Digital Commons Network^™

Energy-Efficient Continuous Activity Recognition On Mobile Phones: An Activity-Adaptive Approach, Zhixian Yan, Vigneshwaran Subbaraju, Dipanjan Chakraborty, Archan Misra, Karl Aberer

Research Collection School Of Computing and Information Systems

Power consumption on mobile phones is a painful obstacle towards adoption of continuous sensing driven applications, e.g., continuously inferring individual’s locomotive activities (such as ‘sit’, ‘stand’ or ‘walk’) using the embedded accelerometer sensor. To reduce the energy overhead of such continuous activity sensing, we first investigate how the choice of accelerometer sampling frequency & classification features affects, separately for each activity, the “energy overhead” vs. “classification accuracy” tradeoff. We find that such tradeoff is activity specific. Based on this finding, we introduce an activity-sensitive strategy (dubbed “A3R” – Adaptive Accelerometer-based Activity Recognition) for continuous activity recognition, where the choice of …

Sammple: Detecting Semantic Indoor Activities In Practical Settings Using Locomotive Signatures, Zhixian Yan, Dipanjan Chakraborty, Archan Misra, Hoyoung Jeung, Karl Aberer

Research Collection School Of Computing and Information Systems

We analyze the ability of mobile phone-generated accelerometer data to detect high-level (i.e., at the semantic level) indoor lifestyle activities, such as cooking at home and working at the workplace, in practical settings. We design a 2-T ier activity extraction framework (called SAMMPLE) for our purpose. Using this, we evaluate discriminatory power of activity structures along the dimension of statistical features and after a transformation to a sequence of individual locomotive micro-activities (e.g. sitting or standing). Our findings from 152 days of real-life behavioral traces reveal that locomotive signatures achieve an average accuracy of 77.14%, an improvement of 16.37% over …

Identifying Linux Bug Fixing Patches, Yuan Tian, Julia Lawall, David Lo

Research Collection School Of Computing and Information Systems

In the evolution of an operating system there is a continuing tension between the need to develop and test new features, and the need to provide a stable and secure execution environment to users. A compromise, adopted by the developers of the Linux kernel, is to release new versions, including bug fixes and new features, frequently, while maintaining some older “longterm” versions. This strategy raises the problem of how to identify bug fixing patches that are submitted to the current version but should be applied to the longterm versions as well. The current approach is to rely on the individual …

Where Should The Bugs Be Fixed? More Accurate Information Retrieval-Based Bug Localization Based On Bug Reports, Jian Zhou, Hongyu Zhang, David Lo

Research Collection School Of Computing and Information Systems

For a large and evolving software system, the project team could receive a large number of bug reports. Locating the source code files that need to be changed in order to fix the bugs is a challenging task. Once a bug report is received, it is desirable to automatically point out to the files that developers should change in order to fix the bug. In this paper, we propose BugLocator, an information retrieval based method for locating the relevant files for fixing a bug. BugLocator ranks all files based on the textual similarity between the initial bug report and the …

What Does Software Engineering Community Microblog About?, Yuan Tian, Palakorn Achananuparp, Ibrahim Nelman Lubis, David Lo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Microblogging is a new trend to communicate and to disseminate information. One microblog post could potentially reach millions of users. Millions of microblogs are generated on a daily basis on popular sites such as Twitter. The popularity of microblogging among programmers, software engineers, and software users has also led to their use of microblogs to communicate software engineering issues apart from using emails and other traditional communication channels.Understanding how millions of users use microblogs in software engineering related activities would shed light on ways we could leverage the fast evolving microblogging content to aid software development efforts. In this work, …

Modeling Diffusion In Social Networks Using Network Properties, Duc Minh Luu, Ee Peng Lim, Tuan Anh Hoang, Chong Tat Freddy Chua

Research Collection School Of Computing and Information Systems

"Diffusion of items occurs in social networks due to spreading of items through word of mouth and exogenous factors. These items may be news, products, videos, advertisements or contagious viruses. When a user purchases or consumes one of such items, we say that she adopts the item and she becomes an item adopter. Previous research has studied diffusion process at both the macro and micro levels. The former models the number of item adopters in the diffusion process while the latter determines which individuals adopt item. Both macro and micro level models have their merits and limitations. In this paper, …

Virality And Susceptibility In Information Diffusions, Tuan-Anh Hoang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Viral diffusion allows a piece of information to widely and quickly spread within the network of users through word-ofmouth. In this paper, we study the problem of modeling both item and user factors that contribute to viral diffusion in Twitter network. We identify three behaviorial factors, namely user virality, user susceptibility and item virality, that contribute to viral diffusion. Instead of modeling these factors independently as done in previous research, we propose a model that measures all the factors simultaneously considering their mutual dependencies. The model has been evaluated on both synthetic and real datasets. The experiments show that our …

