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Articles 5521 - 5550 of 8723
Full-Text Articles in Entire DC Network
User Daily Activity Pattern Learning: A Multi-Memory Modeling Approach, Shan Gao, Ah-Hwee Tan
User Daily Activity Pattern Learning: A Multi-Memory Modeling Approach, Shan Gao, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In this paper, we propose a multi-memory model, ADLART model, to discover the daily activity pattern of a sensor monitored user from his/her activities of daily living (ADL). The proposed model mimics the human multiple memory system which comprises a working memory, an episodic memory, and a semantic memory. Through encoding user's daily activities patterns in episodic memory and extracting the regularities of activity routines in semantic memory, the ADLART system is able to learn, recognize, compare, and retrieve daily ADL patterns of the user. Experiments are presented to show the performance of the ADLART model using different parameter settings …
A Novel Algorithm Based On Visual Saliency Attention For Localization And Segmentation In Rapidly-Stained Leukocyte Images, Xin Zheng, Yong Wang, Guoyou Wang, Zhong Chen
A Novel Algorithm Based On Visual Saliency Attention For Localization And Segmentation In Rapidly-Stained Leukocyte Images, Xin Zheng, Yong Wang, Guoyou Wang, Zhong Chen
Research Collection School Of Computing and Information Systems
In this paper, we propose a fast hierarchical framework of leukocyte localization and segmentation in rapidly-stained leukocyte images (RSLI) with complex backgrounds and varying illumination. The proposed framework contains two main steps. First, a nucleus saliency model based on average absolute difference is built, which locates each leukocyte precisely while effectively removes dyeing impurities and erythrocyte fragments. Secondly, two different schemes are presented for segmenting the nuclei and cytoplasm respectively. As for nuclei segmentation, to solve the overlap problem between leukocytes, we extract the nucleus lobes first and further group them. The lobes extraction is realized by the histogram-based contrast …
On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim
On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to …
Scalable Detection Of Missed Cross-Function Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Scalable Detection Of Missed Cross-Function Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Refactoring is an important way to improve the design of existing code. Identifying refactoring opportunities (i.e., code fragments that can be refactored) in large code bases is a challenging task. In this paper, we propose a novel, automated and scalable technique for identifying cross-function refactoring opportunities that span more than one function (e.g., Extract Method and Inline Method). The key of our technique is the design of efficient vector inlining operations that emulate the effect of method inlining among code fragments, so that the problem of identifying cross-function refactoring can be reduced to the problem of finding similar vectors before …
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.
The Triangular Relationship Between Audit Committee Characteristics, Audit Inputs, And Financial Reporting Quality, Jae Bum Kim, Benjamin Segal, Dan Segal, Yoonseok Zang
The Triangular Relationship Between Audit Committee Characteristics, Audit Inputs, And Financial Reporting Quality, Jae Bum Kim, Benjamin Segal, Dan Segal, Yoonseok Zang
Research Collection School Of Accountancy
Using the exogenous reforms to audit committees mandated by the Sarbanes-Oxley Act of 2002 and a difference-in-difference approach, we examine the impact of changes in audit committee attributes (financial expertise, size, and independence) on firms’ audit inputs and financial reporting quality. Firms directly affected by the reforms experienced a larger improvement in audit inputs (measured by audit fees and the appointment of an industry specialist auditor) and a larger increase in financial reporting quality (measured by restatements of financial reports) relative to firms that were already compliant. Importantly, we find that the decline in restatements is not related to the …
Air Indexing For On-Demand Xml Data Broadcast, Weiwei Sun, Rongrui Qin, Jinjin Wu, Baihua Zheng
Air Indexing For On-Demand Xml Data Broadcast, Weiwei Sun, Rongrui Qin, Jinjin Wu, Baihua Zheng
Research Collection School Of Computing and Information Systems
XML data broadcast is an efficient way to disseminate semi-structured information in wireless mobile environments. In this paper, we propose a novel two-tier index structure to facilitate the access of XML document in an on-demand broadcast system. It provides the clients with an overall image of all the XML documents available at the server side and hence enables the clients to locate complete result sets accordingly. A pruning strategy is developed to cut down the index size and a two-tier structure is proposed to further remove any redundant information. In addition, two index distribution strategies, namely naive distribution and partial …
