Databases and Information Systems | Open Access Articles

Build Emotion Lexicon From The Mood Of Crowd Via Topic-Assisted Joint Non-Negative Matrix Factorization, Kaisong Song, Wei Gao, Ling Chen, Shi Feng, Daling Wang, Chengqi Zhang

Research Collection School Of Computing and Information Systems

Kaisong Song, Wei Gao, Ling Chen, Shi Feng, Daling Wang, and Chengqi Zhang. (2016). . In , pages 773–776, Pisa, Italy. ACM Press. https://doi.org/10.1145/2911451.2914759

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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

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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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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On Very Large Scale Test Collection For Landmark Image Search Benchmarking, Zhiyong Cheng, Jialie Shen

Research Collection School Of Computing and Information Systems

High quality test collections have been becoming more and more important for the technological advancement in geo-referenced image retrieval and analytics. In this paper, we present a large scale test collection to support robust performance evaluation of landmark image search and corresponding construction methodology. Using the approach, we develop a very large scale test collection consisting of three key components: (1) 355,141 images of 128 landmarks in five cities across three continents crawled from Flickr; (2) different kinds of textual features for each image, including surrounding text (e.g. tags), contextual data (e.g. geo-location and upload time), and metadata (e.g. uploader …

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Which Information Sources Are More Effective And Reliable In Video Search, Cheng Zhiyong, Xuanchong Li, Jialie Shen, Alexander G. Hauptmann

Research Collection School Of Computing and Information Systems

It is common that users are interested in finding video segments, which contain further information about the video contents in a segment of interest. To facilitate users to find and browse related video contents, video hyperlinking aims at constructing links among video segments with relevant information in a large video collection. In this study, we explore the effectiveness of various video features on the performance of video hyperlinking, including subtitle, metadata, content features (i.e., audio and visual), surrounding context, as well as the combinations of those features. Besides, we also test different search strategies over different types of queries, which …

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Self-Regulated Incremental Clustering With Focused Preferences, Di Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Due to their online learning nature, incremental clustering techniques can handle a continuous stream of data. In particular, various incremental clustering techniques based on Adaptive Resonance Theory (ART) have been shown to have low computational complexity in adaptive learning and are less sensitive to noisy information. However, parameter regularization in existing ART clustering techniques is applied either on different features or on different clusters exclusively. In this paper, we introduce Interest-Focused Clustering based on Adaptive Resonance Theory (IFC-ART), which self-regulates the vigilance parameter associated with each feature and each cluster. As such, we can incorporate the domain knowledge of the …

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The Impact Of Nasd Rule 2711 And Nyse Rule 472 On Analyst Behavior: The Strategic Timing Of Recommendations Issued On Weekends, Yi Dong, Nan Hu

Research Collection School Of Computing and Information Systems

Amendments to NASD Rule 2711 and NYSE Rule 472, enacted in May 2002, mandate that sell-side analysts disclose the distribution of their security recommendations by buy, hold and sell category. This regulation enhances the transparency of analysts' information and mitigates the long-recognized optimistic bias in their recommendations. However, we find that analysts are more likely to issue sell recommendations or downgrade revisions on weekends when investors have limited attention after these rule changes. This pattern is more pronounced for prestigious analysts, who are more likely to influence stock prices. Market reaction tests reveal an incomplete immediate response and a greater …

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Real-Time Salient Object Detection With A Minimum Spanning Tree, Wei-Chih Tu, Shengfeng He, Qingxiong Yang, Shao-Yi Chien

Research Collection School Of Computing and Information Systems

In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in …

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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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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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Hci Testing In Laboratory Or Field Settings, Chuan-Hoo Tan, Austin Silva, Rich Lee, Kanliang Wang, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

This paper presents perspectives from both academia and practice on how an HCI testing is to be conducted and the deliberations that go into the testing. HCI testing can be conducted in closed-door laboratory or in a field setting. While there is an increased interest in field testing of an HCI artifact, there is always an enduring concern over how to administer a field testing given that the testers will have less control over the course of testing. In this paper, we cover HCI testing deliberation as well as the operational issues of field testing, and conclude the paper with …

