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Articles 6181 - 6210 of 8479
Full-Text Articles in Computer Sciences
It Is Not Just What We Say, But How We Say Them: Lda-Based Behavior-Topic Model, Minghui Qiu, Feida Zhu, Jing Jiang
It Is Not Just What We Say, But How We Say Them: Lda-Based Behavior-Topic Model, Minghui Qiu, Feida Zhu, Jing Jiang
Research Collection School Of Computing and Information Systems
Textual information exchanged among users on online social network platforms provides deep understanding into users' interest and behavioral patterns. However, unlike traditional text-dominant settings such as o ine publishing, one distinct feature for online social network is users' rich interactions with the textual content, which, unfortunately, has not yet been well incorporated in the existing topic modeling frameworks. In this paper, we propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on online social network settings such as microblogs like Twitter where the textual content is relatively …
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
Research Collection School Of Computing and Information Systems
The robustness of a network is determined by how well its vertices are connected to one another so as to keep the network strong and sustainable. As the network evolves its robustness changes and may reveal events as well as periodic trend patterns that affect the interactions among users in the network. In this paper, we develop R-energy as a new measure of network robustness based on the spectral analysis of normalized Laplacian matrix. R-energy can cope with disconnected networks, and is efficient to compute with a time complexity of O (jV j + jEj) where V and E are …
Tesla: An Energy-Saving Agent That Leverages Schedule Flexibility, Jun Young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Burcin Becerik-Gerber, Milind Tambe
Tesla: An Energy-Saving Agent That Leverages Schedule Flexibility, Jun Young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Burcin Becerik-Gerber, Milind Tambe
Research Collection School Of Computing and Information Systems
This innovative application paper presents TESLA, an agent-based application for optimizing the energy use in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. TESLA provides three key contributions: (i) three online scheduling algorithms that consider flexibility of people’s preferences for energyefficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; and (iii) surveys of real users that indicate that TESLA’s assumptions exist in practice. TESLA was evaluated on data of over 110,000 meetings held …
Designing Leakage-Resilient Password Entry On Touchscreen Mobile Devices, Qiang Yan, Jin Han, Yingjiu Li, Jianying Zhou, Robert H. Deng
Designing Leakage-Resilient Password Entry On Touchscreen Mobile Devices, Qiang Yan, Jin Han, Yingjiu Li, Jianying Zhou, Robert H. Deng
Research Collection School Of Computing and Information Systems
Touchscreen mobile devices are becoming commodities as the wide adoption of pervasive computing. These devices allow users to access various services at anytime and anywhere. In order to prevent unauthorized access to these services, passwords have been pervasively used in user authentication. However, password-based authentication has intrinsic weakness in password leakage. This threat could be more serious on mobile devices, as mobile devices are widely used in public places. Most prior research on improving leakage resilience of password entry focuses on desktop computers, where specific restrictions on mobile devices such as small screen size are usually not addressed. Meanwhile, additional …
Expressive Search On Encrypted Data, Junzuo Lai, Xuhua Zhou, Robert H. Deng, Yingjiu Li, Kefei Chen
Expressive Search On Encrypted Data, Junzuo Lai, Xuhua Zhou, Robert H. Deng, Yingjiu Li, Kefei Chen
Research Collection School Of Computing and Information Systems
Different from the traditional public key encryption, searchable public key encryption allows a data owner to encrypt his data under a user’s public key in such a way that the user can generate search token keys using her secret key and then query an encryption storage server. On receiving such a search token key, the server filters all or related stored encryptions and returns matched ones as response. Searchable pubic key encryption has many promising applications. Unfortunately, existing schemes either only support simple query predicates, such as equality queries and conjunctive queries, or have a superpolynomial blowup in ciphertext size …
Anonymous Authentication Of Visitors For Mobile Crowd Sensing At Amusement Parks, Divyan Konidala, Robert H. Deng, Yingjiu Li, Hoong Chuin Lau, Stephen Fienberg
Anonymous Authentication Of Visitors For Mobile Crowd Sensing At Amusement Parks, Divyan Konidala, Robert H. Deng, Yingjiu Li, Hoong Chuin Lau, Stephen Fienberg
Research Collection School Of Computing and Information Systems
In this paper we focus on authentication and privacy aspects of an application scenario that utilizes mobile crowd sensing for the benefit of amusement park operators and their visitors. The scenario involves a mobile app that gathers visitors’ demographic details, preferences, and current location coordinates, and sends them to the park’s sever for various analyses. These analyses assist the park operators to efficiently deploy their resources, estimate waiting times and queue lengths, and understand the behavior of individual visitors and groups. The app server also offers visitors optimal recommendations on routes and attractions for an improved dynamic experience and minimized …
