Yunchuan Sun

Beijing Normal University

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Rongfang Bie

Beijing Normal University

Junsheng Zhang

Ningxia University

Xiuzhen Cheng

Shandong University of Science and Technology

Jiguo Yu

University of Electronic Science and Technology of China

Xiaoping Zeng

Beijing Normal University

Zhangbing Zhou

Telecom SudParis

Houbing Song

University of Maryland, Baltimore County

Chengming Qi

Beijing Union University

Hongli Yan

Northwest Women's and Children's Hospital

Xiaoping Zeng

Chongqing University

Cooperative Institutions

Beijing Normal University

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Shandong University

Shanghai Jiao Tong University

Beijing University of Posts and Telecommunications

Sun Yat-sen University

Tsinghua University

Chinese Academy of Medical Sciences & Peking Union Medical College

Xi'an Jiaotong University

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Inference attacks based on neural networks in social networks

Bo Mei Yinhao Xiao Li Hong Xiuzhen Cheng Yunchuan Sun

In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proved to be very effective in inference attack. This paper conducts an extensive study to infer sensitive personal information from public insensitive attributes in social networks by deploying fully connected neural networks. Correlation matrices and the details of constructing neural networks for social networks are elaborated. To show the advantages of neural networks on inference attack, different traditional machine learning algorithms are also studied. The results show that neural networks can achieve about 4 times of the baseline accuracy to classify low-correlation, high-noise dataset to infer sensitive users' attributes. In addition, neural networks outperform all the selected traditional algorithms. Outcomes from the study are deliberately discussed, and the limitations of both neural networks and traditional machine learning algorithms are also illustrated.

10.1145/3132465.3132469

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Citations (7)

Regional Culture and Digital Financial Inclusion in China

Journal of Organizational and End User Computing (2023)

Yunchuan Sun Ying Xu Xiaoping Zeng Xiao Li Qianqian Xia

Unbalanced development among regions is a prominent feature of the current situation of inclusive finance. Few works have been done on how regional culture affects the imbalance of inclusive financial development. The authors try to investigate this issue using yearly data of digital financial inclusion in China spanning from 2011 to 2020 and the Hofstede regional culture of China. The results reveal regional culture could be the potential driver which leads to regional imbalance of the development of digital financial inclusion in China. Specifically, under Chinese historical culture background, financial inclusion is positively related with indulgence regionally, while individualism or power distance could exert negative impact. The findings are verified by a two-stage least square approach. Due to the anonymity and platform dependence of digital financial, Hofstede culture could make sense by influencing public trust in the financial sector, the internet, and unfamiliar relationships.

10.4018/joeuc.332245

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Citations (4)

Advances on data, information, and knowledge in the internet of things

Personal and Ubiquitous Computing (2014)

Yunchuan Sun Rongfang Bie Peter J. Thomas Xiuzhen Cheng

Mobile internet

10.1007/s00779-014-0789-9

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Citations (14)

MSC: A Semantic Ranking for Hitting Results of Matchmaking of Services

Xian Shen Xin Jin Rongfang Bie Yunchuan Sun

As the e-commerce is done faster, there is a continuous flourishing of e-marketplaces. Matchmaking is an important aspect of e-commerce interactions. Recently, an approach has been taken to service matchmaking based on semantic Web technologies; the designed matching rule can be used to find the sellers' compatible advertisements for buyers. In this paper, we define three categories of attributes for the matchmaking service. Then we present three factors: semantic matching degree, semantic support and relational confidence to capture the semantic characteristics and relationships of the attributes. And we design a semantic ranking MSC combining the three factors to rank the results of advertisements matchmaking. MSC can capture the semantic aspect of matchmaking results; evaluation shows that MSC makes the process of matchmaking more accurately and the advertisement with the highest MSC is better

Semantic Matching

Rank (graph theory)

Semantic Search

10.1109/compsac.2006.147

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Citations (3)

Advances of the Semantic Link Network

Shenling Wang Yunchuan Sun Hongyang Zhang Xiaofeng Yu Rongfang Bie

The Semantic Link Network (SLN) is a loosely coupled semantic data model for managing the network resources. A SLN consists of three components: a set of semantic nodes, a set of semantic links, and a set of reasoning rules. Semantic nodes can be objects of any types. Semantic links reflect the semantic relations among objects. Reasoning rules, which describe the internal relations among semantic links, are used to derive new semantic links hidden behind. SLN is designed to establish semantic relationships among various resources for extending the current hyperlink network to a semantic-rich network. In this paper, we conduct a survey on the research of SLN from its theory and applications. Furthermore, the future trends of SLN and it's potential applications in future Internet of Things are discussed.

