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Full-Text Articles in Entire DC Network
Robust Execution Strategies For Project Scheduling With Unreliable Resources And Stochastic Durations, Na Fu, Hoong Chuin Lau, Pradeep Varakantham
Robust Execution Strategies For Project Scheduling With Unreliable Resources And Stochastic Durations, Na Fu, Hoong Chuin Lau, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
The resource-constrained project scheduling problem with minimum and maximum time lags (RCPSP/max) is a general model for resource scheduling in many real-world problems (such as manufacturing and construction engineering). We consider RCPSP/max problems where the durations of activities are stochastic and resources can have unforeseen breakdowns. Given a level of allowable risk, (Formula presented.), our mechanisms aim to compute the minimum robust makespan execution strategy. Robust makespan for an execution strategy is any makespan value that has a risk less than (Formula presented.). The risk for a makespan value, (Formula presented.) given an execution strategy, is the probability that a …
On The Unreliability Of Bug Severity Data, Yuan Tian, Nasir Ali, David Lo, Ahmed E. Hassan
On The Unreliability Of Bug Severity Data, Yuan Tian, Nasir Ali, David Lo, Ahmed E. Hassan
Research Collection School Of Computing and Information Systems
Severity levels, e.g., critical and minor, of bugs are often used to prioritize development efforts. Prior research efforts have proposed approaches to automatically assign the severity label to a bug report. All prior efforts verify the accuracy of their approaches using human-assigned bug reports data that is stored in software repositories. However, all prior efforts assume that such human-assigned data is reliable. Hence a perfect automated approach should be able to assign the same severity label as in the repository – achieving a 100% accuracy. Looking at duplicate bug reports (i.e., reports referring to the same problem) from three open-source …
A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
Research Collection School Of Computing and Information Systems
We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in …
Flow Experience In Virtual Worlds: Individuals Versus Dyads, Fiona Fui-Hoon Nah, Brenda Eschenbrenner
Flow Experience In Virtual Worlds: Individuals Versus Dyads, Fiona Fui-Hoon Nah, Brenda Eschenbrenner
Research Collection School Of Computing and Information Systems
Flow is a state of mind in which one is deeply absorbed and immersed in an activity to the point where nothing else matters. Although flow can occur in solitary and social contexts, which context fosters greater flow is unclear. Consistent with self-determination theory, dyads working collaboratively achieved higher states of flow than individuals working alone. In other words, dyads achieved higher states of focused concentration and experienced greater temporal dissociation than individuals working alone. Surprisingly and contradictory to previous findings, dyads did not experience greater enjoyment than individuals working alone. We attributed this surprising finding to the hedonic nature …
Understanding Psychophysiological Correlates Of User Experience Using Electroencephalogram (Eeg), Fiona Fui-Hoon Nah, Keng Siau, Randall Minas, Nathan W. Twyman, Nicholas A. Rollins, Wenbo Wang, Xi Wang
Understanding Psychophysiological Correlates Of User Experience Using Electroencephalogram (Eeg), Fiona Fui-Hoon Nah, Keng Siau, Randall Minas, Nathan W. Twyman, Nicholas A. Rollins, Wenbo Wang, Xi Wang
Research Collection School Of Computing and Information Systems
The objective of this research is to understand psychophysiological correlates of user experience using electroencephalogram (EEG). We will conduct an experimental study to assess the psychophysiological differences across three states of user experience – flow, boredom, and anxiety – and compare them with the neutral state as a baseline. EEG measurement provides a plethora of data that can be decomposed using different analytical procedures. We will utilize one type of analysis, timefrequency decomposition, to examine changes in frequency (e.g., alpha/beta/theta band) over time. These frequency bands correlate with different cognitive states. In this study, we will use Event Related Spectral …
A Benchmark And Comparative Study Of Video-Based Face Recognition On Cox Face Database, Zhiwu Huang, S. Shan, R. Wang, H. Zhang, S. Lao, A. Kuerban, X. Chen
A Benchmark And Comparative Study Of Video-Based Face Recognition On Cox Face Database, Zhiwu Huang, S. Shan, R. Wang, H. Zhang, S. Lao, A. Kuerban, X. Chen
Research Collection School Of Computing and Information Systems
Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected …
Learning Query And Image Similarities With Ranking Canonical Correlation Analysis, Ting Yao, Tao Mei, Chong-Wah Ngo
Learning Query And Image Similarities With Ranking Canonical Correlation Analysis, Ting Yao, Tao Mei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
