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User Account Linkage Across Multiple Platforms with Location Data

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Journal of Computer Science & Technology 2020年第4期35卷 751-768页

作者： Wei Chen Weiqing Wang hongzhi yin Jun-Hua Fang Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Faculty of Information Technology Monash UniversityMelbourne 3000Australia School of Information Technology and Electrical Engineering The University of QueenslandBrisbane 4072Australia

Linking user accounts belonging to the same user across different platforms with location data has received significant attention,due to the popularization of GPS-enabled devices and the wide range of applications benefiting from user account linkage(e.g.,cross-platform user profiling and recommendation).Different from most existing studies which only focus on user account linkage across two platforms,we propose a novel model ULMP(i.e.,user account linkage across multiple platforms),with the goal of effectively and efficiently linking user accounts across multiple platforms with location *** of the practical significance brought by successful user linkage across multiple platforms,this task is very challenging compared with the ones across two *** major challenge lies in the fact that the number of user combinations shows an explosive growth with the increase of the number of *** tackle the problem,a novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index ***,in the pruned space,a match score based on kernel density estimation combining both spatial and temporal information is designed to retrieve the linked user *** extensive experiments conducted on four real-world datasets demonstrate the superiority of the proposed model ULMP in terms of both effectiveness and efficiency compared with the state-of-art methods.

关键词： user account linkage multiple platform check-in record

LECF: recommendation via learnable edge collaborative filtering

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Science China(Information Sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO yingxia SHAO Yawen LI hongzhi yin Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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Heterogeneous decentralised machine unlearning with seed model distillation

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 608-619页

作者： Guanhua Ye Tong Chen Quoc Viet Hung Nguyen hongzhi yin School of Information Technology and Electrical Engineering The University of QueenslandSt LuciaQueenslandAustralia School of Information and Communication Technology Griffith UniversityBrisbaneQueenslandAustralia

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model,personalised IoT service pro-viders have to put unlearning functionality into their *** most straight-forward method to unlearn users'contribution is to retrain the model from the initial state,which is not realistic in high throughput applications with frequent unlearning *** some machine unlearning frameworks have been proposed to speed up the retraining process,they fail to match decentralised learning scenarios.A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed(HDUS)is designed,which uses distilled seed models to construct erasable en-sembles for all ***,the framework is compatible with heterogeneous on-device models,representing stronger scalability in real-world *** experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.

关键词： data mining data privacy machine learning

An Efficient Framework for Multiple Subgraph Pattern Matching Models

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Journal of Computer Science & Technology 2019年第6期34卷 1185-1202页

作者： Jiu-Ru Gao Wei Chen Jia-Jie Xu An Liu Zhi-Xu Li hongzhi yin Lei Zhao School of Computer Science and Technology Soochow UniversitySuzhou 215006China School of Information Technology and Electrical Engineering The University of QueenslandBrisbane 4072Australia

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

关键词： subgraph pattern matching k-automorphism label generalization

Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2024年

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

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停车收费价格对居民出行方式选择行为的影响研究——以北京市居民小汽车、公交、出租车选择行为为例

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土木工程学报 2008年第8期41卷 93-98页

作者：秦焕美关宏志殷焕焕北京工业大学北京100022

以通过停车收费价格调解城市中心区的小汽车出行行为、减轻中心区的交通压力为目标,以北京市为例,通过在中心繁华地区的停车调查数据,首先利用回归模型进行初步的单因素分析,即对停车收费价格与出行方式选择的关系进行分析,进而利用非... 详细信息

以通过停车收费价格调解城市中心区的小汽车出行行为、减轻中心区的交通压力为目标,以北京市为例,通过在中心繁华地区的停车调查数据,首先利用回归模型进行初步的单因素分析,即对停车收费价格与出行方式选择的关系进行分析,进而利用非集计理论建立包括停车收费价格、公交服务水平等多因素的行为模型,最后对停车收费价格的敏感性进行分析。研究得出不同条件下,出行方式选择随停车收费价格变化的趋势,确定选择小汽车出行和选择公交/出租车出行的停车收费价格的平衡点,确定出行方式选择变化对于停车收费价格变化最为敏感性的区间,其结论从交通需求管理的角度为大城市停车收费价格政策的合理制定提供参考和借鉴。

关键词：停车收费价格出行方式选择非集计敏感性分析

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卡托普利联合美托洛尔治疗慢性心力衰竭的临床研究

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中国基层医药 2014年第11期21卷 1682-1684页

作者：殷洪志山东省夏津县人民医院内五科 253200

目的观察卡托普利联合美托洛尔对慢性心力衰竭（CHF）的治疗作用,并探讨其作用机制.方法 60例CHF患者分为常规治疗组（A组）和卡托普利＋美托洛尔治疗组（B组）,观察治疗后左心室射血分数（LVEF）、左室舒张末期内径（LVEDD）、基质金... 详细信息

目的观察卡托普利联合美托洛尔对慢性心力衰竭（CHF）的治疗作用,并探讨其作用机制.方法 60例CHF患者分为常规治疗组（A组）和卡托普利＋美托洛尔治疗组（B组）,观察治疗后左心室射血分数（LVEF）、左室舒张末期内径（LVEDD）、基质金属蛋白酶-9（MMP-9）和高敏C反应蛋白（hs-CRP）水平变化.酶联免疫吸附双抗体夹心法（ELISA）检测血清MMP-9表达水平;乳胶增强免疫比浊法检测血清hs-CRP表达水平.结果 B组LVEF及LVEDD[（63.21±6.09）％、（58.42±5.73） mm]的改善显著优于A组[（41.13±3.67）％、（66.24±4.24） mm]（t=7.62、6.54,均P＜0.01）;B组MMP-9和hs-CRP[（1.31±0.24） ng/mL、（6.34±0.58） mg/L]水平显著低于A组[（2.23±0.39） ng/mL、（8.31±0.74） mg/L]（t=6.82、7.03,均P＜0.01）.A和B组的有效率分别为36.7％和73.3％,差异有统计学意义（Х^2=6.90,P＜0.05）.结论卡托普利与美托洛尔联合治疗对CHF患者心功能具有保护作用,抑制MMP-9和hs-CRP的表达可能是其主要的作用机制.

关键词：心力衰竭,充血性血管紧张素转换酶抑制药 β受体阻滞剂基质金属蛋白酶类

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