Ranking-Based Approaches For Localizing Faults, Lucia Lucia
Ranking-Based Approaches For Localizing Faults, Lucia Lucia
Dissertations and Theses Collection (Open Access)
A fault is the root cause of program failures where a program behaves differently from the intended behavior. Finding or localizing faults is often laborious (especially so for complex programs), yet it is an important task in the software lifecycle. An automated technique that can accurately and quickly identify the faulty code is greatly needed to alleviate the costs of software debugging. Many fault localization techniques assume that faults are localizable, i.e., each fault manifests only in a single or a few lines of code that are close to one another. To verify this assumption, we study how faults spread …
Three Essays On Nonstationary Time Series Analysis, Ye Chen
Three Essays On Nonstationary Time Series Analysis, Ye Chen
Dissertations and Theses Collection (Open Access)
Financial and macroeconomic time series data are often nonstationary. My dissertation consists of three essays concerning time series models with nonstationarity. Chapter 1 develops a new jackknife estimator for nonstationary autoregressive model. The remaining two chapters explore the restricted maximum likelihood (REML hereafter) estimation and the restricted maximum likelihood based likelihood ratio test (RLRT hereafter) in predictive regression. Chapter 1 proposes an improved jackknife estimator of the persistence parameter that works for both the discrete time unit root model and the continuous time unit root model. Maximum likelihood estimation of the persistence parameter in the discrete time unit root model …
Recommendation Support For Multi-Attribute Databases, Jilian Zhang
Recommendation Support For Multi-Attribute Databases, Jilian Zhang
Dissertations and Theses Collection (Open Access)
This dissertation studies the subject of providing recommendation support for multi-attribute databases. Recommendation is an important and very useful information evaluation mechanism that explores a database of huge volume, and retrieves from it the interesting data items (tuples) for users based on their preferences.
Deviance Information Criterion For Comparing Var Models, Tao Zeng, Yong Li, Jun Yu
Deviance Information Criterion For Comparing Var Models, Tao Zeng, Yong Li, Jun Yu
Research Collection School Of Economics
Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li et al. (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.
Extremal Quantile Regressions For Selection Models And The Black White Wage Gap, Xavier D'Haultfoeuille, Arnaud Maurel, Yichong Zhang
Extremal Quantile Regressions For Selection Models And The Black White Wage Gap, Xavier D'Haultfoeuille, Arnaud Maurel, Yichong Zhang
Research Collection School Of Economics
We consider the estimation of a semiparametric location-scale model subject to endogenous selection, in the absence of an instrument or a large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. In this context, we propose a simple estimator, which combines extremal quantile regressions with minimum distance. We establish the asymptotic normality of this estimator by extending previous results on extremal quantile regressions to allow for selection. Finally, we apply our method to estimate the black-white wage gap among males from the NLSY79 and NLSY97. We find that premarket factors …
La Cuestionada Deseabilidad Económica De La Business Judgment Rule En El Derecho Español, Aurelio Gurrea-Martinez
La Cuestionada Deseabilidad Económica De La Business Judgment Rule En El Derecho Español, Aurelio Gurrea-Martinez
Research Collection Yong Pung How School Of Law
Spanish Abstract: El presente trabajo pretende evaluar la deseabilidad económica de la business judgment rule en el Derecho español. En este sentido, y a pesar de los beneficios generalmente asociados a la protección de la discrecionalidad empresarial de los administradores sociales, no creemos que la implementación de esta regla resulte necesaria ni, de hecho, deseable en el Derecho español, al menos, en los términos propuestos por la Comisión de Expertos en materia de Gobierno Corporativo y recientemente incorporada en la normativa española de sociedades. En primer lugar, no creemos que la instauración de la business judgment rule resulte necesaria ya …
Daisy Filter Flow: A Generalized Discrete Approach To Dense Correspondences, Hongsheng Yang, Wen-Yan Lin, Jiangbo Lu
Daisy Filter Flow: A Generalized Discrete Approach To Dense Correspondences, Hongsheng Yang, Wen-Yan Lin, Jiangbo Lu
Research Collection School Of Computing and Information Systems
No abstract provided.
