Physical Sciences and Mathematics Commons^™

A Model Checking Framework For Hierarchical Systems., Truong Khanh Nguyen, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

BDD-based symbolic model checking is capable of verifying systems with a large number of states. In this work, we report an extensible framework to facilitate symbolic encoding and checking of hierarchical systems. Firstly, a novel library of symbolic encoding functions for compositional operators (e.g., parallel composition, sequential composition, choice operator, etc.) are developed so that users can apply symbolic model checking techniques to hierarchical systems with little knowledge of symbolic encoding techniques (like BDD or CUDD). Secondly, as the library is language-independent, we build an extensible framework with various symbolic model checking algorithms so that the library can be easily …

Search-Based Fault Localization, Shaowei Wang, David Lo, Lingxiao Jiang, - Lucia, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Many spectrum-based fault localization measures have been proposed in the literature. However, no single fault localization measure completely outperforms others: a measure which is more accurate in localizing some bugs in some programs is less accurate in localizing other bugs in other programs. This paper proposes to compose existing spectrum-based fault localization measures into an improved measure. We model the composition of various measures as an optimization problem and present a search-based approach to explore the space of many possible compositions and output a heuristically near optimal composite measure. We employ two search-based strategies including genetic algorithm and simulated annealing …

Profit-Maximizing Firm Investments In Customer Information Security, Yong Yick Lee, Robert J. Kauffman, Ryan Sougstad

Research Collection School Of Computing and Information Systems

When a customer interacts with a firm, extensive personal information often is gathered without the individual's knowledge. Significant risks are associated with handling this kind of information. Providing protection may reduce the risk of the loss and misuse of private information, but it imposes some costs on both the firm and its customers. Nevertheless, customer information security breaches still may occur. They have several distinguishing characteristics: (1) typically it is hard to quantify monetary damages related to them; (2) customer information security breaches may be caused by intentional attacks, as well as through unintentional organizational and customer behaviors; and (3) …

Software Process Evaluation: A Machine Learning Approach, Ning Chen, Steven C. H. Hoi, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Software process evaluation is essential to improve software development and the quality of software products in an organization. Conventional approaches based on manual qualitative evaluations (e.g., artifacts inspection) are deficient in the sense that (i) they are time-consuming, (ii) they suffer from the authority constraints, and (iii) they are often subjective. To overcome these limitations, this paper presents a novel semi-automated approach to software process evaluation using machine learning techniques. In particular, we formulate the problem as a sequence classification task, which is solved by applying machine learning algorithms. Based on the framework, we define a new quantitative indicator to …

Applying Time-Bound Hierarchical Key Assignment In Wireless Sensor Networks, Wentao Zhu, Robert H. Deng, Jianying Zhou, Feng Bao

Research Collection School Of Computing and Information Systems

Access privileges in distributed systems can be effectively organized as a partial-order hierarchy that consists of distinct security classes, and are often designated with certain temporal restrictions. The time-bound hierarchical key assignment problem is to assign distinct cryptographic keys to distinct security classes according to their privileges so that users from a higher class can use their class key to derive the keys of lower classes, and these keys are time-variant with respect to sequentially allocated temporal units called time slots. In this paper, we explore applications of time-bound hierarchical key assignment in a wireless sensor network environment where there …

Beyond Search: Event-Driven Summarization For Web Videos, Richard Hong, Jinhui Tang, Hung-Khoon Tan, Chong-Wah Ngo, Shuicheng Yan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The explosive growth of Web videos brings out the challenge of how to efficiently browse hundreds or even thousands of videos at a glance. Given an event-driven query, social media Web sites usually return a large number of videos that are diverse and noisy in a ranking list. Exploring such results will be time-consuming and thus degrades user experience. This article presents a novel scheme that is able to summarize the content of video search results by mining and threading "key" shots, such that users can get an overview of main content of these videos at a glance. The proposed …

Enabling Gpu Acceleration With Messaging Middleware, Randall E. Duran, Li Zhang, Tom Hayhurst

Research Collection School Of Computing and Information Systems

Graphics processing units (GPUs) offer great potential for accelerating processing for a wide range of scientific and business applications. However, complexities associated with using GPU technology have limited its use in applications. This paper reviews earlier approaches improving GPU accessibility, and explores how integration with middleware messaging technologies can further improve the accessibility and usability of GPU-enabled platforms. The results of a proof-of-concept integration between an open-source messaging middleware platform and a general-purpose GPU platform using the CUDA framework are presented. Additional applications of this technique are identified and discussed as potential areas for further research.

