Physical Sciences and Mathematics Commons^™

Citizen Sensing: Opportunities And Challenges In Mining Social Signals And Perceptions, Amit P. Sheth

Kno.e.sis Publications

Millions of persons have become 'citizens' of an Internet- or Web-enabled social community. Web 2.0 fostered the open environment and applications for tagging, blogging, wikis, and social networking sites that have made information consumption, production, and sharing so incredibly easy. An interconnected network of people who actively observe, report, collect, analyze, and disseminate information via text, audio, or video messages, increasingly through pervasively connected mobile devices, has led to what we term citizen sensing. In this talk, we review recent progress in supporting collective intelligence through intelligent processing of citizen sensing. Key issues we cover in this talk are: - …

Evaluating And Implementing Web Scale Discovery Services: Part One, Jason Vaughan, Tamera Hanken

Library Faculty Presentations

Preface: Before Web Scale Discovery

• A very brief overview

Part 1: What is Web Scale Discovery

• Content
• Technology

Part 2: Why is Web Scale Discovery important?

• What’s the need?
• How is it different from earlier attempts at broad discovery?

Part 3: A Framework for Evaluating Web Scale Discovery Services

• What we did at UNLV
• Other options

Comparison Of Clustered Rdf Data Stores, Venkata Patchigolla

Purdue Polytechnic Masters Theses

Storing data in RDF format helps in simpler data interchange among different researchers compared to present approaches. There has been tremendous increase in the applications that use RDF data. The nature of RDF data is such that it tends to increase explosively. This makes it necessary to consider the time for retrieval and scalability of data while selecting a suitable RDF data store for developing applications. The research concentrates on comparing BigOWLIM. Bigdata, 4store and Virtuoso RDF stores on basis of their scalability and performance of storing and retrieving cancer proteomics and mass spectrometry data using SPARQL queries. In this …

Panopto Is Now Available For Lecture Capture, Veronica Trammell

Veronica O. Trammell

The Distance Learning Center has purchased a new lecture delivery option for faculty who teach online. Panopto is a lecture capture application which allows faculty to capture their lectures using a simple webcam or a sophisticated video classroom.

Local Closed World Semantics: Keep It Simple, Stupid!, Adila Krishnadhi, Kunal Sengupta, Pascal Hitzler

Computer Science and Engineering Faculty Publications

A combination of open and closed-world reasoning (usually called local closed world reasoning) is a desirable capability of knowledge representation formalisms for Semantic Web applications. However, none of the proposals made to date for extending description logics with local closed world capabilities has had any significant impact on applications. We believe that one of the key reasons for this is that current proposals fail to provide approaches which are intuitively accessible for application developers at the same time are applicable, as extensions, to expressive description logics as SROIQ, which underlies the Web Ontology Language OWL.

In this paper, we propose …

Parallel Learning To Rank For Information Retrieval, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.

Web Wisdom: An Essay On How Web 2.0 And Semantic Web Can Foster A Global Knowledge Society, Christopher Thomas, Amit P. Sheth

Kno.e.sis Publications

Admittedly this is a presumptuous title that should never be used when reporting on individual research advances. Wisdom is just not a scientific concept. In this case, though, we are reporting on recent developments on the web that lead us to believe that the web is on the way to providing a platform for not only information acquisition and business transactions but also for large scale knowledge development and decision support. It is likely that by now every web user has participated in some sort of social function or knowledge accumulating function on the web, many times without even being …

Automatic Content Generation For Video Self Modeling, Ju Shen, Anusha Raghunathan, Sen-Ching S. Cheung, Ravi R. Patel

Computer Science Faculty Publications

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him or herself. Its effectiveness in rehabilitation and education has been repeatedly demonstrated but technical challenges remain in creating video contents that depict previously unseen behaviors. In this paper, we propose a novel system that re-renders new talking-head sequences suitable to be used for VSM treatment of patients with voice disorder. After the raw footage is captured, a new speech track is either synthesized using text-to-speech or selected based on voice similarity from a database of clean speeches. …

Relevant Knowledge Helps In Choosing Right Teacher: Active Query Selection For Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou

Research Collection School Of Computing and Information Systems

Learning to adapt in a new setting is a common challenge to our knowledge and capability. New life would be easier if we actively pursued supervision from the right mentor chosen with our relevant but limited prior knowledge. This variant principle of active learning seems intuitively useful to many domain adaptation problems. In this paper, we substantiate its power for advancing automatic ranking adaptation, which is important in web search since it's prohibitive to gather enough labeled data for every search domain for fully training domain-specific rankers. For the cost-effectiveness, it is expected that only those most informative instances in …

