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Articles 3001 - 3030 of 6721

Full-Text Articles in Physical Sciences and Mathematics

Can Instagram Posts Help Characterize Urban Micro-Events?, Kasthuri Jayarajah, Archan Misra Jul 2016

Can Instagram Posts Help Characterize Urban Micro-Events?, Kasthuri Jayarajah, Archan Misra

Research Collection School Of Computing and Information Systems

Social media content, from platforms such as Twitter and Foursquare, has enabled an exciting new field of social sensing, where participatory content generated by users has been used to identify unexpected emerging or trending events. In contrast to such text-based channels, we focus on image-sharing social applications (specifically Instagram), and investigate how such urban social sensing can leverage upon the additional multi-modal, multimedia content. Given the significantly higher fraction of geotagged content on Instagram, we aim to use such channels to go beyond identification of long-lived events (e.g., a marathon) to achieve finer-grained characterization of multiple micro-events (e.g., a person …

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On Effective Personalized Music Retrieval By Exploring Online User Behaviors, Zhiyong Cheng, Jialie Shen, Steven C. H. Hoi Jul 2016

On Effective Personalized Music Retrieval By Exploring Online User Behaviors, Zhiyong Cheng, Jialie Shen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In this paper, we study the problem of personalized text based music retrieval which takes users’ music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel DualLayer Music Preference Topic Model (DL-MPTM) is proposed to construct latent music interest space and characterize the correlations among (user, song, term). Based on the DL-MPTM, we further develop an effective personalized music retrieval system. To evaluate the system’s performance, extensive experimental studies have been conducted over two test collections to compare the proposed method with the state-of-the-art music retrieval methods. The results …

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On Effective Personalized Music Retrieval Via Exploring Online User Behaviors, Zhiyong Cheng, Jialie Shen, Steven C. H. Hoi Jul 2016

On Effective Personalized Music Retrieval Via Exploring Online User Behaviors, Zhiyong Cheng, Jialie Shen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In this paper, we study the problem of personalized text based music retrieval which takes users' music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel Dual-Layer Music Preference Topic Model (DL-MPTM) is proposed to construct latent music interest space and characterize the correlations among (user, song, term). Based on the DL-MPTM, we further develop an effective personalized music retrieval system. To evaluate the system's performance, extensive experimental studies have been conducted over two test collections to compare the proposed method with the state-of-the-art music retrieval methods. The results …

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Fine-Grained Detection Of Programming Students’ Frustration Using Keystrokes, Mouse Clicks And Interaction Logs, Hua Leong Fwa Jul 2016

Fine-Grained Detection Of Programming Students’ Frustration Using Keystrokes, Mouse Clicks And Interaction Logs, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Prolonged frustration leads to loss of confidence and eventual disinterest in the learning itself. The modelling of frustration in learning is thus important as it informs on the appropriate time to intervene to sustain the interest and motivation of students. To automatically detect learner’s frustration in a naturalistic learning environment, the novel use of keystrokes, mouse clicks and interaction patterns of students captured within the context of a tutoring system was proposed. The modelling approach was described and a comparison was made between the proposed model using Bayesian Network and the baseline Naïve Bayes model. With the formulation of an …

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Outlier Detection In Complex Categorical Data By Modeling The Feature Value Couplings, Guansong Pang, Longbing Cao, Ling Chen Jul 2016

Outlier Detection In Complex Categorical Data By Modeling The Feature Value Couplings, Guansong Pang, Longbing Cao, Ling Chen

Research Collection School Of Computing and Information Systems

This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial …

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Word Clouds With Latent Variable Analysis For Visual Comparison Of Documents, Tuan M. V. Le, Hady W. Lauw Jul 2016

Word Clouds With Latent Variable Analysis For Visual Comparison Of Documents, Tuan M. V. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Word cloud is a visualization form for text that is recognized for its aesthetic, social, and analytical values. Here, we are concerned with deepening its analytical value for visual comparison of documents. To aid comparative analysis of two or more documents, users need to be able to perceive similarities and differences among documents through their word clouds. However, as we are dealing with text, approaches that treat words independently may impede accurate discernment of similarities among word clouds containing different words of related meanings. We therefore motivate the principle of displaying related words in a coherent manner, and propose to …

