Physical Sciences and Mathematics Commons^™

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 …

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 …

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) …

Utilizing Microblogs For Improving Automatic News High-Lights Extraction, Zhongyu Wei, Wei Gao

Research Collection School Of Computing and Information Systems

Story highlights form a succinct single-document summary consisting of 3-4 highlight sentences that reflect the gist of a news article. Automatically producing news highlights is very challenging. We propose a novel method to improve news highlights extraction by using microblogs. The hypothesis is that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently “short and sweet” resulting from the artificial compression effect due to the length limit. Given a news article, we formulate the problem as two rank-then-extract tasks: (1) we find a set of indicative tweets and use …

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. …

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 …

Semantic Visualization For Spherical Representation, Tuan M. V. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Visualization of high-dimensional data such as text documents is widely applicable. The traditional means is to find an appropriate embedding of the high-dimensional representation in a low-dimensional visualizable space. As topic modeling is a useful form of dimensionality reduction that preserves the semantics in documents, recent approaches aim for a visualization that is consistent with both the original word space, as well as the semantic topic space. In this paper, we address the semantic visualization problem. Given a corpus of documents, the objective is to simultaneously learn the topic distributions as well as the visualization coordinates of documents. We propose …

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 …

Jointly Modeling Aspects, Ratings And Sentiments For Movie Recommendation (Jmars), Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, Chong Wang

Research Collection School Of Computing and Information Systems

Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings. The ability to answer questions such as “Does this user care more about the plot or about the special effects?” or ”What is the quality of the movie in terms of acting?” …

Opinion Mining Of Sociopolitical Comments From Social Media, Swapna Gottipati

Dissertations and Theses Collection (Open Access)

Opinions are central to almost all human activities by influencing greatly the decision making process. In this thesis, we present the problems of mining issues, extracting entities and suggestive opinions towards the entities, detecting thoughtful comments, and extracting stances and ideological expressions from online comments in the sociopolitical domain. This study is essential for opinion mining applications that are beneficial for policy makers, government sectors and social organizations. Much work has been done to try to uncover consumer sentiments from online comments to help businesses improve their products and services. However, sociopolitical opinion mining poses new challenges due to complex …

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 …

Lexicon-Based Sentiment Analysis In The Social Web, Fazal Masud Kundi, Dr. Muhammad Zubair Asghar

Dr. Muhammad Zubair Asghar

Sentiment analysis is a compelling issue for both information producers and consumers. We are living in the “age of customer”, where customer knowledge and perception is a key for running successful business. The goal of sentiment analysis is to recognize and express emotions digitally. This paper presents the lexicon-based framework for sentiment classification, which classifies tweets as a positive, negative, or neutral. The proposed framework also detects and scores the slangs used in the tweets. The comparative results show that the proposed system outperforms the existing systems. It achieves 92% accuracy in binary classification and 87% in multi-class classification.

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 …

Lexicon Based Approach For Sentiment Classification Of User Reviews, Dr. Muhammad Zubair Asghar

Dr. Muhammad Zubair Asghar

With the advent of web, online user reviews are getting more and more attention of the researchers because valuable information about products and services are available on social media like twitter1. These reviews are very helpful for organizations as well as for new customers showing interest in these products or services. But this data is generated in tremendous amount which is out of control of manual mining methods. These reviews need a model that has the ability to gauge these shared reviews according to predefined categories. This work introduces a rule based approach to find the opinion classification of reviews. …

Ultimate Codes: Near-Optimal Mds Array Codes For Raid-6, Zhijie Huang, Hong Jiang, Chong Wang, Ke Zhou, Yuhong Zhao

CSE Technical Reports

As modern storage systems have grown in size and complexity, RAID-6 is poised to replace RAID-5 as the dominant form of RAID architectures due to its ability to protect against double disk failures. Many excellent erasure codes specially designed for RAID-6 have emerged in recent years. However, all of them have limitations. In this paper, we present a class of near perfect erasure codes for RAID-6, called the Ultimate codes. These codes encode, update and decode either optimally or nearly optimally, regardless of what the code length is. This implies that utilizing these codes we can build highly efficient and …

Metadata-Driven Threat Classification Of Network Endpoints Appearing In Malware, Andrew G. West, Aziz Mohaisen

Andrew G. West

Networked machines serving as binary distribution points, C&C channels, or drop sites are a ubiquitous aspect of malware infrastructure. By sandboxing malcode one can extract the network endpoints (i.e., domains and URL paths) contacted during execution. Some endpoints are benign, e.g., connectivity tests. Exclusively malicious destinations, however, can serve as signatures enabling network alarms. Often these behavioral distinctions are drawn by expert analysts, resulting in considerable cost and labeling latency.

Leveraging 28,000 expert-labeled endpoints derived from ~100k malware binaries this paper characterizes those domains/URLs towards prioritizing manual efforts and automatic signature generation. Our analysis focuses on endpoints' static metadata properties …

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 …

Querie: Collaborative Database Exploration, Magdalini Eirinaki, Suju Abraham, Neoklis Polyzotis, Naushin Shaikh

Magdalini Eirinaki

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where …

Unraveling The Digital Literacy Paradox: How Higher Education Fails At The Fourth Literacy., Meg Coffin Murray, Jorge Perez

Faculty Articles

Governments around the globe are recognizing the economic ramifications of a digitally literate citizenry and implementing systemic strategies to advance digital literacy. Awareness of the growing importance of digital literacy in today’s workplace coexists paradoxically with apparent foot-dragging on the part of many universities in assessment and amplification of these important competencies. This paper makes a case for digital literacy, presents models of the complex construct, and presents the results of a digital literacy assessment administered to students enrolled in a senior seminar course at a regional university in the United States. Reflection on the study results evoked our mantra …

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 …

Administrative Support - Task Management System, Ramesh Avula

All Capstone Projects

Administrative Support - Task Management System (ASTMS) is a web application by which any university can manage tasks among its employees. This project has various small parts like commenting on task, upload and download files, task forwarding, editing existing and creating new project, task, employee, user etc . ASTMS is an automation system, which is used to store the Work tasks information of a university.

The task management system eliminates manual request and assignment of work. Faculty can request any work tasks online. Administrative staff can review the incoming work tasks and assign them to appropriate personnel Development process of …

Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li

Research Collection School Of Computing and Information Systems

Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could be fitted well by S-curve function (Sigmoid function). …

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.

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 …

Manifold Learning For Jointly Modeling Topic And Visualization, Tuan Minh Van Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to …

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 …

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 …