Computer Sciences Commons^™

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 …

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) …

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 …

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.

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.

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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” …

A Survey Of Techniques And Challenges In Underwater Localization, Hwee-Pink Tan, Roee Diamant, Winston K. G. Seah, Marc Waldmeyer

Research Collection School Of Computing and Information Systems

Underwater Wireless Sensor Networks (UWSNs) are expected to support a variety of civilian and military applications. Sensed data can only be interpreted meaningfully when referenced to the location of the sensor, making localization an important problem. While global positioning system (GPS) receivers are commonly used in terrestrial WSNs to achieve this, this is infeasible in UWSNs as GPS signals do not propagate through water. Acoustic communications is the most promising mode of communication underwater. However, underwater acoustic channels are characterized by harsh physical layer conditions with low bandwidth, high propagation delay and high bit error rate. Moreover, the variable speed …

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. …

An Exploratory Study Of Software Reverse Engineering In A Security Context, Christoph Treude, Fernando Figueira Filho, Margaret-Anne Storey, Martin Salois

Research Collection School Of Computing and Information Systems

Illegal cyberspace activities are increasing rapidly and many software engineers are using reverse engineering methods to respond to attacks. The security-sensitive nature of these tasks, such as the understanding of malware or the decryption of encrypted content, brings unique challenges to reverse engineering: work has to be done offline, files can rarely be shared, time pressure is immense, and there is a lack of tool and process support for capturing and sharing the knowledge obtained while trying to understand plain assembly code. To help us gain an understanding of this reverse engineering work, we report on an exploratory study done …

Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain

Research Collection School Of Computing and Information Systems

We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last …

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. …

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, …

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 …

Concern Localization Using Information Retrieval: An Empirical Study On Linux Kernel, Shaowei Wang, David Lo, Zhenchang Xing, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Many software maintenance activities need to find code units (functions, files, etc.) that implement a certain concern (features, bugs, etc.). To facilitate such activities, many approaches have been proposed to automatically link code units with concerns described in natural languages, which are termed as concern localization and often employ Information Retrieval (IR) techniques. There has not been a study that evaluates and compares the effectiveness of latest IR techniques on a large dataset. This study fills this gap by investigating ten IR techniques, some of which are new and have not been used for concern localization, on a Linux kernel …

Direction-Based Surrounder Queries For Mobile Recommendations, Xi Guo, Baihua Zheng, Yoshiharu Ishikawa, Yunjun Gao

Research Collection School Of Computing and Information Systems

Location-based recommendation services recommend objects to the user based on the user’s preferences. In general, the nearest objects are good choices considering their spatial proximity to the user. However, not only the distance of an object to the user but also their directional relationship are important. Motivated by these, we propose a new spatial query, namely a direction-based surrounder (DBS) query, which retrieves the nearest objects around the user from different directions. We define the DBS query not only in a two-dimensional Euclidean space E">EE but also in a road network R">RR . In the Euclidean space E" …

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 …

Adaptive Collision Resolution For Efficient Rfid Tag Identification, Yung-Chun Chen, Kuo-Hui Yeh, Nai-Wei Lo, Yingjiu Li, Enrico Winata

Research Collection School Of Computing and Information Systems

In large-scale RFID systems, all of the communications between readers and tags are via a shared wireless channel. When a reader intends to collect all IDs from numerous existing tags, a tag identification process is invoked by the reader to collect the tags' IDs. This phenomenon results in tag-to-reader signal collisions which may suppress the system performance greatly. To solve this problem, we design an efficient tag identification protocol in which a significant gain is obtained in terms of both identification delay and communication overhead. A k-ary tree-based abstract is adopted in our proposed tag identification protocol as underlying architecture …

Using Social Annotations For Trend Discovery In Scientific Publications, Meiqun Hu, Ee Peng Lim, Jing Jiang

Research Collection School Of Computing and Information Systems

Social tags and citing documents are two forms of social annotations to scientific publications. These social annotations provide useful contextual and temporal information for the annotated work, which encapsulates the attention and interest of the annotators. In this work, we explore the use of social annotations for discovering trends in scientific publications. We propose a trend discovery process that employs trend estimation and trend selection and ranking for analyzing the emerging trends shown in the social annotation profiles. The proposed sigmoid trend estimator allows us to characterize and compare how much, when and how fast the trends emerge. To perform …

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 …

A Survey Of Information Diffusion Models And Relevant Problems, Minh Duc Luu, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

There has been tremendous interest in diffusion of innovations or information in a social system. Nowadays, social networks (offline as well as online) are considered as important medium for diffusion and large amount of research has been conducted to understand the dynamics of diffusion in social networks. In this work, we review some of the models proposed for diffusion in social networks. We also highlight the major features of these models by dividing the surveyed models into two categories: non-network and network diffusion models. The former refers to user communities without any knowledge about the user relationship network and the …

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 …