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Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., Shuigeng Zhou Jul 2016

Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., Shuigeng Zhou

Research Collection School Of Computing and Information Systems

On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal portfolios based …

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Where Is The Goldmine? Finding Promising Business Locations Through Facebook Data Analytics, Jovian Lin, Richard Oentaryo, Ee-Peng Lim, Casey Vu, Adrian Vu, Agus Kwee Jul 2016

Where Is The Goldmine? Finding Promising Business Locations Through Facebook Data Analytics, Jovian Lin, Richard Oentaryo, Ee-Peng Lim, Casey Vu, Adrian Vu, Agus Kwee

Research Collection School Of Computing and Information Systems

If you were to open your own cafe, would you not want to effortlessly identify the most suitable location to set up your shop? Choosing an optimal physical location is a critical decision for numerous businesses, as many factors contribute to the final choice of the location. In this paper, we seek to address the issue by investigating the use of publicly available Facebook Pages data-which include user "check-ins", types of business, and business locations-to evaluate a user-selected physical location with respect to a type of business. Using a dataset of 20,877 food businesses in Singapore, we conduct analysis of …

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Two Roads, One Destination: A Journey Of Discovery, Karen Joc, Peta J. Hopkins, Jessie Donaghey, Wendy Abbott Jun 2016

Two Roads, One Destination: A Journey Of Discovery, Karen Joc, Peta J. Hopkins, Jessie Donaghey, Wendy Abbott

Karen Joc

The adoption of resource discovery platforms has been a growing trend in libraries. However, few libraries have reported on the transition from one discovery layer to another, and only a few institutions have discussed two discovery layers available in the same institution at the same time. Bond University Library recently implemented Alma as its library management system, and with this change a new discovery platform, Primo, was implemented to supersede the existing Summon platform. This paper presents the results of a usability study undertaken at Bond University Library in the move from one discovery layer to another.

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Blind And Visually Impaired Users Adaptation To Web Environments: A Qualitative Study, Raneem Saqr Jun 2016

Blind And Visually Impaired Users Adaptation To Web Environments: A Qualitative Study, Raneem Saqr

USF Tampa Graduate Theses and Dissertations

Although much research exists on human behavior in online environments, research on users with disabilities is still rare. To draw more attention to this population, this dissertation explored browsing patterns and adaptive behaviors of people with visual disability across different online environments common in daily activities: social network, e-commerce, online information, and search engines’ websites. The main objective of this study is to propose a conceptual framework of how blind and visually impaired users browse and adapt to different web environments. We achieve this objective using a qualitative approach through three studies. In the first study, the researchers collect data …

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Exploring The Human Body Space: A Geographical Information System Based Anatomical Atlas, Antonio Barbeito, Marco Painho, Pedro Cabral, João Goyri O'Neill Jun 2016

Exploring The Human Body Space: A Geographical Information System Based Anatomical Atlas, Antonio Barbeito, Marco Painho, Pedro Cabral, João Goyri O'Neill

Journal of Spatial Information Science

Anatomical atlases allow mapping the anatomical structures of the human body. Early versions of these systems consisted of analogical representations with informative text and labeled images of the human body. With computer systems, digital versions emerged and the third and fourth dimensions were introduced. Consequently, these systems increased their efficiency, allowing more realistic visualizations with improved interactivity and functionality. The 4D atlases allow modeling changes over time on the structures represented. The anatomical atlases based on geographic information system (GIS) environments allow the creation of platforms with a high degree of interactivity and new tools to explore and analyze the …

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A Context-Sensitive Conceptual Framework For Activity Modeling, Rahul Deb Das, Stephan Winter Jun 2016

A Context-Sensitive Conceptual Framework For Activity Modeling, Rahul Deb Das, Stephan Winter

Journal of Spatial Information Science

Human motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in …

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Μ-Shapes: Delineating Urban Neighborhoods Using Volunteered Geographic Information, Matt Aadland, Christopher Farah, Kevin Magee Jun 2016

Μ-Shapes: Delineating Urban Neighborhoods Using Volunteered Geographic Information, Matt Aadland, Christopher Farah, Kevin Magee

