Databases and Information Systems Commons^™

Collaborative Online Multitask Learning, Guangxia Li, Steven C. H. Hoi, Kuiyu Chang, Wenting Liu, Ramesh Jain

Research Collection School Of Computing and Information Systems

We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on a group of users, which classifies micro-blog posts generated by each user into emotional or non-emotional categories. This particular online learning task is challenging for a number of reasons. First of all, to meet the critical requirements of online applications, a highly efficient and scalable classification solution that can make immediate predictions with low learning cost is …

Integrating Motivated Learning And K-Winner-Take-All To Coordinate Multi-Agent Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Janusz Starzyk, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

This work addresses the coordination issue in distributed optimization problem (DOP) where multiple distinct and time-critical tasks are performed to satisfy a global objective function. The performance of these tasks has to be coordinated due to the sharing of consumable resources and the dependency on non-consumable resources. Knowing that it can be sub-optimal to predefine the performance of the tasks for large DOPs, the multi-agent reinforcement learning (MARL) framework is adopted wherein an agent is used to learn the performance of each distinct task using reinforcement learning. To coordinate MARL, we propose a novel coordination strategy integrating Motivated Learning (ML) …

Diversified Social Influence Maximization, Fangshuang Tang, Qi Liu, Hengshu Zhu, Enhong Chen, Feida Zhu

Research Collection School Of Computing and Information Systems

For better viral marketing, there has been a lot of research on social influence maximization. However, the problem that who is influenced and how diverse the influenced population is, which is important in real-world marketing, has largely been neglected. To that end, in this paper, we propose to consider the magnitude of influence and the diversity of the influenced crowd simultaneously. Specifically, we formulate it as an optimization problem, i.e., diversified social influence maximization. First, we present a general framework for this problem, under which we construct a class of diversity measures to quantify the diversity of the influenced crowd. …

Guest Editorial: Special Issue On Brain Inspired Models Of Cognitive Memory, Huajin Tang, Kiruthika Ramanathan, Ning Nign

Research Collection School Of Computing and Information Systems

Current memory technologies have experienced significant progress in terms of storage capacity, operation speed, integration capability, etc. However, their functions are highly constrained in storing and transferring data in space and time, prompting the need for improvement. In contrast to physical memories, the biological counterpart – cognitive memory – has versatile functions. For instance, human memory stores data associatively such that different modalities of data could be retrieved simultaneously; it can learn different concepts, categorize and store them in an organized manner; it can process and store data concurrently and in a distributed fashion; it can restore content even if …

Urban Planning Process: Can Technology Enhance Participatory Communication?, Rojin Vishkaie, Richard Levy, Anthony Tang

Research Collection School Of Computing and Information Systems

Oftentimes, within the urban planning process, urban planners and GIS experts must work together using desktop Computer-Aided Design (CAD) and Geographic Information System (GIS). However, participatory communication and visualization which are important in the urban planning process, are not a central focus in the design of current computer-aided planning technologies. This study tends to provide an understanding of technological challenges and complexities urban planners and GIS experts encounter while engaging in a participatory environment during the urban planning process. This study also explores the perceptions of urban planners and GIS experts about the potential impact and usefulness of interactive surfaces …

On Macro And Micro Exploration Of Hashtag Diffusion In Twitter, Yazhe Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

This exploratory work studies hashtag diffusion in Twitter. The analysis is conducted from two aspects. From the macro perspective, we study general properties of hashtag diffusion, and classify hashtags into three main classes based on their temporal dynamics referred as 'single spike', 'multi-spikes', and 'fluctuation', and find that each of these classes has some unique characteristics. From the micro perspective, we investigate individual diffusion.We adopt Edelman's 'topology of influence' theory to identify four type of users with different influence levels in diffusion based on their dynamic retweet behaviors. The results of our study are useful for gaining more insights of …

Direct Neighbor Search, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc.) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an …

Online Multiple Kernel Regression, Doyen Sahoo, Steven C. H. Hoi, Bin Li

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: (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 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 paper presents a novel …

Generating Supplementary Travel Guides From Social Media, Liu Yang, Jing Jiang, Lifu Huang, Minghui Qiu, Lizi Liao

Research Collection School Of Computing and Information Systems

In this paper we study how to summarize travel-related information in forum threads to generate supplementary travel guides. Such summaries presumably can provide additional and more up-to-date information to tourists. Existing multi-document summarization methods have limitations for this task because (1) they do not generate structured summaries but travel guides usually follow a certain template, and (2) they do not put emphasis on named entities but travel guides often recommend points of interest to travelers. To overcome these limitations, we propose to use a latent variable model to align forum threads with the section structure of well-written travel guides. The …

Ranking Model Selection And Fusion For Effective Microblog Search, Zhongyu Wei, Wei Gao, Tarek El-Ganainy, Walid Magdy, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced …

Structure Preserving Large Imagery Reconstruction, Ju Shen, Jianjun Yang, Sami Taha Abu Sneineh, Bryson Payne, Markus Hitz

Computer Science Faculty Publications

With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Furthermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure-based image …

