As a distributed machine learning paradigm, vertical federated learning enables multiple passive parties with distinct features and an active party with labels to train a model collaboratively. Although it has been wi...
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As a distributed machine learning paradigm, vertical federated learning enables multiple passive parties with distinct features and an active party with labels to train a model collaboratively. Although it has been widely applied for its ability to protect privacy to some extent, this paradigm still faces various threats, especially the label inference attack (LIA). In this paper, we present the first observation of the disparity in LIAs resulting from differences in feature distribution among passive parties. To substantiate this, we study four different types of LIAs across five benchmark datasets, investigating the potential influencing factors and their combined impact. The results show that attack performance disparities can vary up to 15 times among different passive parties. So, how to eliminate this disparity? We explore methods from both attack and defense perspectives, including learning rate adjustment and noise perturbation with differential privacy. Our findings indicate that a modest increase in the learning rate of the passive party effectively enhances the LIA performance. In light of these, we propose a novel defense strategy that identifies passive parties with important features and applies adaptive noise to their gradients. Experiments show that it effectively reduces both attack disparity among passive parties and overall attack accuracy, while maintaining low computational complexity and avoiding additional communication overhead. Our code is publicly accessible at https://***/WWlnZSBMaXU/Attackers-Are-Not-the-Same.
The increasing popularity of crowdsourcing has resulted in the emergence of multiple crowdsourcing service providers (CSPs), such as Mechnical Turk and Crowdflower, which compete to attract crowd workers (CWs). CWs ca...
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In this paper, we proposed a small area hybrid D-flip-flop (DFF) circuit based on the gate diffusion input (GDI) technique. The proposed circuit consisted of only 12 MOS transistors compared to CMOS. This circuit was ...
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Medical image segmentation is essential for diagnosis but requires expensive and time-consuming labeled data. Semi-supervised learning (SSL) mitigates this issue by using unlabeled data to improve generalization. Howe...
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In this paper, we address the problem of automatic clothing parsing in surveillance images using the information from user-generated tags, such as "jeans" and "T-shirt." Although clothing parsing h...
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Matching suitable jobs with qualified candidates is crucial for online recruitment. Typically, users (i.e., candidates and employers) have specific expectations in the recruitment market, making them prefer similar jo...
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Matching suitable jobs with qualified candidates is crucial for online recruitment. Typically, users (i.e., candidates and employers) have specific expectations in the recruitment market, making them prefer similar jobs or candidates. Metric learning technologies provide a promising way to capture the similarity propagation between candidates and jobs. However, they rely on symmetric distance measures, failing to model users' asymmetric relationships in two-way selection. Additionally, users' behaviors (e.g., candidates) are highly affected by the feedback from their counterparts (e.g., employers), which can hardly be captured by the existing person-job fit methods that primarily explore homogeneous and undirected graphs. To address these problems, we propose a quasi-metric learning framework to capture the similarity propagation between candidates and jobs while modeling their asymmetric relations for bilateral person-job fit. Specifically, we propose a quasi-metric space that not only satisfies the triangle inequality to capture the fine-grained similarity between candidates and jobs, but also incorporates a tailored asymmetric measure to model users. two-way selection process in online recruitment. More importantly, the proposed quasi-metric learning framework can theoretically model recruitment rules from similarity and competitiveness perspectives, making it seamlessly align with bilateral person-job fit scenarios. To explore the mutual effects of two-sided users, we first organize candidates, employers, and their different-typed interactions into a heterogeneous relation graph, and then propose a relation-aware graph convolution network to capture users. mutual effects through their bilateral behaviors. Extensive experiments on several real-world datasets demonstrate the effectiveness of the proposed methods.
Recently, time series classification has attracted significant interest. One of the most promising recent approaches is the shapelet transform, which offers two main advantages over traditional approaches: optimizatio...
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This paper proposes an AI-based video metadata extension model to overcome the limitations of video search and recommendation systems in the multimedia industry. Current video searches and recommendations utilize pre-...
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ISBN:
(纸本)9791188428120
This paper proposes an AI-based video metadata extension model to overcome the limitations of video search and recommendation systems in the multimedia industry. Current video searches and recommendations utilize pre-added metadata. Metadata includes filenames, keywords, tags, genres, etc. This makes it impossible to make direct predictions about the content of a video without pre-added metadata. These platforms also analyze your previous search history, viewing history, etc. to understand your interests in order to serve you personalized videos. This may not reflect the actual content and may raise privacy concerns. In addition, recommendation systems suffer from a cold start problem, which is the lack of an initial target, as well as a bubble effect. Therefore, this study proposes a search and recommendation system by expanding metadata in videos using techniques such as shot boundary detection, speech recognition, and text mining. The proposed method selects the main objects required by the recommendation system based on the object frequency and extracts the corresponding objects from the video frame by frame. In addition, we extract the speech from the video separately, convert the speech to text to extract the script and apply text mining techniques to the extracted script to quantify it. Then, we synchronize the object frequency and the transcript to create a single contextual data. After that, we group videos and clips based on the contextual data and index them. Finally, we utilize Shot Boundary Detection to segment videos based on their content. To ensure that the generated contextual data is appropriate for the video, the proposed model compares the extracted script with the video's subtitle data to check and calibrate its accuracy. The model can then be fine-tuned by tuning and cross-validating the hyperparameter to improve its performance. These models can be incorporated into a variety of content discovery and recommendation platforms. By using expanded
The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". ...
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This paper provides an NP procedure that decides whether a linear-exponential system of constraints has an integer solution. Linear-exponential systems extend standard integer linear programs with exponential terms 2x...
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