Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, ex...
The necessity of comprehensive cybersecurity measures cannot be emphasized in today's quickly expanding digital world. Due to the rising complexity and frequency of cyber attacks, detecting malicious domains has b...
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With the development of information technology,a mass of data are generated every *** and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-mea...
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With the development of information technology,a mass of data are generated every *** and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real ***,these analyses are based on users’data,which disclose users’*** differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering ***,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy *** solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering *** of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each *** be specific,we generate a noisy distance first and then synthesize the high-dimensional data *** propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,***,we introduce a way to generate synthetic data records in high-dimensional *** experimental evaluation results show that our methods outperform the traditional methods significantly.
Recently various optimization problems, such as Mixed Integer Linear Programming Problems (MILPs), have undergone comprehensive investigation, leveraging the capabilities of machine learning. This work focuses on lear...
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Recently various optimization problems, such as Mixed Integer Linear Programming Problems (MILPs), have undergone comprehensive investigation, leveraging the capabilities of machine learning. This work focuses on learning-based solutions for efficiently solving the Quadratic Assignment Problem (QAPs), which stands as a formidable challenge in combinatorial optimization. While many instances of simpler problems admit fully polynomial-time approximate solution (FPTAS), QAP is shown to be strongly NP-hard. Even finding a FPTAS for QAP is difficult, in the sense that the existence of a FPTAS implies P = NP. Current research on QAPs suffer from limited scale and computational inefficiency. To attack the aforementioned issues, we here propose the first solution of its kind for QAP in the learn-to-improve category. This work encodes facility and location nodes separately, instead of forming computationally intensive association graphs prevalent in current approaches. This design choice enables scalability to larger problem sizes. Furthermore, a Solution AWare Transformer (SAWT) architecture integrates the incumbent solution matrix with the attention score to effectively capture higher-order information of the QAPs. Our model’s effectiveness is validated through extensive experiments on self-generated QAP instances of varying sizes and the QAPLIB benchmark. Copyright 2024 by the author(s)
This paper presents a novel framework for creating a recoverable rare disease patient identity system using blockchain and smart contracts, decentralized identifiers (DIDs), and the InterPlanetary File System (IPFS). ...
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Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot...
Medical image segmentation is crucial for diagnosis and treatment planning. While recent advancements in deep learning, particularly UNet variants, have improved segmentation performance, they often result in increase...
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The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this demonstration, we propose a scalable and extendible data imputation ...
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With the COVID-19 epidemic having a profound impact on global population mobility patterns, this study aims to explore the new pattern of population mobility in China after the end of the epidemic. Based on Baidu migr...
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Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these system...
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