Unsupervised Discovery Of Discourse Relations For Eliminating Intra-Sentence Polarity Ambiguities, Lanjun Zhou, Binyang Li, Wei Gao, Zhongyu Wei, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Polarity classification of opinionated sentences with both positive and negative sentiments1 is a key challenge in sentiment analysis. This paper presents a novel unsupervised method for discovering intra-sentence level discourse relations for eliminating polarity ambiguities. Firstly, a discourse scheme with discourse constraints on polarity was defined empirically based on Rhetorical Structure Theory (RST). Then, a small set of cuephrase-based patterns were utilized to collect a large number of discourse instances which were later converted to semantic sequential representations (SSRs). Finally, an unsupervised method was adopted to generate, weigh and filter new SSRs without cue phrases for recognizing discourse relations. Experimental …

A Hybrid Agent Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yew-Soon Ong, Akejariyawong Tapanuj

Research Collection School Of Computing and Information Systems

This paper presents a hybrid agent architecture that integrates the behaviours of BDI agents, specifically desire and intention, with a neural network based reinforcement learner known as Temporal DifferenceFusion Architecture for Learning and COgNition (TD-FALCON). With the explicit maintenance of goals, the agent performs reinforcement learning with the awareness of its objectives instead of relying on external reinforcement signals. More importantly, the intention module equips the hybrid architecture with deliberative planning capabilities, enabling the agent to purposefully maintain an agenda of actions to perform and reducing the need of constantly sensing the environment. Through reinforcement learning, plans can also be …

Linking Entities To A Knowledge Base With Query Expansion, Swapna Gottipati, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper we present a novel approach to entity linking based on a statistical language model-based information retrieval with query expansion. We use both local contexts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local contexts and explore a positional language model to weigh them differently based on their distances to the query. Our experiments on the TAC-KBP 2010 data show that incorporating such contextual information indeed aids in disambiguating the named entities and consistently improves the entity linking performance. Compared with the official results from KBP 2010 …

Trust Network Inference For Online Rating Data Using Generative Models, Freddy Tat Chua Chua, Ee Peng Lim

Research Collection School Of Computing and Information Systems

In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF)Model, a novel probabilistic model that generate ratings based on a number of rater’s and contributor’s factors. We demonstrate that parameters of the model can be learnt by Collapsed …

Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayang Wang, Steven C. H. Hoi, Ying He

Research Collection School Of Computing and Information Systems

In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a search-based annotation paradigm is to build a database of facial images with accurate labels. This is however challenging since facial images on the WWW are often noisy and incomplete. To improve the label quality of raw web facial images, we propose an effective Unsupervised Label Refinement (ULR) approach for refining the labels of web facial images by exploring machine learning techniques. We develop effective optimization algorithms to solve the large-scale learning …

Generating Aspect-Oriented Multi-Document Summarization With Event-Aspect Model, Peng Li, Yinglin Wang, Wei Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on …

Unsupervised Information Extraction With Distributional Prior Knowledge, Cane Wing-Ki Leung, Jing Jiang, Kian Ming A. Chai, Hai Leong Chieu, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results suggest that the proposed prior can bring substantial improvements to our task as compared to a K-means baseline and a Gaussian mixture model baseline. Specifically, the proposed prior has shown to be effective when coupled with discriminative features of the candidates.

An Information Technology (It) Based Approach For Enhancing Prompt And Effective Post-Disaster Reconstruction, Faisal Manzoor Arain

Business Review

Information technology (IT) has become strongly established as a supporting tool for many professional tasks in recent years. One application of IT, namely the knowledge management system, has attracted significant attention requiring further exploration as it has the potential to enhance processes, based on the expertise of the decision-makers. A knowledge management system can undertake intelligent tasks in a specific domain that is normally performed by highly skilled people. Typically, the success of such a system relies on the ability to represent the knowledge for a particular subject. Post-disaster reconstruction and rehabilitation is a complex issue with several dimensions. Government, …

Online Auc Maximization, Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbo Yang

Research Collection School Of Computing and Information Systems

Most studies of online learning measure the performance of a learner by classification accuracy, which is inappropriate for applications where the data are unevenly distributed among different classes. We address this limitation by developing online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions. The key challenge of online AUC maximization is that it needs to optimize the pairwise loss between two instances from different classes. This is in contrast to the classical setup of online learning where the overall loss is a sum …

