Databases and Information Systems Commons^™

Semantic And Influence Aware K-Representative Queries Over Social Streams, Yanhao Wang, Yuchen Li, Kianlee Tan

Research Collection School Of Computing and Information Systems

Massive volumes of data continuously generated on social platforms have become an important information source for users. A primary method to obtain fresh and valuable information from social streams is social search. Although there have been extensive studies on social search, existing methods only focus on the relevance of query results but ignore the representativeness. In this paper, we propose a novel Semantic and Influence aware k-Representative (k-SIR) query for social streams based on topic modeling. Specifically, we consider that both user queries and elements are represented as vectors in the topic space. A k-SIR query retrieves a set of …

Welcome Message From The General Chairs, Nabil I. Alshurafa, Archan Misra, Abhishek Mukherji

Research Collection School Of Computing and Information Systems

No abstract provided.

Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr

Research Collection School Of Computing and Information Systems

Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed …

Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr

Research Collection School Of Computing and Information Systems

Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed …

Vistanet: Visual Aspect Attention Network For Multimodal Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Detecting the sentiment expressed by a document is a key task for many applications, e.g., modeling user preferences, monitoring consumer behaviors, assessing product quality. Traditionally, the sentiment analysis task primarily relies on textual content. Fueled by the rise of mobile phones that are often the only cameras on hand, documents on the Web (e.g., reviews, blog posts, tweets) are increasingly multimodal in nature, with photos in addition to textual content. A question arises whether the visual component could be useful for sentiment analysis as well. In this work, we propose Visual Aspect Attention Network or VistaNet, leveraging both textual and …

Gamification Of Enterprise Systems, Fiona Fui-Hoon Nah, B. Eschenbrenner, C. Claybaugh, P. Koob

Research Collection School Of Computing and Information Systems

Enterprise systems have become an integral part of an organization’s operations. However, they also pose many challenges to organizations from the perspective of implementation, user training, as well as use and acceptance. Without effective usage, enterprise systems may not be able to provide the strategic or competitive advantages that organizations desire. Therefore, organizations mayconsider gamification to enhance training, acceptance, and usage. We discuss the various ways in whichenterprise system challenges can be addressed through the lens of gamification and present a frameworkforgamificationofenterprisesystems. Theframeworkiscomprisedofbasicprinciplesand key design elements of gamification, as well as their application to enterprise systems. The specific principles of …

Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang

Research Collection School Of Computing and Information Systems

We investigate whether a firm's risk pooling affects its analysts' forecasts, specifically in terms of forecast accuracy and their use of public vs. private information, and how risk pooling interacts with a firm's position in the supply chain to affect analysts' forecasts. We use a social network analysis method to operationalize risk pooling and supply chain hierarchy, and find that risk pooling significantly reduces analysts' forecast errors and increases (decreases) their use of public (private) information. We also find that the positive (negative) relationships between risk pooling and analyst forecast accuracy and analysts' use of public (private) information are more …

Robust Estimation Of Similarity Transformation For Visual Object Tracking, Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu

Research Collection School Of Computing and Information Systems

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale …

Send Hardest Problems My Way: Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Yue Duan, Jifeng Xuan

Research Collection School Of Computing and Information Systems

Hybrid fuzzing which combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, the state-of-the-art hybrid fuzzing systems deploy ``demand launch'' and ``optimal switch'' strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to oversimplified assumptions. We then propose a novel ``discriminative dispatch'' strategy to better utilize the capability of concolic execution. We design a novel Monte Carlo based probabilistic path prioritization model to quantify each path's difficulty and prioritize them for concolic execution. This model treats …

Adaptive Cost-Sensitive Online Classification, Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang

Research Collection School Of Computing and Information Systems

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, …

Discrete Social Recommendation, Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, …

Social Media Mining For Journalism, Arkaitz Zubiaga, Bahareh Heravi, Jisun An, Haewoon Kwak

Research Collection School Of Computing and Information Systems

The exponential growth of social media as a central communication practice, and its agility in capturing and announcing breaking news events more rapidly than traditional media, has changed the journalistic landscape: social media has been adopted as a significant source by professional journalists, and conversely, citizens are able to use social media as a form of direct reportage. This brings along new opportunities for newsrooms and journalists by providing new means for newsgathering through access to a wealth of citizen reportage and updates about current affairs, as well as an additional showcase for news dissemination.

Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation …

Explainable Reasoning Over Knowledge Graphs For Recommendation, Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledgeaware Path Recurrent …

Multi-Task Learning With Multi-View Attention For Answer Selection And Knowledge Base Question Answering, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen

Research Collection School Of Computing and Information Systems

Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), …

Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, Chen Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

This paper explores machine learning to address a problem of Partially Observable Multi-sensor Sequential Change Detection (POMSCD), where only a subset of sensors can be observed to monitor a target system for change-point detection at each online learning round. In contrast to traditional Multisensor Sequential Change Detection tasks where all the sensors are observable, POMSCD is much more challenging because the learner not only needs to detect on-the-fly whether a change occurs based on partially observed multi-sensor data streams, but also needs to cleverly choose a subset of informative sensors to be observed in the next learning round, in order …

A Coordination Framework For Multi-Agent Persuasion And Adviser Systems, Budhitama Subagdja, Ah-Hwee Tan, Yilin Kang