Graph-Based Semi-Supervised Learning: Realizing Pointwise Smoothness Probabilistically, Yuan Fang, Kevin Chen-Chuan Chang, Hady W. Lauw
Graph-Based Semi-Supervised Learning: Realizing Pointwise Smoothness Probabilistically, Yuan Fang, Kevin Chen-Chuan Chang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
As the central notion in semi-supervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graph-based work exploits them in conjunction, we encompass both in a novel framework of Probabilistic Graph-based Pointwise Smoothness (PGP), building upon two foundational models of data closeness and label coupling. This new form of smoothness axiomatizes a set of probability constraints, which ultimately enables class prediction. Theoretically, we provide an error and robustness analysis of PGP. Empirically, we conduct extensive experiments to show …
Cross-Language Bug Localization, Xin Xia, David Lo, Xingen Wang, Chenyi Zhang, Xinyu Wang
Cross-Language Bug Localization, Xin Xia, David Lo, Xingen Wang, Chenyi Zhang, Xinyu Wang
Research Collection School Of Computing and Information Systems
Bug localization refers to the process of identifying source code files that contain defects from textual descriptions in bug reports. Existing bug localization techniques work on the assumption that bug reports, and identifiers and comments in source code files, are written in the same language (i.e., English). However, software users from non-English speaking countries (e.g., China) often use their native languages (e.g., Chinese) to write bug reports. For this setting, existing studies on bug localization would not work as the terms that appear in the bug reports do not appear in the source code. We refer to this problem as …
Competing Logics Of Commemoration: Cosmopolitanism And Nationalism In East Asia's History Problem, Hiro Saito, Yoko Wang
Competing Logics Of Commemoration: Cosmopolitanism And Nationalism In East Asia's History Problem, Hiro Saito, Yoko Wang
Research Collection School of Social Sciences
Recent studies in collective memory point to the emergence of cosmopolitan commemoration that takes humanity, rather than nationality, as a primary frame of reference. But these studies have yet to specify how cosmopolitan commemoration emerges and articulates with existing nationalist commemoration. To solve this problem, we examine the “history problem” between Japan and South Korea by focusing on how relevant political and civic actors negotiated cosmopolitanism and nationalism in commemorating Japan’s past colonial rule and wartime atrocities. In light of our historical analysis, we argue that a synthesis of theories of institutional logics and social movements is useful in illuminating …
“Monetary And Currency Policy Management In Asia” By M. Kawai, P.J. Morgan And S. Takaji [Book Review], Hwee Kwan Chow-Tan
“Monetary And Currency Policy Management In Asia” By M. Kawai, P.J. Morgan And S. Takaji [Book Review], Hwee Kwan Chow-Tan
Research Collection School Of Economics
No abstract provided.
Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao
Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao
Research Collection School Of Economics
Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li et al. (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.
A Bayesian Chi-Squared Test For Hypothesis Testing, Yong Li, Xiao-Bin Liu, Jun Yu
A Bayesian Chi-Squared Test For Hypothesis Testing, Yong Li, Xiao-Bin Liu, Jun Yu
Research Collection School Of Economics
A new Bayesian test statistic is proposed to test a point null hypothesis based on of regular conditions and follows a chi-squared distribution when the null hypothesis is correct. The new statistic has several important advantages that make it appeal in practical applications. First, it is well-defined under improper prior distributions. Second, it avoids Jeffrey-Lindley’s paradox. Third, it is relatively easy to compute, even for models with latent variables. Finally, it is pivotal and its threshold value can be easily obtained from the asymptotic chi-squared distribution. The method is illustrated using some real examples in economics and finance.