Delayed Observation Planning In Partially Observable Domains, Pradeep Reddy Varakantham, Janusz Marecki

Research Collection School Of Computing and Information Systems

Traditional models for planning under uncertainty such as Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) assume that the observations about the results of agent actions are instantly available to the agent. In so doing, they are no longer applicable to domains where observations are received with delays caused by temporary unavailability of information (e.g. delayed response of the market to a new product). To that end, we make the following key contributions towards solving Delayed observation POMDPs (D-POMDPs): (i) We first provide an parameterized approximate algorithm for solving D-POMDPs efficiently, with desired accuracy; and (ii) We then propose …

Prioritized Shaping Of Models For Solving Dec-Pomdps, Pradeep Reddy Varakantham, William Yeoh, Prasanna Velagapudi, Paul Scerri

Research Collection School Of Computing and Information Systems

An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to …

Dash: A Novel Search Engine For Database-Generated Dynamic Web Pages, Ken C. K. Lee, Kanchan Bankar, Baihua Zheng, Chi-Yin Chow, Honggang Wang

Research Collection School Of Computing and Information Systems

Database-generated dynamic web pages (db-pages, in short), whose contents are created on the fly by web applications and databases, are now prominent in the web. However, many of them cannot be searched by existing search engines. Accordingly, we develop a novel search engine named Dash, which stands for Db-pAge SearcH, to support db-page search. Dash determines db-pages possibly generated by a target web application and its database through exploring the application code and the related database content and supports keyword search on those db-pages. In this paper, we present its system design and focus on the efficiency issue.

To minimize …

Trurepec: A Trust-Behavior-Based Reputation And Recommender System For Mobile Applications, Zheng Yan, Peng Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Mobile applications are software packages that can be installed and executed in a mobile device. Which mobile application is trustworthy for a user to purchase, download, install, execute or recommend becomes a crucial issue that impacts its final success. This paper proposes TruBeRepec, a trust-behavior-based reputation and recommender system for mobile applications. We explore a model of trust behavior for mobile applications based on the result of a large-scale user survey. We further develop a number of algorithms that are used to evaluate individual user’s trust in a mobile application through trust behavior observation, generate the application’s reputation by aggregating …

Vertical Differentiation And A Comparison Of Online Advertising Models, Mei Lin, Xuqing Ke, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

Designing business models that take into consideration the role of advertising support is critical to the success of online services. In this paper, we address the challenges of these business model strategies and compare different ad revenue models. We use game theory to model vertical differentiation in both monopoly and duopoly settings, in which online service providers may offer an ad-free service, an ad-supported service, or a combination of these services. Offering both ad-free and ad-supported services is the optimal strategy for a monopolist because ad revenues compensate for the cannibalistic effect of vertical differentiation. In a duopoly equilibrium, exactly …

Spatial Queries In Wireless Broadcast Environments [Keynote Speech], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Wireless data broadcasting is a promising technique for information dissemination that exploits the computational capabilities of mobile devices, in order to enhance the scalability of the system. Under this environment, the data are continuously broadcast by the server, interleaved with some indexing information for query processing. Clients may tune in the broadcast channel and process their queries locally without contacting the server. In this paper we focus on spatial queries in particular. First, we review existing methods on this topic. Next, taking shortest path computation as an example, we showcase technical challenges arising in this processing model and describe techniques …

#Epicplay: Crowd-Sourcing Sports Video Highlights, Anthony Tang, Sebastian Boring

Research Collection School Of Computing and Information Systems

During a live sports event, many sports fans use social media as a part of their viewing experience, reporting on their thoughts on the event as it unfolds. In this work, we use this information stream to semantically annotate live broadcast sports games, using these annotations to select video highlights from the game. We demonstrate that this approach can be used to select highlights specific for fans of each team, and that these clips reflect the emotions of a fan during a game. Further, we describe how these clips differ from those seen on nightly sportscasts.

Expressive Cp-Abe With Partially Hidden Access Structures, Junzuo Lai, Robert H. Deng, Yingjiu Li

Research Collection School Of Computing and Information Systems

At Eurocrypt 2005, Sahai and Waters [7] introduced the concept of attribute-based encryption (ABE). ABE enables public key based one-to-many encryption and is envisioned as a promising cryptographic primitive for realizing scalable and fine-grained access control systems. There are two kinds of ABE schemes [1], key-policy ABE (KP-ABE) and ciphertext-policy ABE (CP-ABE) schemes. This paper, our concern is on the latter.