Coping With Distance: An Empirical Study Of Communication On The Jazz Platform, Renuka Sindhgatta, Bikram Sengupta, Subhajit Datta

Research Collection School Of Computing and Information Systems

Global software development - which is characterized by teams separated by physical distance and/or time-zone differences - has traditionally posed significant communication challenges. Often these have caused delays in completing tasks, or created misalignment across sites leading to re-work. In recent years, however, a new breed of development environments with rich collaboration features have emerged to facilitate cross-site work in distributed projects. In this paper we revisit the question "does distance matter?" in the context of IBM Jazz Platform -- a state-of-the-art collaborative development environment. We study the ecosystem of a large distributed team of around 300 members across 35 …

Learning Human Emotion Patterns For Modeling Virtual Humans, Shu Feng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Emotion modeling is a crucial part in modeling virtual humans. Although various emotion models have been proposed, most of them focus on designing specific appraisal rules. As there is no unified framework for emotional appraisal, the appraisal variables have to be defined beforehand and evaluated in a subjective way. In this paper, we propose an emotion model based on machine learning methods by taking the following position: an emotion model should mirror actual human emotion in the real world and connect tightly with human inner states, such as drives, motivations and personalities. Specifically, a self-organizing neural model called Emotional Appraisal …

Managing Successive Generation Product Diffusion In The Presence Of Strategic Consumers, Zhiling Guo

Research Collection School Of Computing and Information Systems

Frequent new product release and technological uncertainty about the release time pose significant challenges for firms to manage successive generation of products. On the one hand, strategic consumers may delay their purchase decision and substitute the earlier generation with the newer generation product. On the other hand, the firm must fully anticipate consumer reactions and take into account the effect of their strategic behavior on product pricing and successive generation product diffusion. This paper proposes a prediction market to forecast new product release. We show that the market information aggregation mechanism can improve forecast accuracy of new product launch. Better …

Are There Contagion Effects In Information Technology And Business Process Outsourcing?, Arti Mann, Robert J. Kauffman, Kunsoo Han, Barrie R. Nault

Research Collection School Of Computing and Information Systems

We model the diffusion of IT outsourcing using announcements about IT outsourcing deals. We estimate a lognormal diffusion curve to test whether IT outsourcing follows a pure diffusion process or there are contagion effects involved. The methodology permits us to study the consequences of outsourcing events, especially mega-deals with IT contract amounts that exceed US$1 billion. Mega-deals act, we theorize, as precipitating events that create a strong basis for contagion effects and are likely to affect decision-making by other firms in an industry. Then, we evaluate the role of different communication channels in the diffusion process of IT outsourcing by …

Pat 3: An Extensible Architecture For Building Multi-Domain Model Checkers, Yang Liu, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Model checking is emerging as an effective software verification method. Although it is desirable to have a dedicated model checker for each application domain, implementing one is rather challenging. In this work, we develop an extensible and integrated architecture in PAT3 (PAT version 3.*) to support the development of model checkers for wide range application domains. PAT3 adopts a layered design with an intermediate representation layer (IRL), which separates modeling languages from model checking algorithms so that the algorithms can be shared by different languages. IRL contains several common semantic models to support wide application domains, and builds both explicit …

A Pomdp Model For Guiding Taxi Cruising In A Congested Urban City, Lucas Agussurja, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider a partially observable Markov decision process (POMDP) model for improving a taxi agent cruising decision in a congested urban city. Using real-world data provided by a large taxi company in Singapore as a guide, we derive the state transition function of the POMDP. Specifically, we model the cruising behavior of the drivers as continuous-time Markov chains. We then apply dynamic programming algorithm for finding the optimal policy of the driver agent. Using a simulation, we show that this policy is significantly better than a greedy policy in congested road network.

Finding Relevant Answers In Software Forums, Swapna Gottopati, David Lo, Jing Jiang

Research Collection School Of Computing and Information Systems

Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containing relevant keywords. However, in software forums, often there are many threads containing similar keywords where each thread could contain a lot of posts as many as 1,000 or more. Manually finding relevant answers from these long threads is a painstaking task to …

Consistent Community Identification In Complex Networks, Haewoon Kwak, Young-Ho Eom, Yoonchan Choi, Hawoong Jeong

Research Collection School Of Computing and Information Systems

We have found that known community identification algorithms produce inconsistent communities when the node ordering changes at input. We use the pairwise membership probability and consistency to quantify the level of consistency across multiple runs of an algorithm. Based on these two metrics, we address the consistency problem without compromising the modularity. The key insight of the algorithm is to use pairwise membership probabilities as link weights. It offers a new tool in the study of community structures and their evolutions.