One of the fundamental problems in image search is to learn the ranking functions, i.e., similarity between the query and image. The research on this topic has evolved through two paradigms: feature-based vector model and image ranker learning. The former relies on the image surrounding texts, while the latter learns a ranker based on human labeled query-image pairs. Each of the paradigms has its own limitation. The vector model is sensitive to the quality of text descriptions, and the learning paradigm is difficult to be scaled up as human labeling is always too expensive to obtain. We demonstrate in this …
Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan
Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Ageing in place demands a new paradigm of inhouse caregiving allowing many aspects of daily lives to be tackled by smart appliances and technologies. The important challenges include the effective provision of recommendations by multiple parties of caregiver constituting changes of the user's behavior. In this multiagent environment, interdependencies between agents become major issues to tackle. This paper presents an approach of dynamic group formation for autonomous caregiving agents to collaborate in recommending different aspects of well-being. The approach supports the agents to regulate the timing of their recommendations, prevent conflicting messages, and cooperate to make more effective persuasions. A …
Bl-Mle: Block-Level Message-Locked Encryption For Secure Large File Deduplication, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo
Bl-Mle: Block-Level Message-Locked Encryption For Secure Large File Deduplication, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo
Research Collection School Of Computing and Information Systems
Deduplication is a popular technique widely used to save storage spaces in the cloud. To achieve secure deduplication of encrypted files, Bellare et al. formalized a new cryptographic primitive named message-locked encryption (MLE) in Eurocrypt 2013. Although an MLE scheme can be extended to obtain secure deduplication for large files, it requires a lot of metadata maintained by the end user and the cloud server. In this paper, we propose a new approach to achieve more efficient deduplication for (encrypted) large files. Our approach, named block-level message-locked encryption (BL-MLE), can achieve file-level and block-level deduplication, block key management, and proof …
A Bayesian Recommender Model For User Rating And Review Profiling, Mingming Jiang, Dandan Song, Lejian Liao, Feida Zhu
A Bayesian Recommender Model For User Rating And Review Profiling, Mingming Jiang, Dandan Song, Lejian Liao, Feida Zhu
Research Collection School Of Computing and Information Systems
Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews accompanied by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles' information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering (CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with "user attitudes" (i.e., abstract rating patterns) over the same distribution, our method achieves …
Progressive Sequence Matching For Adl Plan Recommendation, Shan Gao, Di Wang, Ah-Hwee Tan, Chunyan Miao
Progressive Sequence Matching For Adl Plan Recommendation, Shan Gao, Di Wang, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
Activities of Daily Living (ADLs) are indicatives of a person’s lifestyle. In particular, daily ADL routines closely relate to a person’s well-being. With the objective of promoting active lifestyles, this paper presents an agent system that provides recommendations of suitable ADL plans (i.e., selected ADL sequences) to individual users based on the more active lifestyles of the others. Specifically, we develop a set of quantitative measures, named wellness scores, spanning the evaluation across the physical, cognitive, emotion, and social aspects based on his or her ADL routines. Then we propose an ADL sequence learning model, named Recommendation ADL ART, or …
Modeling Social Media Content With Word Vectors For Recommendation, Ying Ding, Jing Jiang
Modeling Social Media Content With Word Vectors For Recommendation, Ying Ding, Jing Jiang
Research Collection School Of Computing and Information Systems
In social media, recommender systems are becoming more and more important. Different techniques have been designed for recommendations under various scenarios, but many of them do not use user-generated content, which potentially reflects users’ opinions and interests. Although a few studies have tried to combine user-generated content with rating or adoption data, they mostly reply on lexical similarity to calculate textual similarity. However, in social media, a diverse range of words is used. This renders the traditional ways of calculating textual similarity ineffective. In this work, we apply vector representation of words to measure the semantic similarity between text. We …
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Research Collection School Of Computing and Information Systems