Hyperlink

Semantic Search

Semantic network

Semantic compression

Semantic data model

10.1109/skg.2012.16

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Citations (1)

Measuring the Resilience of Defi Market

SSRN Electronic Journal (2024)

Yunchuan Sun Xiaoping Zeng Zhaojun Liu Yongqin Ma Haifeng Hu

This paper constructs a DeFi Resilience Index (DRI) to assess the DeFi market’s capability to withstand risks and rebound from shocks. The DRI is developed through principal component analysis on four indicators relevant with the DeFi market, including the Total Value Locked, the Number of Active Addresses, the Volume of Fiat-backed Stablecoins, and the Volume of Trading. During the sample period from April 1, 2021 to October 25, 2023, the trend DRI aligns with the real actual state of the DeFi market. We identify 114 external shocks to the DeFi market to examine the effectiveness of the DRI, and find that higher DRI indicates the DeFi market’s stronger ability to recover from external shocks. We also observe that the DRI has a positive impact on the simultaneous and future 5-day performance of DeFi token prices. Further analysis indicates that DRI exhibits dynamic correlations with the performance of the cryptocurrency market, the stock market, and the option market. Regression results show that the prices of Bitcoin and Ether have positive and significant influences on the DRI, while the S&P 500 index and the VIX index have no significant impacts on the DRI. Our findings extend studies on market resilience from conventional financial markets to the emerging DeFi market, and highlight the interconnections between DeFi market resilience and other financial markets’ performance.

Resilience

10.2139/ssrn.4691406

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OCTOPIA: An Observant-Clinician and Touchable-Patient Integrated Architecture

Guangzhi Zhang Yunchuan Sun Rongfang Bie Xianlin Ma

It is time for clinician to be more elegant in diagnosis, and at the same time the patient to be taken care of in an all-round way. The improving and opening of the health and medical data, as well as the wide use of the environment and the human body sensors, gives us the opportunity to do so. This paper proposes an "observant clinician and touchable patient" integrated architecture (OCTOPIA), which is for both clinician and patient and extendible. On the basis of integrated medical knowledge, human health and environmental data, OCTOPIA interacts with the patient in a heuristic way to analyze the patient's symptoms and likely diseases. Then, comprehensive data information will be presented to the clinician. In all key links, clinicians will have the opportunity to use their own experience to influence the final diagnosis. Broadly, OCTOPIA consists of five major components: data capture, symptom-disease graph, QA system, ranking likely diseases and visualization. This paper proposes an architecture integrating the environment and human body information, and presents the solutions of an interactive QA system for symptom, a multi-factor calculation model for disease and a visualization design. With the help of OCTOPIA, the patient's information and the state of the environment are more fully perceived, while clinician can make a more detailed diagnosis.

10.1109/iiki.2016.27

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Algebra model and experiment for semantic link network

International Journal of High Performance Computing and Networking (2005)

Hai Zhuge Yunchuan Sun Ruixiang Jia Jie Liu

Semantic Link Network, SLN, is a semantic extension of hyperlink network. Based on the previous SLN model, this paper enriches the semantic links and reasoning rules, proposes an algebra model that supports semantic reasoning and the management of SLN by using a semantic matrix and presents the experiment for putting an SLN into practice. A solution to efficiently manage a huge SLN by dividing the whole SLN into partitioned semantic matrices is also put forth. The consistency issue about SLN is discussed, based on the matrix representation. Experiment of establishing and using the SLN shows the feasibility of the proposed model.

Hyperlink

Link (geometry)

Semantic network

Representation

Collocation (remote sensing)

10.1504/ijhpcn.2005.008565

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Citations (17)

Determinants of price fluctuations in the electricity market: a study with PCA and NARDL models

Economic Research-Ekonomska Istraživanja (2019)

Kun Li Joseph D. Cursio Yunchuan Sun Zizheng Zhu

In the modern electricity markets, negative prices and spike prices coexist as a pair of opposite economic phenomena. This study investigates how these extreme prices play as the determinants to drive price fluctuations in the electricity market. We construct a two-stage analysis including a principal component analysis (PCA) and a nonlinear autoregressive distributed lags model (NARDL). We apply this analytical method to the wholesale Pennsylvania, New Jersey and Maryland (PJM) electricity market. We find that according to PCA, in the individual transmission lines, spike prices are determinants with largest explanatory power to the variation of prices, while according to NARDL, from the standpoint of the overall market, negative prices have a larger potential effect on both the real-time market and the forward market. These results are valuable and contributive to managers and operators in the electricity markets for policy decision making.

Distributed lag

10.1080/1331677x.2019.1645712

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Citations (12)

Guest Editorial: Design and Analysis of Communication Interfaces for Industry 4.0

IEEE Journal on Selected Areas in Communications (2020)

Syed Ali Raza Zaidi Muhammad Zeeshan Shakir Houbing Song Antonio J. Jara Yunchuan Sun

This special issue (SI) aims to present recent advances in the design and analysis of communication interfaces for Industry 4.0. The Industry 4.0 paradigm aims to integrate advanced manufacturing techniques with Industrial Internet-of-Things (IIoT) to create an agile digital manufacturing ecosystem. The main goal is to instrument production processes by embedding sensors, actuators and other control devices which autonomously communicate with each other throughout the value-chain [1] .

Industry 4.0

Industrial Internet

10.1109/jsac.2020.2983815

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Citations (4)