Research Collection School Of Computing and Information Systems

Assistive agents have been used to give advices to the users regarding activities in daily lives. Although adviser bots are getting smarter and gaining more popularity these days they are usually developed and deployed independent from each other. When several agents operate together in the same context, their advices may no longer be effective since they may instead overwhelm or confuse the user if not properly arranged. Only little attentions have been paid to coordinating different agents to give different advices to a user within the same environment. However, aligning the advices on-the-fly with the appropriate presentation timing at the …

Two-Sided Value-Based Music Artist Recommendation In Streaming Music Services, J. Ren, Robert John Kauffman, D. King

Research Collection School Of Computing and Information Systems

Most work on music recommendations has focused on the consumer side not the provider side. We develop a two-sided value-based approach to music artist recommendation for a streaming music scenario. It combines the value yielded for the music industry and consumers in an integrated model. For the industry, the approach aims to increase the conversion rate of potential listeners to adopters, which produces new revenue. For consumers, it aims to improve their utility related to recommendations they receive. We use one year of listening records for 15,000+ Last.fm users to train and test the proposed recommendation model on 143 artists. …

Online Burst Events Detection Oriented Real-Time Microblog Message Stream, Guozhong Dong, Jun Gao, Liang Huang, Chunlei Shi

Research Collection School Of Computing and Information Systems

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table. …

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia

Research Collection School Of Computing and Information Systems

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …

Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints …

Renegotiation Of Software Outsourcing Contracts, H. Huang, X. Hu, Robert John Kauffman, H. Xu

Research Collection School Of Computing and Information Systems

Fixed-price and time-and-materials contracts are commonly-used contract forms by clients in software outsourcing. The two parties, client and provider, usually renegotiate the testing time after system development occurs. This research investigates the impacts of such renegotiation on the client’s contract choice. Our analysis shows that under both contract forms, renegotiation can incentivize the provider’s effort, and this effect becomes more influential when the provider has higher bargaining power. Compared with a fixedprice contract, a time-and-materials contract can stimulate the provider’s effort based on the terms for monitoring and reimbursement. The results suggest that when the provider has high bargaining power, …

Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior helps us gain useful insights about venues as well as the users visiting them. These insights are important in urban planning and recommender system applications. Since check-in behavior is the result of multiple factors, this paper focuses on studying two venue related factors, namely, area attraction and neighborhood competition. The former refers to the ability of a spatial area covering multiple venues to collectively attract check-ins from users, while the latter represents the extent to which a venue can compete with other venues in the same area for check-ins. We first embark on empirical studies to …

An Economic Analysis Of Consumer Learning On Entertainment Shopping Websites, Jin Li, Zhiling Guo, Geoffrey K.F. Tso

Research Collection School Of Computing and Information Systems

Online entertainment shopping, normally supported by the pay-to-bid auction mechanism, represents an innovative business model in e-commerce. Because the unique selling mechanism combines features of shopping and online auction, consumers expect both monetary return and entertainment value from their participation. We propose a dynamic structural model to analyze consumer behaviors on entertainment shopping websites. The model captures the consumer learning process, based both on individual participation experiences and also on observational learning of historical auction information. We estimate the model using a large data set from an online entertainment shopping website. Results show that consumers’ initial participation incentives mainly come …

Artificial Intelligence, Machine Learning, And Autonomous Technologies In Mining Industry, Zeshan Hyder, Keng Siau, Fiona Nah

Research Collection School Of Computing and Information Systems

The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application. The article presents …

Large Scale Online Multiple Kernel Regression With Application To Time-Series Prediction, Doyen Sahoo, Steven C. H. Hoi, Bin Lin

Research Collection School Of Computing and Information Systems

Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks as follows: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume that a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this work …

The Rise Of Real-Time Retail Payments, Zhiling Guo

MITB Thought Leadership Series

TRANSACTING for just about anything using our mobile phones has become commonplace, and so many consumers will be intrigued to discover that after making a purchase it can still take longer for payment to reach a vendor’s bank account than it does for the purchased goods to be delivered.

Leveraging Artificial Intelligence To Capture The Singapore Rideshare Market, Pradeep Varakantham

MITB Thought Leadership Series

BIKE-SHARING programmes face many of the issues encountered by their counterparts in the carsharing world. But in Singapore, there are a number of factors that have a unique impact on the industry. These include the regulatory structure and the significant fines for those companies who do not abide by these regulations. When this is combined with the competitive nature of the industry in one of the world's most dynamic cities, it becomes clear that first movers who leverage machine learning and prediction will come to dominate the industry

Comparison Mining From Text, Maksim Tkachenko

Dissertations and Theses Collection (Open Access)

Online product reviews are important factors of consumers' purchase decisions. They invade more and more spheres of our life, we have reviews on books, electronics, groceries, entertainments, restaurants, travel experiences, etc. More than 90 percent of consumers read online reviews before they purchase products as reported by various consumers surveys. This observation suggests that product review information enhances consumer experience and helps them to make better-informed purchase decisions. There is an enormous amount of online reviews posted on e-commerce platforms, such as Amazon, Apple, Yelp, TripAdvisor. They vary in information and may be written with different experiences and preferences.

If …

Data Mining Approach To The Identification Of At-Risk Students, Li Chin Ho, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS ® Enterprise Miner (EM) and SAS ® JMP Pro.