Method And Apparatus For Rapid Identification Of Column Heterogeneity, Bing Tian Dai, Nikolaos Koudas, Beng Chin Ooi, Divesh Srivastava, Suresh Venkatasubranmanian

Research Collection School Of Computing and Information Systems

A method and apparatus for rapid identification of column heterogeneity in databases are disclosed. For example, the method receives data associated with a column in a database. The method computes a cluster entropy for the data as a measure of data heterogeneity and then determines whether said data is heterogeneous in accordance with the cluster entropy.

Spatial Queries In Wireless Broadcast Environments [Keynote Speech], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Wireless data broadcasting is a promising technique for information dissemination that exploits the computational capabilities of mobile devices, in order to enhance the scalability of the system. Under this environment, the data are continuously broadcast by the server, interleaved with some indexing information for query processing. Clients may tune in the broadcast channel and process their queries locally without contacting the server. In this paper we focus on spatial queries in particular. First, we review existing methods on this topic. Next, taking shortest path computation as an example, we showcase technical challenges arising in this processing model and describe techniques …

Pamr: Passive-Aggressive Mean Reversion Strategy For Portfolio Selection, Bin Li, Peilin Zhao, Steven C. H. Hoi, Vivekanand Gopalkrishnan

Research Collection School Of Computing and Information Systems

This project proposes a novel online portfolio selection strategy named ``Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We …

Spalendar: Visualizing A Group's Calendar Events Over A Geographic Space On A Public Display, Chen Xiang, Sebastian Boring, Sheelagh Carpendale, Anthony Tang, Saul Greenberg

Research Collection School Of Computing and Information Systems

Portable paper calendars (i.e., day planners and organizers) have greatly influenced the design of group electronic calendars. Both use time units (hours/days/weeks/etc.) to organize visuals, with useful information (e.g., event types, locations, attendees) usually presented as - perhaps abbreviated or even hidden - text fields within those time units. The problem is that, for a group, this visual sorting of individual events into time buckets conveys only limited information about the social network of people. For example, people’s whereabouts cannot be read ‘at a glance’ but require examining the text. Our goal is to explore an alternate visualization that can …

Empirical Studies On Collaboration In Software Development: A Systematic Literature Review, Christoph Treude, Margaret-Anne Storey, Jens Weber

Research Collection School Of Computing and Information Systems

Collaboration in software development is a complex is- sue that has been examined by various researchers over the last decade. This paper presents a systematic literature re- view of pertinent literature on empirical studies on collabo- rative software development. We found that a lot of progress has been achieved in the field of global or distributed de- velopment. While there are still many challenges, today's projects seem to have overcome at least some of those. Also, first steps have been made in understanding the collabora- tive aspects of requirements engineering and design. Re- search on tools for collaboration is advanced, …

Coercion Resistance In Authentication Responsibility Shifting, Payas Gupta, Xuhua Ding, Debin Gao

Research Collection School Of Computing and Information Systems

Responsibility shifting, a popular solution used in the event of failure of primary authentication where a human helper is involved in regaining access, is vulnerable to coercion attacks. In this work, we report our user study which investigates the helper’s emotional status when being coerced to assist in an attack. Results show that the coercion causes involuntary skin conductance fluctuation on the helper, which indicates that he/she is nervous and stressed. This response can be used to strengthen the security of the authentication system by providing coercion resistance.

Student Usage Patterns And Perceptions For Differentiated Lab Exercises In An Undergraduate Programming Course, Heng Ngee Mok

Research Collection School Of Computing and Information Systems

Differentiated instruction in the form of tiered take-home lab exercises was implemented for students of an undergraduate-level programming course. This paper attempts to uncover the perceptions and usage patterns of students toward these new lab exercises using a comprehensive survey. Findings reveal that these tiered exercises are generally very well received and preferred over their traditional "one size fits all" counter-parts. Although the study does not show that tiered exercises have improved proÞciency or scores, it does seem to indicate higher student engagement and motivation levels. Based on the survey results, a list of recommendations is put forth for the …

Organizing User Search Histories, Heasoo Hwang, Hady W. Lauw, Lise Getoor, Alexandros Ntoulas

Research Collection School Of Computing and Information Systems

Users are increasingly pursuing complex task-oriented goals on the web, such as making travel arrangements, managing finances, or planning purchases. To this end, they usually break down the tasks into a few codependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user's historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a …

Vignette: Interactive Texture Design And Manipulation With Freeform Gestures For Pen-And-Ink Illustration, Rubaiat Habib Kazi, Takeo Igarashi, Shengdong Zhao, Richard C. Davis

Research Collection School Of Computing and Information Systems

Vignette is an interactive system that facilitates texture creation in pen-and-ink illustrations. Unlike existing systems, Vignette preserves illustrators' workflow and style: users draw a fraction of a texture and use gestures to automatically fill regions with the texture. We currently support both 1D and 2D synthesis with stitching. Our system also has interactive refinement and editing capabilities to provide a higher level texture control, which helps artists achieve their desired vision. A user study with professional artists shows that Vignette makes the process of illustration more enjoyable and that first time users can create rich textures from scratch within minutes.

