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Identifying an unfamiliar caller’s profession is important to protect citizens’ personal safety and property. Owing to limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraud phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for identification of multiple professions. We observed that web service requests issued from users’ mobile phones may exhibit their applications preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users’ mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concern. In this paper, we develop CPFinder —- a system which exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.
Jiaquan Zhang; Hui Chen; XiaoMing Yao; XiaoMing Fu. CPFinder: Finding an Unknown Caller’s Profession from Anonymized Mobile Phone Data. Digital Communications and Networks 2021, 1 .
AMA StyleJiaquan Zhang, Hui Chen, XiaoMing Yao, XiaoMing Fu. CPFinder: Finding an Unknown Caller’s Profession from Anonymized Mobile Phone Data. Digital Communications and Networks. 2021; ():1.
Chicago/Turabian StyleJiaquan Zhang; Hui Chen; XiaoMing Yao; XiaoMing Fu. 2021. "CPFinder: Finding an Unknown Caller’s Profession from Anonymized Mobile Phone Data." Digital Communications and Networks , no. : 1.
Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy.
Hui Chen; Sven Voigt; XiaoMing Fu. Data-Driven Analysis on Inter-City Commuting Decisions in Germany. Sustainability 2021, 13, 6320 .
AMA StyleHui Chen, Sven Voigt, XiaoMing Fu. Data-Driven Analysis on Inter-City Commuting Decisions in Germany. Sustainability. 2021; 13 (11):6320.
Chicago/Turabian StyleHui Chen; Sven Voigt; XiaoMing Fu. 2021. "Data-Driven Analysis on Inter-City Commuting Decisions in Germany." Sustainability 13, no. 11: 6320.
Hui Chen; Mengru Ji. Experimental Comparison of Classification Methods under Class Imbalance. ICST Transactions on Scalable Information Systems 2018, "0", 1 .
AMA StyleHui Chen, Mengru Ji. Experimental Comparison of Classification Methods under Class Imbalance. ICST Transactions on Scalable Information Systems. 2018; "0" ():1.
Chicago/Turabian StyleHui Chen; Mengru Ji. 2018. "Experimental Comparison of Classification Methods under Class Imbalance." ICST Transactions on Scalable Information Systems "0", no. : 1.