Retweeting: An Act Of Viral Users, Susceptible Users, Or Viral Topics?, Tuan-Anh Hoang, Ee Peng Lim
Retweeting: An Act Of Viral Users, Susceptible Users, Or Viral Topics?, Tuan-Anh Hoang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
When a user retweets, there are three behavioral factors that cause the actions. They are the topic virality, user virality and user susceptibility. Topic virality captures the degree to which a topic attracts retweets by users. For each topic, user virality and susceptibility refer to the likelihood that a user attracts retweets and performs retweeting respectively. To model a set of observed retweet data as a result of these three topic specific factors, we first represent the retweets as a three-dimensional tensor of the tweet authors, their followers, and the tweets themselves. We then propose the V 2S model, a …
Tag Recommendation In Software Information Sites, Xin Xia, David Lo, Xinyu Wang, Bo Zhou
Tag Recommendation In Software Information Sites, Xin Xia, David Lo, Xinyu Wang, Bo Zhou
Research Collection School Of Computing and Information Systems
Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance and test processes as software information sites. It is common to see tags in software information sites and many sites allow users to tag various objects with their own words. Users increasingly use tags to describe the most important features of their posted contents or projects. In this paper, we propose TagCombine, an automatic …
Your Love Is Public Now: Questioning The Use Of Personal Information In Authentication, Payas Gupta, Swapna Gottipati, Jing Jiang, Debin Gao
Your Love Is Public Now: Questioning The Use Of Personal Information In Authentication, Payas Gupta, Swapna Gottipati, Jing Jiang, Debin Gao
Research Collection School Of Computing and Information Systems
Most social networking platforms protect user's private information by limiting access to it to a small group of members, typically friends of the user, while allowing (virtually) everyone's access to the user's public data. In this paper, we exploit public data available on Facebook to infer users' undisclosed interests on their profile pages. In particular, we infer their undisclosed interests from the public data fetched using Graph APIs provided by Facebook. We demonstrate that simply liking a Facebook page does not corroborate that the user is interested in the page. Instead, we perform sentiment-oriented mining on various attributes of a …
Cost-Sensitive Double Updating Online Learning And Its Application To Online Anomaly Detection, Peilin Zhao, Steven C. H. Hoi
Cost-Sensitive Double Updating Online Learning And Its Application To Online Anomaly Detection, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Although both cost-sensitive classification and online learning have been well studied separately in data mining and machine learning, there was very few comprehensive study of cost-sensitive online classification in literature. In this paper, we formally investigate this problem by directly optimizing cost-sensitive measures for an online classification task. As the first comprehensive study, we propose the Cost-Sensitive Double Updating Online Learning (CSDUOL) algorithms, which explores a recent double updating technique to tackle the online optimization task of cost-sensitive classification by maximizing the weighted sum or minimizing the weighted misclassification cost. We theoretically analyze the cost-sensitive measure bounds of the proposed …
Fans: Face Annotation By Searching Large-Scale Web Facial Images, Steven Hoi, Dayong Wang, I Yeu Cheng, Elmer Lin, Jianke Zhu, Ying He, Chunyan Miao
Fans: Face Annotation By Searching Large-Scale Web Facial Images, Steven Hoi, Dayong Wang, I Yeu Cheng, Elmer Lin, Jianke Zhu, Ying He, Chunyan Miao
Research Collection School Of Computing and Information Systems
Auto face annotation is an important technique for many real-world applications, such as online photo album management, new video summarization, and so on. It aims to automatically detect human faces from a photo image and further name the faces with the corresponding human names. Recently, mining web facial images on the internet has emerged as a promising paradigm towards auto face annotation. In this paper, we present a demonstration system of search-based face annotation: FANS - Face ANnotation by Searching large-scale web facial images. Given a query facial image for annotation, we first retrieve a short list of the most …
Impact Of Multimedia In Sina Weibo: Popularity And Life Span, Xun Zhao, Feida Zhu, Weining Qian, Aoying Zhou
Impact Of Multimedia In Sina Weibo: Popularity And Life Span, Xun Zhao, Feida Zhu, Weining Qian, Aoying Zhou
Research Collection School Of Computing and Information Systems
Multimedia contents such as images and videos are widely used in social network sites nowadays. Sina Weibo, a Chinese microblogging service, is one of the first microblog platforms to incorporate multimedia content sharing features. This work provides statistical analysis on how multimedia contents are produced, consumed, and propagated in Sina Weibo. Based on 230 million tweets and 1.8 million user profiles in Sina Weibo, we study the impact of multimedia contents on the popularity of both users and tweets as well as tweet life span. Our preliminary study shows that multimedia tweets dominant pure text ones in Sina Weibo. Multimedia …
Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We study a real-world problem arising from the operations of a hospital service provider, which we term the master physician scheduling problem. It is a planning problem of assigning physicians’ full range of day-to-day duties (including surgery, clinics, scopes, calls, administration) to the defined time slots/shifts over a time horizon, incorporating a large number of constraints and complex physician preferences. The goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We propose mathematical programming models that represent different variants of this problem. The models were tested on a real …
Energy-Neutral Scheduling And Forwarding In Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Weng Seng Soh, Hwee-Pink Tan
Energy-Neutral Scheduling And Forwarding In Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Weng Seng Soh, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In environmentally-powered wireless sensor networks (EPWSNs), low latency wakeup scheduling and packet forwarding is challenging due to dynamic duty cycling, posing time-varying sleep latencies and necessitating the use of dynamic wakeup schedules. We show that the variance of the intervals between receiving wakeup slots affects the expected sleep latency: when the variance of the intervals is low (high), the expected latency is low (high). We therefore propose a novel scheduling scheme that uses the bit-reversal permutation sequence (BRPS) – a finite integer sequence that positions receiving wakeup slots as evenly as possible to reduce the expected sleep latency. At the …
Distributed Gibbs: A Memory-Bounded Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau
Distributed Gibbs: A Memory-Bounded Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this paper, we introduce a new sampling-based DCOP algorithm called Distributed Gibbs, whose memory requirements per agent is linear in the number of agents in the problem. Additionally, we show …
Demand Forecasting Using A Growth Model And Negative Binomial Regression Framework, Cally Yeru Ong, Murphy Choy, Michelle L. F. Cheong
Demand Forecasting Using A Growth Model And Negative Binomial Regression Framework, Cally Yeru Ong, Murphy Choy, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
In this paper, we look at demand forecasting by using a growth model and negative binomial regression framework. Using cumulative sales, we model the sales data for different wristwatch brands and relate it to their sales and growth characteristics. We apply clustering to determine the distinctive characteristics of each individual cluster. Four different growth models are applied to the clusters to find the most suitable growth model to be used. After determining the appropriate growth model to be applied, we then forecast the sales by applying the model to new products being launched in the market and continue to monitor …
Implementation Of Slowly Changing Dimension To Data Warehouse To Manage Marketing Campaigns In Banks, Lihui Wang, Junyu Choy, Michelle L. F. Cheong
Implementation Of Slowly Changing Dimension To Data Warehouse To Manage Marketing Campaigns In Banks, Lihui Wang, Junyu Choy, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Management of updating and recording campaign leads in data warehouse of any banking environment is complex especially with multiple campaigns are active simultaneously. As a way to avoid overly contacting customers for sales-based marketing contacts, the concept of Recency Frame is introduced to “lock” the customers who are targeted in Sales-based campaign for a specified time period. During this Recency Frame, the customer cannot be targeted by other Sales-based campaign under the same channel. This approach increased the difficulties of managing the customers’ data with proper data updating and storing and procedures have to be placed and made sufficiently robust …
Your Love Is Public Now: Questioning The Use Of Personal Information In Authentication, Payas Gupta, Swapna Gottipati, Jing Jiang, Debin Gao
Your Love Is Public Now: Questioning The Use Of Personal Information In Authentication, Payas Gupta, Swapna Gottipati, Jing Jiang, Debin Gao
Research Collection School Of Computing and Information Systems
Most social networking platforms protect user's private information by limiting access to it to a small group of members, typically friends of the user, while allowing (virtually) everyone's access to the user's public data. In this paper, we exploit public data available on Facebook to infer users' undisclosed interests on their profile pages. In particular, we infer their undisclosed interests from the public data fetched using Graph APIs provided by Facebook. We demonstrate that simply liking a Facebook page does not corroborate that the user is interested in the page. Instead, we perform sentiment-oriented mining on various attributes of a …
Strong Location Privacy: A Case Study On Shortest Path Queries [Invited Paper], Kyriakos Mouratidis
Strong Location Privacy: A Case Study On Shortest Path Queries [Invited Paper], Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
The last few years have witnessed an increasing availability of location-based services (LBSs). Although particularly useful, such services raise serious privacy concerns. For example, exposing to a (potentially untrusted) LBS the client's position may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. There is a large body of work on protecting the location privacy of the clients. In this paper, we focus on shortest path queries, describe a framework based on private information retrieval (PIR), and conclude with open questions about the practicality of PIR and other location privacy approaches.