Detecting Extreme Rank Anomalous Collections, Hanbo Dai, Feida Zhu, Ee-Peng Lim, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Anomaly or outlier detection has a wide range of applications, including fraud and spam detection. Most existing studies focus on detecting point anomalies, i.e., individual, isolated entities. However, there is an increasing number of applications in which anomalies do not occur individually, but in small collections. Unlike the majority, entities in an anomalous collection tend to share certain extreme behavioral traits. The knowledge essential in understanding why and how the set of entities becomes outliers would only be revealed by examining at the collection level. A good example is web spammers adopting common spamming techniques. To discover this kind of …

Structural Analysis In Multi-Relational Social Networks, Bing Tian Dai, Freddy Chong Tat Chua, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modern social networks often consist of multiple relationsamong individuals. Understanding the structureof such multi-relational network is essential. In sociology,one way of structural analysis is to identify differentpositions and roles using blockmodels. In thispaper, we generalize stochastic blockmodels to GeneralizedStochastic Blockmodels (GSBM) for performing positionaland role analysis on multi-relational networks.Our GSBM generalizes many different kinds of MultivariateProbability Distribution Function (MVPDF) tomodel different kinds of multi-relational networks. Inparticular, we propose to use multivariate Poisson distributionfor multi-relational social networks. Our experimentsshow that GSBM is able to identify the structuresfor both synthetic and real world network data.These structures can further be used for predicting …

Provable De-Anonymization Of Large Datasets With Sparse Dimensions, Anupam Datta, Divya Sharma, Arunesh Sinha

Research Collection School Of Computing and Information Systems

There is a significant body of empirical work on statistical de-anonymization attacks against databases containing micro-dataabout individuals, e.g., their preferences, movie ratings, or transactiondata. Our goal is to analytically explain why such attacks work. Specifically, we analyze a variant of the Narayanan-Shmatikov algorithm thatwas used to effectively de-anonymize the Netflix database of movie ratings. We prove theorems characterizing mathematical properties of thedatabase and the auxiliary information available to the adversary thatenable two classes of privacy attacks. In the first attack, the adversarysuccessfully identifies the individual about whom she possesses auxiliaryinformation (an isolation attack). In the second attack, the adversarylearns additional …

Motivated Learning For The Development Of Autonomous Agents, Janusz A. Starzyk, James T. Graham, Pawel Raif, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically changing environments. In …

Scalable Activity-Travel Pattern Monitoring Framework For Large-Scale City Environment, Youngki Lee, Sangjeong Lee, Byoungjip Kim, Jungwoo Kim, Yunseok Rhee, Junehwa Song

Research Collection School Of Computing and Information Systems

In this paper, we introduce Activity Travel Pattern (ATP) monitoring in a large-scale city environment. ATP represents where city residents and vehicles stay and how they travel around in a complex megacity. Monitoring ATP will incubate new types of value-added services such as predictive mobile advertisement, demand forecasting for urban stores, and adaptive transportation scheduling. To enable ATP monitoring, we develop ActraMon, a high-performance ATP monitoring framework. As a first step, ActraMon provides a simple but effective computational model of ATP and a declarative query language facilitating effective specification of various ATP monitoring queries. More important, ActraMon employs the shared …

Message Passing Algorithms For Map Estimation Using Dc Programming, Akshat Kumar, Shlomo Zilberstein, Marc Toussaint

Research Collection School Of Computing and Information Systems

We address the problem of finding the most likely assignment or MAP estimation in a Markov random field. We analyze the linear programming formulation of MAP through the lens of difference of convex functions (DC) programming, and use the concave-convex procedure (CCCP) to develop efficient message-passing solvers. The resulting algorithms are guaranteed to converge to a global optimum of the well-studied local polytope, an outer bound on the MAP marginal polytope. To tighten the outer bound, we show how to combine it with the mean-field based inner bound and, again, solve it using CCCP. We also identify a useful relationship …