Debate On Bernard Yack’S Book Nationalism And The Moral Psychology Of Community, Jonathan Hearn, Chandran Kukathas, David Miller, Bernard Yack
Debate On Bernard Yack’S Book Nationalism And The Moral Psychology Of Community, Jonathan Hearn, Chandran Kukathas, David Miller, Bernard Yack
Research Collection School of Social Sciences
Bernard Yack's Nationalism and the Moral Psychology of Community (2012) was the subject of the eighth in a long‐running series of debates hosted by the Association for the Study of Ethnicity and Nationalism (ASEN) and the journal Nations and Nationalism in November of 2013. These debates bring together the authors of recent important works in the study of nationalism and ethnicity with appropriate scholars to explore the questions they have provoked. On this occasion Professor Yack was joined by Professor Chandran Kukathas and Professor David Miller.
Ar-Miner: Mining Informative Reviews For Developers From Mobile App Marketplace, Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, Boshen Zhang
Ar-Miner: Mining Informative Reviews For Developers From Mobile App Marketplace, Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, Boshen Zhang
Research Collection School Of Computing and Information Systems
With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by …
Version History, Similar Report, And Structure: Putting Them Together For Improved Bug Localization, Shaowei Wang, David Lo
Version History, Similar Report, And Structure: Putting Them Together For Improved Bug Localization, Shaowei Wang, David Lo
Research Collection School Of Computing and Information Systems
During the evolution of a software system, a large number of bug reports are submitted. Locating the source code files that need to be fixed to resolve the bugs is a challenging problem. Thus, there is a need for a technique that can automatically figure out these buggy files. A number of bug localization solutions that take in a bug report and output a ranked list of files sorted based on their likelihood to be buggy have been proposed in the literature. However, the accuracy of these tools still need to be improved. In this paper, to address this need, …
Towards More Accurate Content Categorization Of Api Discussions, Bo Zhou, Xin Xia, David Lo, Cong Tian, Xinyu Wang
Towards More Accurate Content Categorization Of Api Discussions, Bo Zhou, Xin Xia, David Lo, Cong Tian, Xinyu Wang
Research Collection School Of Computing and Information Systems
Nowadays, software developers often discuss the usage of various APIs in online forums. Automatically assigning pre-defined semantic categorizes to API discussions in these forums could help manage the data in online forums, and assist developers to search for useful information. We refer to this process as content categorization of API discussions. To solve this problem, Hou and Mo proposed the usage of naive Bayes multinomial, which is an effective classification algorithm. In this paper, we propose a Cache-bAsed compoSitE algorithm, short formed as CASE, to automatically categorize API discussions. Considering that the content of an API discussion contains both textual …
Condensing Class Diagrams By Analyzing Design And Network Metrics Using Optimistic Classification, Ferdian Thung, David Lo, Mohd Hafeez Osman, Michel R.V. Chaudron
Condensing Class Diagrams By Analyzing Design And Network Metrics Using Optimistic Classification, Ferdian Thung, David Lo, Mohd Hafeez Osman, Michel R.V. Chaudron
Research Collection School Of Computing and Information Systems
A class diagram of a software system enhances our ability to understand software design. However, this diagram is often unavailable. Developers usually reconstruct the diagram by reverse engineering it from source code. Unfortunately, the resultant diagram is often very cluttered; making it difficult to learn anything valuable from it. Thus, it would be very beneficial if we are able to condense the reverse- engineered class diagram to contain only the important classes depicting the overall design of a software system. Such diagram would make program understanding much easier. A class can be important, for example, if its removal would break …
An Air Index For Spatial Query Processing In Road Networks, Weiwei Sun, Chunan Chen, Baihua Zheng, Chong Chen, Peng Liu
An Air Index For Spatial Query Processing In Road Networks, Weiwei Sun, Chunan Chen, Baihua Zheng, Chong Chen, Peng Liu