Searching Visual Instances With Topology Checking And Context Modeling, Wei Zhang, Chong-Wah Ngo
Searching Visual Instances With Topology Checking And Context Modeling, Wei Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Instance Search (INS) is a realistic problem initiated by TRECVID, which is to retrieve all occurrences of the querying object, location, or person from a large video collection. It is a fundamental problem with many applications, and also a challenging problem different from the traditional concept or near-duplicate (ND) search, since the relevancy is defined at instance level. True responses could exhibit various visual variations, such as being small on the image with different background, or showing a non-homography spatial configuration. Based on the Bag-of-Words model, we propose two techniques tailored for Instance Search. Specifically, we explore the use of …
Delayed Insertion And Rule Effect Moderation Of Domain Knowledge For Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan
Delayed Insertion And Rule Effect Moderation Of Domain Knowledge For Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Though not a fundamental pre-requisite to efficient machine learning, insertion of domain knowledge into adaptive virtual agent is nonetheless known to improve learning efficiency and reduce model complexity. Conventionally, domain knowledge is inserted prior to learning. Despite being effective, such approach may not always be feasible. Firstly, the effect of domain knowledge is assumed and can be inaccurate. Also, domain knowledge may not be available prior to learning. In addition, the insertion of domain knowledge can frame learning and hamper the discovery of more effective knowledge. Therefore, this work advances the use of domain knowledge by proposing to delay the …
Circular Reranking For Visual Search, Ting Yao, Chong-Wah Ngo
Circular Reranking For Visual Search, Ting Yao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Search reranking is regarded as a common way to boost retrieval precision. The problem nevertheless is not trivial especially when there are multiple features or modalities to be considered for search, which often happens in image and video retrieval. This paper proposes a new reranking algorithm, named circular reranking, that reinforces the mutual exchange of information across multiple modalities for improving search performance, following the philosophy that strong performing modality could learn from weaker ones, while weak modality does benefit from interacting with stronger ones. Technically, circular reranking conducts multiple runs of random walks through exchanging the ranking scores among …
Towards Omnidirectional Passive Human Detection, Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, Yunhao Liu
Towards Omnidirectional Passive Human Detection, Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, Yunhao Liu
Research Collection School Of Computing and Information Systems
Passive human detection and localization serve as key enablers for various pervasive applications such as smart space, human-computer interaction and asset security. The primary concern in devising scenario-tailored detecting systems is the coverage of their monitoring units. In conventional radio-based schemes, the basic unit tends to demonstrate a directional coverage, even if the underlying devices are all equipped with omnidirectional antennas. Such an inconsistency stems from the link-centric architecture, creating an anisotropic wireless propagating environment. To achieve an omnidirectional coverage while retaining the link-centric architecture, we propose the concept of Omnidirectional Passive Human Detection, and investigate to harness the PHY …
Information Security As A Credence Good, Ping Fan Ke, Kai-Lung Hui, Wei Thoo Yue
Information Security As A Credence Good, Ping Fan Ke, Kai-Lung Hui, Wei Thoo Yue
Research Collection School Of Computing and Information Systems
With increasing use of information systems, many organizations are outsourcing information security protection to a managed security service provider (MSSP). However, diagnosing the risk of an information system requires special expertise, which could be costly and difficult to acquire. The MSSP may exploit their professional advantage and provide fraudulent diagnosis of clients’ vulnerabilities. Such an incentive to mis-represent clients’ risks is often called the credence goods problem in the economics literature[3]. Although different mechanisms have been introduced to tackle the credence goods problem, in the information security outsourcing context, such mechanisms may not work well with the presence of system …
Predicting Sql Injection And Cross Site Scripting Vulnerabilities Through Mining Input Sanitization Patterns, Lwin Khin Shar, Hee Beng Kuan Tan
Predicting Sql Injection And Cross Site Scripting Vulnerabilities Through Mining Input Sanitization Patterns, Lwin Khin Shar, Hee Beng Kuan Tan
Research Collection School Of Computing and Information Systems