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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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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

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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Hci Testing In Laboratory Or Field Settings, Chuan-Hoo Tan, Austin Silva, Rich Lee, Kanliang Wang, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

This paper presents perspectives from both academia and practice on how an HCI testing is to be conducted and the deliberations that go into the testing. HCI testing can be conducted in closed-door laboratory or in a field setting. While there is an increased interest in field testing of an HCI artifact, there is always an enduring concern over how to administer a field testing given that the testers will have less control over the course of testing. In this paper, we cover HCI testing deliberation as well as the operational issues of field testing, and conclude the paper with …

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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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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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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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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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Smart Living For Elderly: Design And Human-Computer Interaction Considerations, Ranjana Sharma, Fiona Fui-Hoon Nah, Kavya Sharma, Teja S. Katta, Natalie Pang, Alvin Yong

Research Collection School Of Computing and Information Systems

To address aging challenges, we examine the concept of smart living and its applications for the elderly. Smart living refers to improving quality of life by transforming environments to become more intelligent and adaptable to users. In this paper, we discuss how smart living applications can help to address the needs of the elderly, as well as the design and human-computer interaction considerations for such applications.

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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

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

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

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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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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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

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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Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li

Research Collection School Of Computing and Information Systems

A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the one-vs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks. Moreover, this framework makes active learning practically feasible for multi-class problems, while the one-vs.-all strategy cannot. Specifically, we employ a novel randomized query technique to prioritize the informative instances. This query technique based on the hybrid criterion of "margin" and "uncertainty" can achieve a comparable mistake bound …

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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

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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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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Full-Text Articles in Databases and Information Systems

Build Emotion Lexicon From The Mood Of Crowd Via Topic-Assisted Joint Non-Negative Matrix Factorization, Kaisong Song, Wei Gao, Ling Chen, Shi Feng, Daling Wang, Chengqi Zhang

Research Collection School Of Computing and Information Systems

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

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 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

On Very Large Scale Test Collection For Landmark Image Search Benchmarking, Zhiyong Cheng, Jialie Shen

Research Collection School Of Computing and Information Systems

Which Information Sources Are More Effective And Reliable In Video Search, Cheng Zhiyong, Xuanchong Li, Jialie Shen, Alexander G. Hauptmann

Research Collection School Of Computing and Information Systems

Self-Regulated Incremental Clustering With Focused Preferences, Di Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

The Impact Of Nasd Rule 2711 And Nyse Rule 472 On Analyst Behavior: The Strategic Timing Of Recommendations Issued On Weekends, Yi Dong, Nan Hu

Research Collection School Of Computing and Information Systems

Real-Time Salient Object Detection With A Minimum Spanning Tree, Wei-Chih Tu, Shengfeng He, Qingxiong Yang, Shao-Yi Chien

Research Collection School Of Computing and Information Systems

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

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

Hci Testing In Laboratory Or Field Settings, Chuan-Hoo Tan, Austin Silva, Rich Lee, Kanliang Wang, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

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

USF Tampa Graduate Theses and Dissertations

Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le

Master's Projects

Musictrakr, Benjamin Lin

Computer Engineering

Hci Testing In Laboratory Or Field Settings, Chuan-Hoo Tan, Austin Silva, Rich Lee, Kanliang Wang, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

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

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

User Behavior Mining In Microblogging, Tuan Anh Hoang

Dissertations and Theses Collection (Open Access)

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

Research Collection School Of Computing and Information Systems

Smart Living For Elderly: Design And Human-Computer Interaction Considerations, Ranjana Sharma, Fiona Fui-Hoon Nah, Kavya Sharma, Teja S. Katta, Natalie Pang, Alvin Yong

Research Collection School Of Computing and Information Systems

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

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

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

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

Research Collection School Of Computing and Information Systems

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

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

Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li

Research Collection School Of Computing and Information Systems

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

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