Research Collection School Of Computing and Information Systems
Spatial queries such as range query and kNN query in road networks have received a growing number of attention in real life. Considering the large population of the users and the high overhead of network distance computation, it is extremely important to guarantee the efficiency and scalability of query processing. Motivated by the scalable and secure properties of wireless broadcast model, this paper presents an air index called Network Partition Index (NPI) to support efficient spatial query processing in road networks via wireless broadcast. The main idea is to partition the road network into a number of regions and then …
Industry-Based Style Investing, Russell Jame, Qing Tong
Industry-Based Style Investing, Russell Jame, Qing Tong
Research Collection Lee Kong Chian School Of Business
Motivated by the style investing model of Barberis and Shleifer (2003), we examine the industry-wide investment decisions of retail investors. We find that retail investor industry demand is highly correlated and strongly related to past industry returns. Moreover, industries heavily bought by retail investors over the past year significantly underperform industries heavily sold over the subsequent year. Similarly, stocks in industries heavily bought by retail investors underperform stocks in industries heavily sold, even after controlling for firm-level demand. Our results suggest that industry-wide categorization influences the investment decisions of retail investors and has a significant impact on asset prices.
Cost Growth And Stock Returns, Dashan Huang, Fuwei Jiang, Jun Tu, Guofu Zhou
Cost Growth And Stock Returns, Dashan Huang, Fuwei Jiang, Jun Tu, Guofu Zhou
Research Collection Lee Kong Chian School Of Business
In this paper, we examine the valuation implications of cost behavior, and find that firms with high growth rate in operating costs generate substantially lower future stock returns than those with low cost growth. A spread portfolio of long stocks with low cost growth and short stocks with high cost growth earns an average abnormal return of 12% per year. This strategy is robust over time, across market capitalization, and to control for alternative anomalies and risks. Cost growth is strongly associated with deterioration in a firm’s future profitability, which investors appear failing to incorporate into valuation. In addition, the …
Of Accessibility And Applicability: How Heat-Related Primes Affect Belief In “Global Warming” And “Climate Change”, Jonathon P. Schuldt, Sungjong Roh
Of Accessibility And Applicability: How Heat-Related Primes Affect Belief In “Global Warming” And “Climate Change”, Jonathon P. Schuldt, Sungjong Roh
Research Collection Lee Kong Chian School Of Business
Research shows that exposure to heat-related cues (e.g., warm temperatures, “fry” and “boil”) influences the belief that global warming exists and poses a serious threat to humans. Drawing on social-cognitive principles of concept accessibility and applicability, we hypothesized that these effects may depend on how the issue is framed, given that heat-related concepts are more compatible with “global warming” than “climate change.” Exploring this possibility, we asked campus passersby about their belief in global warming or climate change shortly after a real-life unseasonably cold weather event (i.e., snowfall during Spring; Study 1). A controlled Web experiment …
Revisiting Risk-Sensitive Mdps: New Algorithms And Results, Ping Hou, William Yeoh, Pradeep Reddy Varakantham
Revisiting Risk-Sensitive Mdps: New Algorithms And Results, Ping Hou, William Yeoh, Pradeep Reddy Varakantham
Research Collection School Of Computing and Information Systems
While Markov Decision Processes (MDPs) have been shown to be effective models for planning under uncertainty, theobjective to minimize the expected cumulative cost is inappropriate for high-stake planning problems. As such, Yu, Lin, and Yan (1998) introduced the Risk-Sensitive MDP (RSMDP) model, where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, we revisit this problem and introduce new algorithms that are based on classical techniques, such as depth-first search and dynamic programming, and a recently introduced technique called Topological Value Iteration (TVI). We demonstrate …