ContextSQL injection (SQLI) and cross site scripting (XSS) are the two most common and serious web application vulnerabilities for the past decade. To mitigate these two security threats, many vulnerability detection approaches based on static and dynamic taint analysis techniques have been proposed. Alternatively, there are also vulnerability prediction approaches based on machine learning techniques, which showed that static code attributes such as code complexity measures are cheap and useful predictors. However, current prediction approaches target general vulnerabilities. And most of these approaches locate vulnerable code only at software component or file levels. Some approaches also involve process attributes that …
Enabling An Integrated Rate-Temporal Learning Scheme On Memristor, Wei He, Kejie Huang, Ning Ning, Kiruthika Ramanathan, Guoqi Li, Yu Jiang, Jiayin Sze, Luping Shi, Rong Zhao, Jing Pei
Enabling An Integrated Rate-Temporal Learning Scheme On Memristor, Wei He, Kejie Huang, Ning Ning, Kiruthika Ramanathan, Guoqi Li, Yu Jiang, Jiayin Sze, Luping Shi, Rong Zhao, Jing Pei
Research Collection School Of Computing and Information Systems
Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies …
Cross-Domain Password-Based Authenticated Key Exchange Revisited, Liqun Chen, Hoon Wei Lim, Guomin Yang
Cross-Domain Password-Based Authenticated Key Exchange Revisited, Liqun Chen, Hoon Wei Lim, Guomin Yang
Research Collection School Of Computing and Information Systems
We revisit the problem of secure cross-domain communication between two users belonging to different security domains within an open and distributed environment. Existing approaches presuppose that either the users are in possession of public key certificates issued by a trusted certificate authority (CA), or the associated domain authentication servers share a long-term secret key. In this paper, we propose a generic framework for designing four-party password-based authenticated key exchange (4PAKE) protocols. Our framework takes a different approach from previous work. The users are not required to have public key certificates, but they simply reuse their login passwords they share with …
Core Versus Peripheral Information Technology Employees And Their Impact On Firm Performance, Ling Liu, Daniel Q. Chen, Nan Hu, Indranil Bose, Garry D. Bruton
Core Versus Peripheral Information Technology Employees And Their Impact On Firm Performance, Ling Liu, Daniel Q. Chen, Nan Hu, Indranil Bose, Garry D. Bruton
Research Collection School Of Computing and Information Systems
Scholars have widely argued, but not previously examined, that core employees with firm specific skills are critical to the firm's strategic success. This argument has led to the belief that employees whose skills are not firm specific can be readily replaced in the external market and are peripheral to the firm's strategic goals. Employing a resource based view of the firm, we find that the core information technology (IT) employees with firm specific skills are value-adding resources that aid the firm's performance whereas peripheral employees with less firm specific skills provide no value to the firm's performance. Examining the issue …
Roundtriprank: Graph-Based Proximity With Importance And Specificity, Yuan Fang, Kevin Chen-Chuan Chang, Hady W. Lauw
Roundtriprank: Graph-Based Proximity With Importance And Specificity, Yuan Fang, Kevin Chen-Chuan Chang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Graph-based proximity has many applications with different ranking needs. However, most previous works only stress the sense of importance by finding "popular” results for a query. Often times important results are overly general without being well-tailored to the query, lacking a sense of specificity— which only emerges recently. Even then, the two senses are treated independently, and only combined empirically. In this paper, we generalize the well-studied importance-based random walk into a round trip and develop RoundTripRank, seamlessly integrating specificity and importance in one coherent process. We also recognize the need for a flexible trade-off between the two senses, and …
Twicube: A Real-Time Twitter Online Community Analysis Tool, Juan Du, Wei Xie, Cheng Li, Feida Zhu, Ee Peng Lim
Twicube: A Real-Time Twitter Online Community Analysis Tool, Juan Du, Wei Xie, Cheng Li, Feida Zhu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
As a micro-blogging service, Twitter differs from other social network services in two ways: 1) the absence of mutual consent in establishing follow links and 2) being a mixture of news media and social network. A key question to ask in better understanding Twitter user behavior is which part of a user’s Twitter network reflects one’s real-life social network. TwiCube is an online tool that employs a novel algorithm capable of identifying a user’s real-life social community, which we call the user’s off-line community, purely from examining the link structure among the user’s followers and followees. Based on the identified …