Crowdsourcing has shown great potential in obtaining large-scale and cheap labels for different tasks. However, obtaining reliable labels is challenging due to several reasons, such as noisy annotators, limited budget and so on. The state-of-the-art approaches, either suffer in some noisy scenarios, or rely on unlimited resources to acquire reliable labels. In this article, we adopt the learning with expert~(AKA worker in crowdsourcing) advice framework to robustly infer accurate labels by considering the reliability of each worker. However, in order to accurately predict the reliability of each worker, traditional learning with expert advice will consult with external oracles~(AKA domain experts) …
Lossy Projective Hashing And Its Applications, Haiyang Xue, Yamin Liu, Xianhui Lu, Bao Li
Lossy Projective Hashing And Its Applications, Haiyang Xue, Yamin Liu, Xianhui Lu, Bao Li
Research Collection School Of Computing and Information Systems
In this paper, we introduce a primitive called lossy projective hashing. It is unknown before whether smooth projective hashing (Cramer-Shoup, Eurocrypt’02) can be constructed from dual projective hashing (Wee, Eurocrypt’12). The lossy projective hashing builds a bridge between dual projective hashing and smooth projective hashing. We give instantiations of lossy projective hashing from DDH, DCR, QR and general subgroup membership assumptions (including 2k-th residue, p-subgroup and higher residue assumptions). We also show how to construct lossy encryption and fully IND secure deterministic public key encryption from lossy projective hashing. – We give a construction of lossy projective hashing via dual …
An Adaptive Markov Strategy For Effective Network Intrusion Detection, Jianye Hao, Yinxing Xue, Mahinthan Chandramohan, Yang Liu, Jun Sun
An Adaptive Markov Strategy For Effective Network Intrusion Detection, Jianye Hao, Yinxing Xue, Mahinthan Chandramohan, Yang Liu, Jun Sun
Research Collection School Of Computing and Information Systems
Network monitoring is an important way to ensure the security of hosts from being attacked by malicious attackers. One challenging problem for network operators is how to distribute the limited monitoring resources (e.g., intrusion detectors) among the network to detect attacks effectively, especially when the attacking strategies can be changing dynamically and unpredictable. To this end, we adopt Markov game to model the interactions between the network operator and the attacker and propose an adaptive Markov strategy (AMS) to determine how the detectors should be placed on the network against possible attacks to minimize the network’s accumulated cost over time. …
Security Slicing For Auditing Xml, Xpath, And Sql Injection Vulnerabilities, Julian Thome, Lwin Khin Shar, Lionel Briand
Security Slicing For Auditing Xml, Xpath, And Sql Injection Vulnerabilities, Julian Thome, Lwin Khin Shar, Lionel Briand
Research Collection School Of Computing and Information Systems
XML, XPath, and SQL injection vulnerabilities are among the most common and serious security issues for Web applications and Web services. Thus, it is important for security auditors to ensure that the implemented code is, to the extent possible, free from these vulnerabilities before deployment. Although existing taint analysis approaches could automatically detect potential vulnerabilities in source code, they tend to generate many false warnings. Furthermore, the produced traces, i.e. dataflow paths from input sources to security-sensitive operations, tend to be incomplete or to contain a great deal of irrelevant information. Therefore, it is difficult to identify real vulnerabilities and …
Shopminer: Mining Customer Shopping Behavior In Physical Clothing Stores With Passive Rfids, Longfei Shangguan, Zimu Zhou, Xiaolong Zheng, Lei Yang, Yunhao Liu, Jinsong Han
Shopminer: Mining Customer Shopping Behavior In Physical Clothing Stores With Passive Rfids, Longfei Shangguan, Zimu Zhou, Xiaolong Zheng, Lei Yang, Yunhao Liu, Jinsong Han
Research Collection School Of Computing and Information Systems
Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on …
Challenges In Analyzing Software Documentation In Portuguese, Christoph Treude, Carlos A. Prolo, Fernando Figueira Filho
Challenges In Analyzing Software Documentation In Portuguese, Christoph Treude, Carlos A. Prolo, Fernando Figueira Filho
Research Collection School Of Computing and Information Systems
Many tools that automatically analyze, summarize, or transform software artifacts rely on natural language processing tooling for the interpretation of natural language text produced by software developers, such as documentation, code comments, commit messages, or bug reports. Processing natural language text produced by software developers is challenging because of unique characteristics not found in other texts, such as the presence of code terms and the systematic use of incomplete sentences. In addition, texts produced by Portuguese-speaking developers mix languages since many keywords and programming concepts are referred to by their English name. In this paper, we provide empirical insights into …