Towards Trajectory-Based Experience Sharing In A City, Byoungjip Kim, Youngki Lee, Sangjeong Lee, Yunseok Rhee, Junehwa Song

Research Collection School Of Computing and Information Systems

As location-aware mobile devices such as smartphones have now become prevalent, people are able to easily record their trajectories in daily lives. Such personal trajectories are a very promising means to share their daily life experiences, since important contextual information such as significant locations and activities can be extracted from the raw trajectories. In this paper, we propose MetroScope, a trajectory-based real-time and on-the-go experience sharing system in a metropolitan city. MetroScope allows people to share their daily life experiences through trajectories, and enables them to refer to other people's diverse and interesting experiences in a city. Eventually, MetroScope aims …

Unsupervised Multiple Kernel Learning, Jinfeng Zhuang, Jialei Wang, Steven C. H. Hoi, Xiangyang Lan

Research Collection School Of Computing and Information Systems

Traditional multiple kernel learning (MKL) algorithms are essentially supervised learning in the sense that the kernel learning task requires the class labels of training data. However, class labels may not always be available prior to the kernel learning task in some real world scenarios, e.g., an early preprocessing step of a classification task or an unsupervised learning task such as dimension reduction. In this paper, we investigate a problem of Unsupervised Multiple Kernel Learning (UMKL), which does not require class labels of training data as needed in a conventional multiple kernel learning task. Since a kernel essentially defines pairwise similarity …

Exploring Tweets Normalization And Query Time Sensitivity For Twitter Search, Zhongyu Wei, Wei Gao, Lanjun Zhou, Binyang Li, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

This paper presents our work for the Realtime Adhoc Task of TREC 2011 Microblog Track. Microblog texts like tweets are generally characterized by the inclusion of a large proportion of irregular expressions, such as ill-formed words, which can lead to significant mismatch between query terms and tweets. In addition, Twitter queries are distinguished from Web queries with many unique characteristics, one of which reflects the clearly distinct temporal aspects of Twitter search behavior. In this study, we deal with the first problem by normalizing tweet texts and the second by capturing the temporal characteristics of topic. We divided topics into …

Price Points And Price Rigidity, Daniel Levy, Dongwon Lee, Haipeng (Allen) Lee, Robert J. Kauffman, Mark Bergen

Research Collection School Of Computing and Information Systems

We study the link between price points and price rigidity using two data sets: weekly scanner data and Internet data. We find that ‘‘9’’ is the most frequent ending for the penny, dime, dollar, and ten-dollar digits; the most common price changes are those that keep the price endings at ‘‘9’’; 9-ending prices are less likely to change than non-9-ending prices; and the average size of price change is larger for 9-ending than non-9- ending prices. We conclude that 9-ending contributes to price rigidity from penny to dollar digits and across a wide range of product categories, retail formats, and …

A Brain-Inspired Model Of Hierarchical Planner, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Hierarchical planning is an approach of planning by composing and executing hierarchically arranged plans to solve some problems. Most symbolic-based hierarchical planners have been devised to allow the knowledge to be described expressively. However, a great challenge is to automatically seek and acquire new plans on the fly. This paper presents a novel neural-based model of hierarchical planning that can seek and acquired new plans on-line if the necessary knowledge are lacking. Inspired by findings in neuropsychology, plans can be inherently learnt, retrieved, and manipulated simultaneously rather than discretely processed like in most symbolic approaches. Using a multi-channel adaptive resonance …

Virality Modeling And Analysis, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Virality is a virus-like behavior that allows a piece of information to widely and quickly diffuse within the network of adopters through word of mouth. It is about how easy users propagate information to their friends and friends of friends by means of diffusion. While virality of information has several interesting applications, there are much research to be conducted on virality. These areas of research include understanding the mechanism of virality, modeling the virality both qualitatively and quantitatively, and applying virality to applications such as marketing, event detection, and others. In this paper, we survey existing works on quantitative models …

Active Multiple Kernel Learning For Interactive 3d Object Retrieval Systems, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. …

Strategic Responses To Standardization: Embrace, Extend Or Extinguish?, C. Jason Woodard, Joel West

Research Collection School Of Computing and Information Systems

Prior research on technology standardization has focused on two common patterns: processes in which product developers and other stakeholders cooperate to achieve a consensus outcome, and “standards wars” in which competing technologies vie for dominance in the market. This study examines Microsoft's responses to 12 software technologies in the period between 1990 and 2005. Despite the company's reputed tendency to pursue a strategy dubbed “embrace, extend, and extinguish,” a content analysis of news articles from the same period reveals surprising diversity in Microsoft's responses at the product level.