Journal of Spatial Information Science

Urban neighborhoods are a unique form of geography in that their boundaries rely on a social definition rather than a well-defined physical or administrative boundary. Currently, geographic gazetteers capture little more than then the centroid of a neighborhood, limiting potential applications of the data. In this paper, we present µ-shapes, an algorithm that employs fuzzy-set theory to model neighborhood boundaries suitable for populating gazetteers using volunteered geographic information (VGI). The algorithm is evaluated using a reference dataset and VGI from the Map Kibera Project. A confusion matrix comparison between the reference dataset and µ-shape's output demonstrated high sensitivity and accuracy. …

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Creating The 2011 Area Classification For Output Areas (2011 Oac), Christopher G. Gale, Alexander D. Singleton, Andrew G. Bates, Paul A. Longley Jun 2016

Creating The 2011 Area Classification For Output Areas (2011 Oac), Christopher G. Gale, Alexander D. Singleton, Andrew G. Bates, Paul A. Longley

Journal of Spatial Information Science

This paper presents the methodology that has been used to create the 2011 Area Classification for Output Areas (2011 OAC). This extends a lineage of widely used public domain census-only geodemographic classifications in the UK. It provides an update to the successful 2001 OAC methodology, and summarizes the social and physical structure of neighborhoods using data from the 2011 UK Census. The results of a user engagement exercise that underpinned the creation of an updated methodology for the 2011 OAC are also presented. The 2011 OAC comprises 8 Supergroups, 26 Groups, and 76 Subgroups. An example of the results of …

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Two Roads, One Destination: A Journey Of Discovery, Karen Joc, Peta J. Hopkins, Jessie Donaghey, Wendy Abbott Jun 2016

Two Roads, One Destination: A Journey Of Discovery, Karen Joc, Peta J. Hopkins, Jessie Donaghey, Wendy Abbott

Wendy Abbott

The adoption of resource discovery platforms has been a growing trend in libraries. However, few libraries have reported on the transition from one discovery layer to another, and only a few institutions have discussed two discovery layers available in the same institution at the same time. Bond University Library recently implemented Alma as its library management system, and with this change a new discovery platform, Primo, was implemented to supersede the existing Summon platform. This paper presents the results of a usability study undertaken at Bond University Library in the move from one discovery layer to another.

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Two Roads, One Destination: A Journey Of Discovery, Karen Joc, Peta J. Hopkins, Jessie Donaghey, Wendy Abbott Jun 2016

Two Roads, One Destination: A Journey Of Discovery, Karen Joc, Peta J. Hopkins, Jessie Donaghey, Wendy Abbott

Wendy Abbott

The adoption of resource discovery platforms has been a growing trend in libraries. However, few libraries have reported on the transition from one discovery layer to another, and only a few institutions have discussed two discovery layers available in the same institution at the same time. Bond University Library recently implemented Alma as its library management system, and with this change a new discovery platform, Primo, was implemented to supersede the existing Summon platform. This paper presents the results of a usability study undertaken at Bond University Library in the move from one discovery layer to another.

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Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le Jun 2016

Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le

Master's Projects

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper …

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Musictrakr, Benjamin Lin Jun 2016

Musictrakr, Benjamin Lin

Computer Engineering

MusicTrackr is an IoT device that musicians attach to their instruments. The device has a start and stop button that allows users to record their playing sessions. Each recorded session is sent wirelessly to a cloud database. An accompanying website displays all of the recorded sessions, organized by date. After picking a specific date, the user can view graphs showing total practice time and average session length as well play back any recordings during that date. In addition, the user may add comments to any specific date or recording. Lastly, the user may tag a specific date with a color …

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An Experimental Investigation Of Product Competition And Marketing In Social Networks, Cen Chen, Zhiling Guo, Shih-Fen Cheng, Hoong Chuin Lau Jun 2016