Lesinn: Detecting Anomalies By Identifying Least Similar Nearest Neighbours, Guansong Pang, Kai Ming Ting, David Albrecht
Lesinn: Detecting Anomalies By Identifying Least Similar Nearest Neighbours, Guansong Pang, Kai Ming Ting, David Albrecht
Research Collection School Of Computing and Information Systems
We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ensemble method which works well with models trained using samples of one instance. LeSiNN has linear time complexity with respect to data size and the number of dimensions, and it is one of the few anomaly detectors which can apply directly to both numeric and categorical data sets. Our extensive …
Whom Should We Sense In 'Social Sensing' - Analyzing Which Users Work Best For Social Media Now-Casting, Jisun An, Ingmar Weber
Whom Should We Sense In 'Social Sensing' - Analyzing Which Users Work Best For Social Media Now-Casting, Jisun An, Ingmar Weber
Research Collection School Of Computing and Information Systems
Given the ever increasing amount of publicly available social media data, there is growing interest in using online data to study and quantify phenomena in the offline 'real' world. As social media data can be obtained in near real-time and at low cost, it is often used for 'now-casting' indices such as levels of flu activity or unemployment. The term 'social sensing' is often used in this context to describe the idea that users act as 'sensors', publicly reporting their health status or job losses. Sensor activity during a time period is then typically aggregated in a 'one tweet, one …
Powerforecaster: Predicting Smartphone Power Impact Of Continuous Sensing Applications At Pre-Installation Time, Chulhong Min, Youngki Lee, Chungkuk Yoo, Seungwoo Kang, Sangwon Choi, Pillsoon Park, Inseok Hwang, Younghyun Ju, Seungpyo Choi, Junehwa Song
Powerforecaster: Predicting Smartphone Power Impact Of Continuous Sensing Applications At Pre-Installation Time, Chulhong Min, Youngki Lee, Chungkuk Yoo, Seungwoo Kang, Sangwon Choi, Pillsoon Park, Inseok Hwang, Younghyun Ju, Seungpyo Choi, Junehwa Song
Research Collection School Of Computing and Information Systems
Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe …
Dictionary Pair Learning On Grassmann Manifolds For Image Denoising, Xianhua Zeng, Wei Bian, Wei Liu, Jialie Shen, Dacheng Tao
Dictionary Pair Learning On Grassmann Manifolds For Image Denoising, Xianhua Zeng, Wei Bian, Wei Liu, Jialie Shen, Dacheng Tao
Research Collection School Of Computing and Information Systems
Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary …
Where Are The Passengers? A Grid-Based Gaussian Mixture Model For Taxi Bookings, Meng-Fen Chiang, Tuan Anh Hoang, Ee-Peng Lim
Where Are The Passengers? A Grid-Based Gaussian Mixture Model For Taxi Bookings, Meng-Fen Chiang, Tuan Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Taxi bookings are events where requests for taxis are made by passengers either over voice calls or mobile apps. As the demand for taxis changes with space and time, it is important to model both the space and temporal dimensions in dynamic booking data. Several applications can benefit from a good taxi booking model. These include the prediction of number of bookings at certain location and time of the day, and the detection of anomalous booking events. In this paper, we propose a Grid-based Gaussian Mixture Model (GGMM) with spatio-temporal dimensions that groups booking data into a number of spatio-temporal …
Intelligshop: Enabling Intelligent Shopping In Malls Through Location-Based Augmented Reality, Aditi Adhikari, Vincent W. Zheng, Hong Cao, Miao Lin, Yuan Fang, Kevin Chen-Chuan Chang
Intelligshop: Enabling Intelligent Shopping In Malls Through Location-Based Augmented Reality, Aditi Adhikari, Vincent W. Zheng, Hong Cao, Miao Lin, Yuan Fang, Kevin Chen-Chuan Chang
Research Collection School Of Computing and Information Systems
Shopping experience is important for both citizens and tourists. We present IntelligShop, a novel location-based augmented reality application that supports intelligent shopping experience in malls. As the key functionality, IntelligShop provides an augmented reality interface-people can simply use ubiquitous smartphones to face mall retailers, then IntelligShop will automatically recognize the retailers and fetch their online reviews from various sources (including blogs, forums and publicly accessible social media) to display on the phones. Technically, IntelligShop addresses two challenging data mining problems, including robust feature learning to support heterogeneous smartphones in localization and learning to query for automatically gathering the retailer content …
Codehow: Effective Code Search Based On Api Understanding And Extended Boolean Model (E), Fei Lv, Jian-Guang Lou, Shaowei Wang, Dongmei Zhang, Jainjun Zhao