Heuristic Algorithms For Balanced Multi-Way Number Partitioning, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Balanced multi-way number partitioning (BMNP) seeks to split a collection of numbers into subsets with (roughly) the same cardinality and subset sum. The problem is NP-hard, and there are several exact and approximate algorithms for it. However, existing exact algorithms solve only the simpler, balanced two-way number partitioning variant, whereas the most effective approximate algorithm, BLDM, may produce widely varying subset sums. In this paper, we introduce the LRM algorithm that lowers the expected spread in subset sums to one third that of BLDM for uniformly distributed numbers and odd subset cardinalities. We also propose Meld, a novel strategy for …

Generating Aspect-Oriented Multi-Document Summarization With Event-Aspect Model, Peng Li, Yinglin Wang, Wei Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on …

Effects Of Mentoring On Player Performance In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Kuo-Wei Hsu, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in Ever Quest II, a popular MMORPG developed by Sony Online Entertainment.

Diagnosis Of Skin Diseases Using Online Expert System, Dr. Muhammad Zubair Asghar, Muhammad Junaid Asghar

Dr. Muhammad Zubair Asghar

This paper describes Expert System (ES) for diagnosis and management of skin diseases. More than 13 types of skin diseases can be diagnosed and treated by our system. It is rule based web-supported expert system, assisting skin specialists, medical students doing specialization in dermatology, researchers as well as skin patients having computer know-how. System was developed with Java Technology. The expert rules were developed on the symptoms of each type of skin disease, and they were presented using tree-graph and inferred using forward-chaining with depth-first search method. User interaction with system is enhanced with efficient user interfaces. The web based …

Smob: The Best Of Both Worlds, Alexandre Passant, Julia Anaya, Owen Sacco, Pavan Kapanipathi

Kno.e.sis Publications

This paper presents the architecture of SMOB and the way it combines Semantic Web standards (RDF(S) / SPARQL) and new protocols such as PubSubHubbub to enable a Federated and Privacy-Aware Social Web.

Local Closed-World Reasoning With Description Logics Under The Well-Founded Semantics, Matthias Knorr, Jose Julio Alferes, Pascal Hitzler

Computer Science and Engineering Faculty Publications

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based …

Automatic Domain Model Creation Using Pattern-Based Fact Extraction, Christopher Thomas, Pankaj Mehra, Wenbo Wang, Amit P. Sheth, Gerhard Weikum, Victor Chan

Kno.e.sis Publications

This paper describes a minimally guided approach to automatic domain model creation. The first step is to carve an area of interest out of the Wikipedia hierarchy based on a simple query or other starting point. The second step is to connect the concepts in this domain hierarchy with named relationships. A starting point is provided by Linked Open Data, such as DBPedia. Based on these community-generated facts we train a pattern-based fact-extraction algorithm to augment a domain hierarchy with previously unknown relationship occurrences. Pattern vectors are learned that represent occurrences of relationships between concepts. The process described can be …

Privacy-By-Design In Federated Social Web Applications, Alexandre Passant, Owen Sacco, Julia Anaya, Pavan Kapanipathi

Kno.e.sis Publications

No abstract provided.

Regret Minimizing Audits: A Learning-Theoretic Basis For Privacy Protection, Jeremiah Blocki, Nicolas Christin, Anupam Datta, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Audit mechanisms are essential for privacy protection in permissive access control regimes, such as in hospitals where denying legitimate access requests can adversely affect patient care. Recognizing this need, we develop the first principled learning-theoretic foundation for audits. Our first contribution is a game-theoretic model that captures the interaction between the defender (e.g., hospital auditors) and the adversary (e.g., hospital employees). The model takes pragmatic considerations into account, in particular, the periodic nature of audits, a budget that constrains the number of actions that the defender can inspect, and a loss function that captures the economic impact of detected and …

Towards Human-Like Social Multi-Agents With Memetic Automaton, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Xian-Shun Chen

Research Collection School Of Computing and Information Systems

Memetics is a new science that has attracted increasing attentions in the recent decades. Beyond the formalism of simple hybrids, adaptive hybrids and memetic algorithms, the notion of memetic automaton as an adaptive entity that is self-contained and uses memes as building blocks of information is recently conceptualized in the context of computational intelligence as potential tools for effective problem-solving [1]. Taking this cue, this paper embarks a study on Memetic Multiagent system (MeM) towards human-like social agents with memetic automaton. Particularly, we introduce a potentially rich meme-inspired design and operational model, with Darwin’s theory of natural selections and Dawkins’ …

Query Weighting For Ranking Model Adaptation, Peng Cai, Wei Gao, Aoying Zhou, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at query level than document level. We present two query weighting schemes. The first compresses the query into a query feature vector, which aggregates all document instances in the same query, and then conducts query weighting …

Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li

Research Collection School Of Computing and Information Systems

Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.