We classify these responses using a typology that treats “embrace” and “extend” …

Influence Diagrams With Memory States: Representation And Algorithms, Xiaojian Wu, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Influence diagrams (IDs) offer a powerful framework for decision making under uncertainty, but their applicability has been hindered by the exponential growth of runtime and memory usage--largely due to the no-forgetting assumption. We present a novel way to maintain a limited amount of memory to inform each decision and still obtain near-optimal policies. The approach is based on augmenting the graphical model with memory states that represent key aspects of previous observations--a method that has proved useful in POMDP solvers. We also derive an efficient EM-based message-passing algorithm to compute the policy. Experimental results show that this approach produces highquality …

Location-Dependent Spatial Query Containment, Ken C. K. Lee, Brandon Unger, Baihua Zheng, Wang-Chien Lee

Research Collection School Of Computing and Information Systems

Nowadays, location-related information is highly accessible to mobile users via issuing Location-Dependent Spatial Queries (LDSQs) with respect to their locations wirelessly to Location-Based Service (LBS) servers. Due to the limited mobile device battery energy, scarce wireless bandwidth, and heavy LBS server workload, the number of LDSQs submitted over wireless channels to LBS servers for evaluation should be minimized as appropriate. In this paper, we exploit query containment techniques for LDSQs (called LDSQ containment) to enable mobile clients to determine whether the result of a new LDSQ Q′ is completely covered by that of another LDSQ Q previously answered by a …

Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Social networking has grown rapidly over the last few years, and social networks contain a huge amount of content. However, it can be not easy to navigate the social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified. CANN considers both the structure of the social network, and the profile information of the nodes. We design ahyper-graph based index structure to support approximated CANN search efficiently.

An Efficient Algorithm For Learning Event-Recording Automata, Shang-Wei Lin, Étienne André, Jin Song Dong, Jun Sun, Yang Liu

Research Collection School Of Computing and Information Systems

In inference of untimed regular languages, given an unknown language to be inferred, an automaton is constructed to accept the unknown language from answers to a set of membership queries each of which asks whether a string is contained in the unknown language. One of the most well-known regular inference algorithms is the L* algorithm, proposed by Angluin in 1987, which can learn a minimal deterministic finite automaton (DFA) to accept the unknown language. In this work, we propose an efficient polynomial time learning algorithm, TL*, for timed regular language accepted by event-recording automata. Given an unknown timed regular language, …

Verification Of Orchestration Systems Using Compositional Partial Order Reduction, Tian Huat Tan, Yang Liu, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Orc is a computation orchestration language which is designed to specify computational services, such as distributed communication and data manipulation, in a concise and elegant way. Four concurrency primitives allow programmers to orchestrate site calls to achieve a goal, while managing timeouts, priorities, and failures. To guarantee the correctness of Orc model, effective verification support is desirable. Orc has a highly concurrent semantics which introduces the problem of state-explosion to search-based verification methods like model checking. In this paper, we present a new method, called Compositional Partial Order Reduction (CPOR), which aims to provide greater state-space reduction than classic partial …

Differencing Labeled Transition Systems, Zhenchang Xing, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Concurrent programs often use Labeled Transition Systems (LTSs) as their operational semantic models, which provide the basis for automatic system analysis and verification. System behaviors (generated from the operational semantics) evolve as programs evolve for fixing bugs or implementing new user requirements. Even when a program remains unchanged, its LTS models explored by a model checker or analyzer may be different due to the application of different exploration methods. In this paper, we introduce a novel approach (named SpecDiff) to computing the differences between two LTSs, representing the evolving behaviors of a concurrent program. SpecDiff considers LTSs as Typed Attributed …

Cooperative Reinforcement Learning In Topology-Based Multi-Agent Systems, Dan Xiao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Topology-based multi-agent systems (TMAS), wherein agents interact with one another according to their spatial relationship in a network, are well suited for problems with topological constraints. In a TMAS system, however, each agent may have a different state space, which can be rather large. Consequently, traditional approaches to multi-agent cooperative learning may not be able to scale up with the complexity of the network topology. In this paper, we propose a cooperative learning strategy, under which autonomous agents are assembled in a binary tree formation (BTF). By constraining the interaction between agents, we effectively unify the state space of individual …