An Experimental Investigation Of Product Competition And Marketing In Social Networks, Cen Chen, Zhiling Guo, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We conduct computational experiment using Facebook data to evaluate competing firms’ initial market seeding and subsequent targeted marketing strategies that influence consumers’ new product adoption decisions. We find that firms generally overspend their advertising budget in the market seeding phase. In the subsequent market advertising phase, a coupon strategy (equivalent to price discount) generally yields higher market share than the strategy of distributing free product samples. The effect is more significant when both price and product quality are low. We offer managerial insights into firms’ effective competition strategies for new product introduction in the presence of consumers’ word of mouth …

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An Economic Analysis Of Consumer Learning For Online Entertainment Shopping, Jin Li, Zhiling Guo, Geoffrey K.F. Tso Jun 2016

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

Research Collection School Of Computing and Information Systems

Entertainment shopping supported by pay-to-bid auction is an emerging online business model in recent years. Consumers expect both entertainment value and monetary return from their participation in entertainment shopping. We propose a dynamic structural model to study consumers’ online shopping behavior. We analyze the learning process of consumers from two perspectives based on the Bayesian updating framework: (1) consumers update their beliefs about the entertainment value through their repeated personal participation experiences, and (2) consumers infer the expected monetary payoffs on the website by observing the publically available auction ending price information. We estimate the model using a large dataset …

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Learning Natural Language Inference With Lstm, Shuohang Wang, Jing Jiang Jun 2016

Learning Natural Language Inference With Lstm, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI). In this paper, we propose a special long short-term memory (LSTM) architecture for NLI. Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea. Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution …

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Protecting The Nectar Of The Ganga River Through Game-Theoretic Factory Inspections, Benjamin Ford, Matthew Brown, Amulya Yadav, Amandeep Singh, Arunesh Sinha, Biplav Srivastava, Christopher Kiekintveld, Tambe Millind Jun 2016

Protecting The Nectar Of The Ganga River Through Game-Theoretic Factory Inspections, Benjamin Ford, Matthew Brown, Amulya Yadav, Amandeep Singh, Arunesh Sinha, Biplav Srivastava, Christopher Kiekintveld, Tambe Millind

Research Collection School Of Computing and Information Systems

Leather is an integral part of the world economy and a substantial income source for developing countries. Despite government regulations on leather tannery waste emissions, inspection agencies lack adequate enforcement resources, and tanneries’ toxic wastewaters wreak havoc on surrounding ecosystems and communities. Previous works in this domain stop short of generating executable solutions for inspection agencies. We introduce NECTAR - the first security game application to generate environmental compliance inspection schedules. NECTAR’s game model addresses many important real-world constraints: a lack of defender resources is alleviated via a secondary inspection type; imperfect inspections are modeled via a heterogeneous failure rate; …

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User Behavior Mining In Microblogging, Tuan Anh Hoang Jun 2016

User Behavior Mining In Microblogging, Tuan Anh Hoang

Dissertations and Theses Collection (Open Access)

This dissertation addresses the modeling of factors concerning microblogging users' content and behavior. We focus on two sets of factors. The first set includes behavioral factors of users and content items driving content propagation in microblogging. The second set consists of latent topics and communities of users as the users are engaged in content generation and behavior adoptions. These two sets of factors are extremely important in many applications, e.g., network monitoring and recommender systems. In the first part of this dissertation, we identify user virality, user susceptibility, and content virality as three behavioral factors that affect users' behaviors in …

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Finding The Shortest Path In Stochastic Vehicle Routing: A Cardinality Minimization Approach, Zhiguang Cao, Hongliang Guo, Jie Zhang, Dusit Niyato, Ulrich Fastenrath Fastenrath Jun 2016

Finding The Shortest Path In Stochastic Vehicle Routing: A Cardinality Minimization Approach, Zhiguang Cao, Hongliang Guo, Jie Zhang, Dusit Niyato, Ulrich Fastenrath Fastenrath

Research Collection School Of Computing and Information Systems

This paper aims at solving the stochastic shortest path problem in vehicle routing, the objective of which is to determine an optimal path that maximizes the probability of arriving at the destination before a given deadline. To solve this problem, we propose a data-driven approach, which directly explores the big data generated in traffic. Specifically, we first reformulate the original shortest path problem as a cardinality minimization problem directly based on samples of travel time on each road link, which can be obtained from the GPS trajectory of vehicles. Then, we apply an l(1)-norm minimization technique and its variants to …

