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Wang-Chien Lee

Bio: Wang-Chien Lee is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Nearest neighbor search. The author has an hindex of 60, co-authored 366 publications receiving 14123 citations. Previous affiliations of Wang-Chien Lee include Ohio State University & Verizon Communications.


Papers
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01 Jan 2018
TL;DR: This research proposes a semi-supervised career track labelling framework to automatically assign career tracks for large set of jobs and reduces the human annotation efforts in maintaining the career track knowledge databases over time across different geographical regions, but also facilitates data science study on career movements.
Abstract: Career track represents a vertical career pathway, where one can gradually move up to take up higher job appointments when relevant skills are acquired. Understanding the propensity of career movements in an evolving job market can enable timely career guidance to job seekers and working professionals. To this end, we harvest career trajectories from online professional network (OPN). Our focus lies on obtaining a macro view on career movements at the track granularity. Specifically, we propose a semi-supervised career track labelling framework to automatically assign career tracks for large set of jobs. To contextually label jobs, we collect example jobs with career track labels identified by human resource specialists and domain experts in Singapore. An intuitive idea is to learn the labelling knowledge from the example jobs and then apply to jobs in OPN. Unfortunately, such small amount of labeled jobs presents a great challenge in our attempt to accurately recover career tracks for plentiful unlabelled jobs. We thus address the issue by resorting to semi-supervised learning methods. This research not only reduces the human annotation efforts in maintaining the career track knowledge databases over time across different geographical regions, but also facilitates data science study on career movements. Extensive experiments are conducted to demonstrate the labelling accuracy as well as to gain insights upon obtained career track labels.

3 citations

Proceedings ArticleDOI
20 Apr 2020
TL;DR: In this article, the authors proposed and evaluated algorithms that facilitate network intervention planning through simultaneous optimization of network degree, closeness, betweenness, and local clustering coefficient, under scenarios involving Network Intervention with Limited Degradation - for Single target (NILD-S) and NILD-M).
Abstract: Research suggests that social relationships have substantial impacts on individuals’ health outcomes. Network intervention, through careful planning, can assist a network of users to build healthy relationships. However, most previous work is not designed to assist such planning by carefully examining and improving multiple network characteristics. In this paper, we propose and evaluate algorithms that facilitate network intervention planning through simultaneous optimization of network degree, closeness, betweenness, and local clustering coefficient, under scenarios involving Network Intervention with Limited Degradation - for Single target (NILD-S) and Network Intervention with Limited Degradation - for Multiple targets (NILD-M). We prove that NILD-S and NILD-M are NP-hard and cannot be approximated within any ratio in polynomial time unless P=NP. We propose the Candidate Re-selection with Preserved Dependency (CRPD) algorithm for NILD-S, and the Objective-aware Intervention edge Selection and Adjustment (OISA) algorithm for NILD-M. Various pruning strategies are designed to boost the efficiency of the proposed algorithms. Extensive experiments on various real social networks collected from public schools and Web and an empirical study are conducted to show that CRPD and OISA outperform the baselines in both efficiency and effectiveness.

2 citations

Proceedings ArticleDOI
13 Sep 2010
TL;DR: A comprehensive performance evaluation has been conducted to demonstrate the superiority of DISQO design, compared with existing state-of-the-art frameworks for monitoring moving objects.
Abstract: This paper presents DISQO, a DIStributed Framework for Spatial Queries over Moving Objects. Distinguished from existing work, DISQO aims at achieving high scalability and system performance in support of both snapshot and continuous spatial queries over moving objects. The design of DISQO is based on our observation that exchanging object location information and query information between the location server and moving objects can reduce communication cost and facilitate scalable query processing. Thus, DISQO is built upon the notions of roaming regions and query maps in correspondence with object location information and query information. A comprehensive performance evaluation has been conducted to demonstrate the superiority of DISQO design, compared with existing state-of-the-art frameworks for monitoring moving objects.

2 citations

Journal ArticleDOI
TL;DR: Pervasive (or Ubiquitous) Computing aims to seamlessly integrate computing and communication with the authors' environment so as to make their day-to-day activities the central focus rather than the computing or communication devices per se.
Abstract: Pervasive (or Ubiquitous) Computing aims to seamlessly integrate computing and communication with our environment so as to make our day-to-day activities the central focus rather than the computing or communication devices per se. Mark Weiser in his 1991 visionary paper "The Computer for 21st Century" laid the foundations of this field. Last decade has seen a steady increase in the momentum for his ideas. This is mainly due to dramatic increase in the availability of personal digital assistants (PDAs), wireless networking solutions, and embedded computing devices. For example, wirelessly connected organizers and smart phones are becoming popular, and digital computing in some form is now an integral part of numerous everyday appliances. This has led to a fundamental shift in the way we perceive computing and computers. Computers are no longer stand alone special purpose machines to be used by experts; rather, they are ubiquitously present in a networked environment to serve myriad needs of everyday life. The change in our perception of computing and computing devices and their ever increasing presence in our everyday life in form of smart spaces is the subject of the field of pervasive computing. The word pervasive means having power to spread throughout. Pervasive Computing is an environment where people interact with various companion, embedded, or invisible computers. It essentially means to enable networked devices to be aware of their surroundings and peers, and to be capable to provide services to and use services from peers effectively.

2 citations

Proceedings ArticleDOI
06 Nov 2017
TL;DR: This paper proposes a probabilistic generative model, Menu-Offering-Bundle (MOB) model, to capture the offering and bundling decisions of project creators based on collected data of 14K crowdfunding projects and their 149K reward bundles across a half-year period, and shows that the learned offering and bundle topics carry distinguishable meanings and provide insights of key factors on project success.
Abstract: Offering products in the forms of menu bundles is a common practice in marketing to attract customers and maximize revenues. In crowdfunding platforms such as Kickstarter, rewards also play an important part in influencing project success. Designing rewards consisting of the appropriate items is a challenging yet crucial task for the project creators. However, prior research has not considered the strategies project creators take to offer and bundle the rewards, making it hard to study the impact of reward designs on project success. In this paper, we raise a novel research question: understanding project creators' decisions of reward designs to level their chance to succeed. We approach this by modeling the design behavior of project creators, and identifying the behaviors that lead to project success. We propose a probabilistic generative model, Menu-Offering-Bundle (MOB) model, to capture the offering and bundling decisions of project creators based on collected data of 14K crowdfunding projects and their 149K reward bundles across a half-year period. Our proposed model is shown to capture the offering and bundling topics, outperform the baselines in predicting reward designs. We also find that the learned offering and bundling topics carry distinguishable meanings and provide insights of key factors on project success.

2 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI

6,278 citations

Proceedings ArticleDOI
21 Aug 2011
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
Abstract: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

2,922 citations

01 Nov 2008

2,686 citations

Journal ArticleDOI
TL;DR: This review presents the emergent field of temporal networks, and discusses methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems.
Abstract: A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems In many cases, however, the edges are not continuously active As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts In some cases, edges are active for non-negligible periods of time: eg, the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks

2,452 citations