Codehow: Effective Code Search Based On Api Understanding And Extended Boolean Model (E), Fei Lv, Jian-Guang Lou, Shaowei Wang, Dongmei Zhang, Jainjun Zhao
Research Collection School Of Computing and Information Systems
Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previously-written code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying …
A Passive Testing Approach For Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee Xian Tan, Hwee-Pink Tan, Zhangbing Zhou
A Passive Testing Approach For Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee Xian Tan, Hwee-Pink Tan, Zhangbing Zhou
Research Collection School Of Computing and Information Systems
Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a cruicial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works …
Stack Layout Randomization With Minimal Rewriting Of Android Binaries, Yu Liang, Xinjie Ma, Daoyuan Wu, Xiaoxiao Tang, Debin Gao, Guojun Peng, Chunfu Jia, Huanguo Zhang
Stack Layout Randomization With Minimal Rewriting Of Android Binaries, Yu Liang, Xinjie Ma, Daoyuan Wu, Xiaoxiao Tang, Debin Gao, Guojun Peng, Chunfu Jia, Huanguo Zhang
Research Collection School Of Computing and Information Systems
Stack-based attacks typically require that attackers have a good understanding of the stack layout of the victim program. In this paper, we leverage specific features on ARM architecture and propose a practical technique that introduces randomness to the stack layout when an Android application executes. We employ minimal binary rewriting on the Android app that produces randomized executable of the same size which can be executed on an unmodified Android operating system. Our experiments on applying this randomization on the most popular 20 free Android apps on Google Play show that the randomization coverage of functions increases from 65% (by …
Cost-Sensitive Online Classification With Adaptive Regularization And Its Applications, Peilin Zhao, Furen Zhuang, Min Wu, Xiao-Li Li, Hoi, Steven C. H.
Cost-Sensitive Online Classification With Adaptive Regularization And Its Applications, Peilin Zhao, Furen Zhuang, Min Wu, Xiao-Li Li, Hoi, Steven C. H.
Research Collection School Of Computing and Information Systems
Cost-Sensitive Online Classification is recently proposed to directly online optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. However, the previous existing learning algorithms only utilized the first order information of the data stream. This is insufficient, as recent studies have proved that incorporating second order information could yield significant improvements on the prediction model. Hence, we propose a novel cost-sensitive online classification algorithm with adaptive regularization. We theoretically analyzed the proposed algorithm and empirically validated its effectiveness with extensive experiments. We also demonstrate the application of the …
Cnl: Collective Network Linkage Across Heterogeneous Social Platforms, Ming Gao, Ee-Peng Lim, David Lo, Feida Zhu, Philips Kokoh Prasetyo, Aoying Zhou
Cnl: Collective Network Linkage Across Heterogeneous Social Platforms, Ming Gao, Ee-Peng Lim, David Lo, Feida Zhu, Philips Kokoh Prasetyo, Aoying Zhou
Research Collection School Of Computing and Information Systems
The popularity of social media has led many users to create accounts with different online social networks. Identifying these multiple accounts belonging to same user is of critical importance to user profiling, community detection, user behavior understanding and product recommendation. Nevertheless, linking users across heterogeneous social networks is challenging due to large network sizes, heterogeneous user attributes and behaviors in different networks, and noises in user generated data. In this paper, we propose an unsupervised method, Collective Network Linkage (CNL), to link users across heterogeneous social networks. CNL incorporates heterogeneous attributes and social features unique to social network users, handles …
Experience Report: An Industrial Experience Report On Test Outsourcing Practices, Xin Xia, David Lo, Pavneet Singh Kochhar, Zhenchang Xing, Xinyu Wang, Shanping Li
Experience Report: An Industrial Experience Report On Test Outsourcing Practices, Xin Xia, David Lo, Pavneet Singh Kochhar, Zhenchang Xing, Xinyu Wang, Shanping Li
Research Collection School Of Computing and Information Systems
Nowadays, many companies contract their testing functionalities out to third-party IT outsourcing companies. This process referred to as test outsourcing is common in the industry, yet it is rarely studied in the research community. In this paper, to bridge the gap, we performed an empirical study on test outsourcing with 10 interviewees and 140 survey respondents. We investigated various research questions such as the types, the process, and the challenges of test outsourcing, and the differences between test outsourcing and in-house testing. We found customer satisfaction, tight project schedule, and domain unfamiliarity are the top-3 challenges faced by the testers. …