on the region of interest during the training process. This paper proposes an Uncertainty Guided Network (UG-Net) for corneal endothelial cell segmentation based on uncertainty estimation and spatial attention. We first use Bayesian approximation to obtain aleatoric and epistemic uncertainty maps. Then, two uncertainty maps are utilized to guide the model to focus on the low-contrast regions with uncertainty-based soft spatial attention. Experimental results show that the proposed method performs better than other state-of-the-art methods.
Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution...
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Phishing emails are a significant threat to organizations, with over $90 \%$ of cyber attacks starting from a malicious email. Despite built-in security measures, relying solely on these defenses can leave organizatio...
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ISBN:
(数字)9798331540906
ISBN:
(纸本)9798331540913
Phishing emails are a significant threat to organizations, with over $90 \%$ of cyber attacks starting from a malicious email. Despite built-in security measures, relying solely on these defenses can leave organizations vulnerable to cybercriminals who exploit human nature and the lack of tight security. Phishing emails, designed to deceive recipients into disclosing personal and financial information, represent a significant cybersecurity challenge. This paper introduces a comprehensive dataset curated explicitly for detecting phishing emails, featuring a collection of authentic and phishing emails. The dataset includes a broad spectrum of phishing techniques, such as sophisticated social engineering tactics, impersonation of reputable entities, and using urgent or threatening language to manipulate recipients. Phishing emails were collected to cover various scenarios, including financial fraud, account verification, and malware dissemination attempts. Our analysis involves a range of classical machine learning models alongside exploratory analysis with LLMs. The performance of these models was rigorously evaluated to furnish a comparative analysis of their detection capabilities. The dataset, one of the largest of its kind, offers a significant resource for researchers and cybersecurity professionals aiming to advance phishing detection methods. The dataset used in this research is publicly available, enabling further exploration and replication of the findings by the research community [1].
Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many co...
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