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Articles 4381 - 4410 of 6727

Full-Text Articles in Physical Sciences and Mathematics

Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu Aug 2012

Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu

Research Collection School Of Computing and Information Systems

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location …

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Common Criteria Meets Realpolitik Trust, Alliances, And Potential Betrayal, Jan Kallberg Jul 2012

Common Criteria Meets Realpolitik Trust, Alliances, And Potential Betrayal, Jan Kallberg

Jan Kallberg

Common Criteria for Information Technology Security Evaluation has the ambition to be a global standard for IT-security certification. The issued certifications are mutually recognized between the signatories of the Common Criteria Recognition Arrangement. The key element in any form of mutual relationships is trust. A question raised in this paper is how far trust can be maintained in Common Criteria when additional signatories enter with conflicting geopolitical interests to earlier signatories. Other issues raised are control over production, the lack of permanent organization in the Common Criteria, which leads to concerns of being able to oversee the actual compliance. As …

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Data Mining Of Protein Databases, Christopher Assi Jul 2012

Data Mining Of Protein Databases, Christopher Assi

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Data mining of protein databases poses special challenges because many protein databases are non-relational whereas most data mining and machine learning algorithms assume the input data to be a relational database. Protein databases are non-relational mainly because they often contain set data types. We developed new data mining algorithms that can restructure non-relational protein databases so that they become relational and amenable for various data mining and machine learning tools. We applied the new restructuring algorithms to a pancreatic protein database. After the restructuring, we also applied two classification methods, such as decision tree and SVM classifiers and compared their …

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Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu Jul 2012

Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu

Kyriakos MOURATIDIS

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location …

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Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis Jul 2012

Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis

Kyriakos MOURATIDIS

Users of databases that are hosted on shared servers cannot take for granted that their queries will not be disclosed to unauthorized parties. Even if the database is encrypted, an adversary who is monitoring the I/O activity on the server may still be able to infer some information about a user query. For the particular case of a B+-tree that has its nodes encrypted, we identify properties that enable the ordering among the leaf nodes to be deduced. These properties allow us to construct adversarial algorithms to recover the B+-tree structure from the I/O traces generated by range queries. Combining …

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Heuristic Algorithms For Balanced Multi-Way Number Partitioning, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang Jul 2012

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

Kyriakos MOURATIDIS

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 …

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Twitris+: Social Media Analytics Platform For Effective Coordination, Gary Alan Smith, Amit P. Sheth, Ashutosh Sopan Jadhav, Hemant Purohit, Lu Chen, Michael Cooney, Pavan Kapanipathi, Pramod Anantharam, Pramod Koneru, Wenbo Wang Jul 2012

Twitris+: Social Media Analytics Platform For Effective Coordination, Gary Alan Smith, Amit P. Sheth, Ashutosh Sopan Jadhav, Hemant Purohit, Lu Chen, Michael Cooney, Pavan Kapanipathi, Pramod Anantharam, Pramod Koneru, Wenbo Wang

Kno.e.sis Publications

Twitris+ is a Semantic Social Media analytics platform to provide technologies for analyzing large-scale social media streams across Spatio-Temporal-Thematic (STT) and People-Content-Network (PCN) dimensions. It provides holistic situational awareness from one interface and enables organizational actors to engage in well-coordinated ways for desired tasks during emergency response.

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Embracing Analytics For A Better Competitive Edge, Tin Seong Kam Jul 2012

Embracing Analytics For A Better Competitive Edge, Tin Seong Kam

Research Collection School Of Computing and Information Systems

No abstract provided.

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Feature-Based Opinion Mining And Ranking, Magdalini Eirinaki, S. Pisal, J. Singh Jul 2012

Feature-Based Opinion Mining And Ranking, Magdalini Eirinaki, S. Pisal, J. Singh

Magdalini Eirinaki

The proliferation of blogs and social networks presents a new set of challenges and opportunities in the way information is searched and retrieved. Even though facts still play a very important role when information is sought on a topic, opinions have become increasingly important as well. Opinions expressed in blogs and social networks are playing an important role influencing everything from the products people buy to the presidential candidate they support. Thus, there is a need for a new type of search engine which will not only retrieve facts, but will also enable the retrieval of opinions. Such a search …

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Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis Jul 2012

Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Users of databases that are hosted on shared servers cannot take for granted that their queries will not be disclosed to unauthorized parties. Even if the database is encrypted, an adversary who is monitoring the I/O activity on the server may still be able to infer some information about a user query. For the particular case of a B+-tree that has its nodes encrypted, we identify properties that enable the ordering among the leaf nodes to be deduced. These properties allow us to construct adversarial algorithms to recover the B+-tree structure from the I/O traces generated by range queries. Combining …

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Determinants In Sustaining A Local Information System In The Philippines: The Case Of The Barangay Management Information System (Bmis), Charina P. Maneja, Nancy A. Tandang, Merlyne M. Paunlagui Jul 2012

Determinants In Sustaining A Local Information System In The Philippines: The Case Of The Barangay Management Information System (Bmis), Charina P. Maneja, Nancy A. Tandang, Merlyne M. Paunlagui

Journal of Public Affairs and Development

Information is important for the executive and legislative functions of local officials. The study determined the institutional and individual factors that contributed in sustaining a Barangay Management Information System (BMIS). The study was done in five provinces covering 90 randomly selected continuing barangays and 68 randomly selected non-continuing barangays. Chi-square Test of Independence was used to determine factors associated with whether the barangay will continue to sustain BMIS or not. Logistic regression analysis was also performed to determine factors that may influence barangay's decision to sustain BMIS. The identified significant individual factors that influenced the barangays' decision to sustain BMIS …

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The Latent Maximum Entropy Principle, Shaojun Wang, Dale Schuurmans, Yunxin Zhao Jul 2012

The Latent Maximum Entropy Principle, Shaojun Wang, Dale Schuurmans, Yunxin Zhao

Kno.e.sis Publications

We present an extension to Jaynes’ maximum entropy principle that incorporates latent variables. The principle of latent maximum entropy we propose is different from both Jaynes’ maximum entropy principle and maximum likelihood estimation, but can yield better estimates in the presence of hidden variables and limited training data. We first show that solving for a latent maximum entropy model poses a hard nonlinear constrained optimization problem in general. However, we then show that feasible solutions to this problem can be obtained efficiently for the special case of log-linear models---which forms the basis for an efficient approximation to the latent maximum …

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Exploring The Impact Of Knowledge And Social Environment On Influenza Prevention And Transmission In Midwestern United States High School Students, William L. Romine, Tanvi Banerjee, William S. Barrow, William R. Folk Jul 2012

Exploring The Impact Of Knowledge And Social Environment On Influenza Prevention And Transmission In Midwestern United States High School Students, William L. Romine, Tanvi Banerjee, William S. Barrow, William R. Folk

Kno.e.sis Publications

We used data from a convenience sample of 410 Midwestern United States students from six secondary schools to develop parsimonious models for explaining and predicting precautions and illness related to influenza. Scores for knowledge and perceptions were obtained using two-parameter Item Response Theory (IRT) models. Relationships between outcome variables and predictors were verified using Pearson and Spearman correlations, and nested [student within school] fixed effects multinomial logistic regression models were specified from these using Akaike’s Information Criterion (AIC). Neural network models were then formulated as classifiers using 10-fold cross validation to predict precautions and illness. Perceived barriers against taking precautions …

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What Kind Of #Communication Is Twitter? A Psycholinguistic Perspective On Communication In Twitter For The Purpose Of Emergency Coordination, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach Jul 2012

What Kind Of #Communication Is Twitter? A Psycholinguistic Perspective On Communication In Twitter For The Purpose Of Emergency Coordination, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach

Kno.e.sis Publications

The present research aims to detect coordinated citizen response within social media traffic to assist emergency response. We use domain-independent linguistic properties as the first step in narrowing the candidate set of messages for domain-dependent and computationally intensive analysis.

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K-Partite Graph Reinforcement And Its Application In Multimedia Information Retrieval, Yue Gao, Meng Wang, Rongrong Ji, Zheng-Jun Zha, Jialie Shen Jul 2012

K-Partite Graph Reinforcement And Its Application In Multimedia Information Retrieval, Yue Gao, Meng Wang, Rongrong Ji, Zheng-Jun Zha, Jialie Shen

Research Collection School Of Computing and Information Systems

In many example-based information retrieval tasks, example query actually contains multiple sub-queries. For example, in 3D object retrieval, the query is an object described by multiple views. In content-based video retrieval, the query is a video clip that contains multiple frames. Without prior knowledge, the most intuitive approach is to treat the sub-queries equally without difference. In this paper, we propose a k-partite graph reinforcement approach to fuse these sub-queries based on the to-be-retrieved database. The approach first collects the top retrieved results. These results are regarded as pseudo-relevant samples and then a k-partite graph reinforcement is performed on these …

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Information-Theoretic Multi-View Domain Adaptation, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong Jul 2012

Information-Theoretic Multi-View Domain Adaptation, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

We use multiple views for cross-domain document classification. The main idea is to strengthen the views’ consistency for target data with source training data by identifying the correlations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) based on a multi-way clustering scheme, where word and link clusters can draw together seemingly unrelated domain-specific features from both sides and iteratively boost the consistency between document clusterings based on word and link views. Experiments show that IMAM significantly outperforms state-of-the-art baselines.

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Formal Analysis Of Pervasive Computing Systems, Yan Liu, Xian Zhang, Jin Song Dong, Yang Liu, Jun Sun, Jit Biswas, Mounir Mokhtari Jul 2012

Formal Analysis Of Pervasive Computing Systems, Yan Liu, Xian Zhang, Jin Song Dong, Yang Liu, Jun Sun, Jit Biswas, Mounir Mokhtari

Research Collection School Of Computing and Information Systems

Pervasive computing systems are heterogenous and complex as they usually involve human activities, various sensors and actuators as well as middleware for system controlling. Therefore, analyzing such systems is highly nontrivial. In this work, we propose to use formal methods for analyzing pervasive computing systems. Firstly, a formal modeling framework is proposed to cover the main characteristics of pervasive computing systems (e.g., context-awareness, concurrent communications, layered architectures). Secondly, we identify the safety requirements (e.g., free of deadlocks and conflicts etc.) and propose their specifications as safety and liveness properties. Finally, we demonstrate our ideas using a case study of a …

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Adaptive Cgf For Pilots Training In Air Combat Simulation, Teck-Hou Teng, Ah-Hwee Tan, Wee-Sze Ong, Kien-Lip Lee Jul 2012

Adaptive Cgf For Pilots Training In Air Combat Simulation, Teck-Hou Teng, Ah-Hwee Tan, Wee-Sze Ong, Kien-Lip Lee

Research Collection School Of Computing and Information Systems

Training of combat fighter pilots is often conducted using either human opponents or non-adaptive computer-generated force (CGF) inserted with the doctrine for conducting air combat mission. The novelty and challenges of such non-adaptive doctrine-driven CGF is often lost quickly. Incorporating more complex knowledge manually is known to be tedious and time-consuming. Therefore, a study of using adaptive CGF to learn from the real-time interactions with human pilots to extend the existing doctrine is conducted in this work. The goal of this study is to show how an adaptive CGF can be more effective than a non-adaptive doctrine-driven CGF for simulator-based …

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Mydeal: The Context-Aware Urban Shopping Assistant, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan Jul 2012

Mydeal: The Context-Aware Urban Shopping Assistant, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

A common problem in large Urban cities, of the sort seen in Asia, is the huge number of retail options available in the city. In particular, it is not uncommon to find multiple malls, each with hundreds of stores inside, just a short distance from each other in almost every part of these cities. These factors make it incredibly hard for consumers to identify stores of interest to them in any particular mall.In response, a number of shopping assistance applications have been created for mobile phones.However, these applications mostly just allow users to know which stores are where or to …

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On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi Jul 2012

On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a …

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Online Kernel Selection: Algorithms And Evaluations, Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi Jul 2012

Online Kernel Selection: Algorithms And Evaluations, Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational …

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Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim Jul 2012

Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneousy captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) …

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Detecting Anomalous Twitter Users By Extreme Group Behaviors, Hanbo Dai, Ee-Peng Lim, Feida Zhu, Hwee Hwa Pang Jul 2012

Detecting Anomalous Twitter Users By Extreme Group Behaviors, Hanbo Dai, Ee-Peng Lim, Feida Zhu, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Twitter has enjoyed tremendous popularity in the recent years. To help categorizing and search tweets, Twitter users assign hashtags to their tweets. Given that hashtag assignment is the primary way to semantically categorizing and search tweets, it is highly susceptible to abuse by spammers and other anomalous users [1]. Popular hashtags such as #Obama and #ladygaga could be hijacked by having them added to unrelated tweets with the intent of misleading many other users or promoting specific agenda to the users. The users performing this act are known as the hashtag hijackers. As the hijackers usually abuse common sets of …

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Exact Soft Confidence-Weighted Learning, Jialei Wang, Steven C. H. Hoi Jul 2012

Exact Soft Confidence-Weighted Learning, Jialei Wang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the art algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive …

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Topic Discovery From Tweet Replies, Bingtian Dai, Ee Peng Lim, Philips Kokoh Prasetyo Jul 2012

Topic Discovery From Tweet Replies, Bingtian Dai, Ee Peng Lim, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness.

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Fast Bounded Online Gradient Descent Algorithms For Scalable Kernel-Based Online Learning, Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, Steven C. H. Hoi Jul 2012

Fast Bounded Online Gradient Descent Algorithms For Scalable Kernel-Based Online Learning, Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem of bounded kernel-based online learning that aims to constrain the number of support vectors by a predefined budget. Although several algorithms have been proposed in literature, they are neither computationally efficient due to their intensive budget maintenance strategy nor effective due to the use of simple Perceptron algorithm. To overcome these limitations, we propose a …

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Joint Learning For Coreference Resolution With Markov Logic, Yang Song, Jing Jiang, Xin Zhao, Sujian Li, Houfeng Wang Jul 2012

Joint Learning For Coreference Resolution With Markov Logic, Yang Song, Jing Jiang, Xin Zhao, Sujian Li, Houfeng Wang

Research Collection School Of Computing and Information Systems

Pairwise coreference resolution models must merge pairwise coreference decisions to generate final outputs. Traditional merging methods adopt different strategies such as the best first method and enforcing the transitivity constraint, but most of these methods are used independently of the pairwise learning methods as an isolated inference procedure at the end. We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic. Experimental results show that our joint learning system outperforms independent learning systems. Our system gives a better performance than all the learning-based systems from the CoNLL-2011 shared task on the same dataset. Compared …

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Identifying Event-Related Bursts Via Social Media Activities, Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, Xiaoming Li Jul 2012

Identifying Event-Related Bursts Via Social Media Activities, Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, Xiaoming Li

Research Collection School Of Computing and Information Systems

Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments …

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Iexplore: A Provenance-Based Application For Exploring Biomedical Knowledge, Vinh Nguyen, Olivier Bodenreider, Thomas Rindflesch, Amit P. Sheth Jun 2012

Iexplore: A Provenance-Based Application For Exploring Biomedical Knowledge, Vinh Nguyen, Olivier Bodenreider, Thomas Rindflesch, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

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W3c Semantic Sensor Networks: Ontologies, Applications, And Future Directions, Cory Andrew Henson Jun 2012

W3c Semantic Sensor Networks: Ontologies, Applications, And Future Directions, Cory Andrew Henson

Kno.e.sis Publications

Plenary Talk discussing the W3C Semantic Sensor Network, including the ontology, applications, and future directions.

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