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Poster: Improving Communication And Communicability With Smarter Use Of Text-Based Messages On Mobile And Wearable Devices, Kenny T. W. Choo Jun 2016

Poster: Improving Communication And Communicability With Smarter Use Of Text-Based Messages On Mobile And Wearable Devices, Kenny T. W. Choo

Research Collection School Of Computing and Information Systems

While smartphones have undoubtedly afforded many modern conveniences such as emails, instant messaging or web search, the notifications from smartphones conversely impact our lives through a deluge of information, or stress arising from expectations that we should turn our immediate attention to them (e.g., work emails). In my latest research, we find that the glanceability of smartwatches may provide an opportunity to reduce the perceived disruption from mobile notifications. Text is a common medium for communication in smart devices, the application of natural language processing on text, together with the physical affordances of smartwatches, present exciting opportunities for research to …

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Collective Rumor Correction On The Death Hoax Of A Political Figure In Social Media, Alton Y. K. Chua, Sin-Mei Cheah, Dion Hoe-Lian Goh, Ee-Peng Lim Jun 2016

Collective Rumor Correction On The Death Hoax Of A Political Figure In Social Media, Alton Y. K. Chua, Sin-Mei Cheah, Dion Hoe-Lian Goh, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Conversations on social media networks that discuss a crisis incident as it unfolds have become a norm in recent years. Left to its own devices, such conversations could quickly degenerate into rumor mills. Little research has thus far examined the correction of rumors on social media. Using the third person effect as a theoretical underpinning, we developed a model of collective rumor correction on social media based on an incident surrounding the death hoax of a political figure. Tweets from Twitter were collected and analyzed for the period when a spike of circulating rumors speculating the demise of Singapore's first …

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Qcri At Semeval-2016 Task 4: Probabilistic Methods For Binary And Ordinal Quantification, Giovanni Da San Martino, Wei Gao, Fabrizio Sebastiani Jun 2016

Qcri At Semeval-2016 Task 4: Probabilistic Methods For Binary And Ordinal Quantification, Giovanni Da San Martino, Wei Gao, Fabrizio Sebastiani

Research Collection School Of Computing and Information Systems

. (2016). n. In , pages 58—63, San Diego, California, USA. Association for Computational Linguistics. (1st place in sub-task E of Sentiment Analysis in Twitter)

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Video Modeling And Learning On Riemannian Manifold For Emotion Recognition In The Wild, Mengyi Liu, Ruiping Wang, Shaoxin Li, Zhiwu Huang, Shiguang Shan, Xilin Chen Jun 2016

Video Modeling And Learning On Riemannian Manifold For Emotion Recognition In The Wild, Mengyi Liu, Ruiping Wang, Shaoxin Li, Zhiwu Huang, Shiguang Shan, Xilin Chen

Research Collection School Of Computing and Information Systems

In this paper, we present the method for our submission to the emotion recognition in the wild challenge (EmotiW). The challenge is to automatically classify the emotions acted by human subjects in video clips under real-world environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/ distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, …

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Link Prediction In A Weighted Network Using Support Vector Machine, Jan Miles Co, Proceso L. Fernandez Jr Jun 2016

Link Prediction In A Weighted Network Using Support Vector Machine, Jan Miles Co, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

Link prediction is a field under network analysis that deals with the existence or emergence of links. In this study, we investigate the effect of using weighted networks for two link prediction techniques, which are the Vector Auto Regression (VAR) technique and our proposed modified VAR that uses Support Vector Machine (SVM). Using a co-authorship network from DBLP as the dataset and the Area Under the Receiver Operating Curve (AUC-ROC) as the fitness metric, the results show that the performance of both VAR and SVM are surprisingly lower in the weighted network than in the unweighted network. In an attempt …

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Geometric Aspects And Auxiliary Features To Top-K Processing [Advanced Seminar], Kyriakos Mouratidis Jun 2016

Geometric Aspects And Auxiliary Features To Top-K Processing [Advanced Seminar], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision making software on PCs, PDAs and smart-phones. The objective of this seminar is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries that have a strong geometric nature. The seminar will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.

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