Systems For Delivering Electric Vehicle Data Analytics, Vamshi Krishna Bolly

Open Access Theses

n the recent times, advances in scientific research related to electric vehicles led to generation of large amounts of data. This data is majorly logger data collected from various sensors in the vehicle. It is predominantly unstructured and non-relational in nature, also called Big Data. Analysis of such data needs a high performance information technology infrastructure that provides superior computational efficiency and storage capacity. It should be scalable to accommodate the growing data and ensure its security over a network. This research proposes an architecture built over Hadoop to effectively support distributed data management over a network for real-time data …

A Model-Based Approach To System-Of-Systems Engineering Via The Systems Modeling Language, Kevin Hughes Bonanne

Open Access Theses

In the field of Systems Engineering, a movement is underway to capture the aspects of a system in a centralized model format instead of various documents. This is the basis of Model Based Systems Engineering (MBSE). In order to better formalize this change, the Systems Modeling Language (SysML) was developed to characterize an ontology for MBSE. Despite the growth of both MBSE practices and SysML tools, they have yet to be rigorously analyzed as to their applicability to the field of System-of-Systems (SoS). This thesis applies SysML to a methodology for System-of-Systems Engineering (SoSE) known as the Wave Model, which …

The Use Of Business Intelligence Techniques In Supply Chain Performance, Jue Gu

Open Access Theses

Who likes data? Businesses are always loyal data followers. Companies analyze various forms of data to maintain businesses and identify their current performance in different areas so they can find business opportunities to improve and obtain more market share in advance (Qrunfleh & Tarafdar, 2012). When Big Data comes to businesses, companies who can take advantage of data the best tend to regularly get more business and customers (Waller & Fawcett, 2013). Collecting, analyzing, and demonstrating data could be essential to a single business, a company's supply chain performance and its sustainability. As an intelligent data processing product in terms …

A Quantitative Framework Of Skill Evaluation Of It Workforce, O Hyun Hwang

Open Access Theses

Every employee has different abilities as well as experience, leading to different results in terms of skills and job performance. In order to improve employee IT skills, organizations should evaluate their employees' skills to understand the current levels of skills and knowledge and to figure out areas where skills are currently lacking.

This study used a different approach than other existing methods to evaluate the IT skills of employees. The research used quantitative empirical data that represents the work experience of employees based on their task history and job positions. The suggested method defines the relationship between IT skills and …

Cenknn: A Scalable And Effective Text Classifier, Guansong Pang, Huidong Jin, Shengyi Jiang

Research Collection School Of Computing and Information Systems

A big challenge in text classification is to perform classification on a large-scale and high-dimensional text corpus in the presence of imbalanced class distributions and a large number of irrelevant or noisy term features. A number of techniques have been proposed to handle this challenge with varying degrees of success. In this paper, by combining the strengths of two widely used text classification techniques, K-Nearest-Neighbor (KNN) and centroid based (Centroid) classifiers, we propose a scalable and effective flat classifier, called CenKNN, to cope with this challenge. CenKNN projects high-dimensional (often hundreds of thousands) documents into a low-dimensional (normally a few …

Integrating Self-Organizing Neural Network And Motivated Learning For Coordinated Multi-Agent Reinforcement Learning In Multi-Stage Stochastic Game, Teck-Hou Teng, Ah-Hwee Tan, Janusz A. Starzyk, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

Most non-trivial problems require the coordinated performance of multiple goal-oriented and time-critical tasks. Coordinating the performance of the tasks is required due to the dependencies among the tasks and the sharing of resources. In this work, an agent learns to perform a task using reinforcement learning with a self-organizing neural network as the function approximator. We propose a novel coordination strategy integrating Motivated Learning (ML) and a self-organizing neural network for multi-agent reinforcement learning (MARL). Specifically, we adapt the ML idea of using pain signal to overcome the resource competition issue. Dependency among the agents is resolved using domain knowledge …

Board Interlock Networks And The Use Of Relative Performance Evaluation, Qian Hao, Nan Hu, Ling Liu, Lee J. Yao

Research Collection School Of Computing and Information Systems

Purpose - The purpose of this paper is to explore how networks of boards of directors affect relative performance evaluation (RPE) in chief executive officer (CEO) compensation. Design/methodology/approach - In this study, the authors propose that an interlocking network is an important inter-corporate setting, which has a bearing on whether boards decide to use RPE in CEO compensation. They adopt four typical graph measures to depict the centrality/position of each board in the interlock network: degree, betweenness, eigenvector and closeness, and study their impacts on RPE use. Findings - The authors find that firms that have more connected board members …

Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu

Research Collection School Of Computing and Information Systems

Influential user can play a crucial role in online social networks. This paper documents an empirical study aiming at exploring the effects of influential users in the context of music social network. To achieve this goal, music diffusion graph is developed to model how information propagates over network. We also propose a heuristic method to measure users' influences. Using the real data from Last. fm, our empirical test demonstrates key effects of influential users and reveals limitations of existing influence identification/characterization schemes.

Predicting The Popularity Of Web 2.0 Items Based On User Comments, Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama

Research Collection School Of Computing and Information Systems

In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible …