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Research Collection School Of Computing and Information Systems

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Full-Text Articles in Databases and Information Systems

On Very Large Scale Test Collection For Landmark Image Search Benchmarking, Zhiyong Cheng, Jialie Shen Jul 2016

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 Jul 2016

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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Learning Compact Visual Representation With Canonical Views For Robust Mobile Landmark Search, Lei Zhu, Jialie Shen, Xiaobai Liu, Liang Xie, Liqiang Nie Jul 2016

Learning Compact Visual Representation With Canonical Views For Robust Mobile Landmark Search, Lei Zhu, Jialie Shen, Xiaobai Liu, Liang Xie, Liqiang Nie

Research Collection School Of Computing and Information Systems

Mobile Landmark Search (MLS) recently receives increasing attention. However, it still remains unsolved due to two important issues. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images. This paper proposes a Canonical View based Compact Visual Representation (2CVR) to handle these problems via novel three-stage learning. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multimodal sparse coding is applied …

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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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Hci Testing In Laboratory Or Field Settings, Chuan-Hoo Tan, Austin Silva, Rich Lee, Kanliang Wang, Fiona Fui-Hoon Nah Jun 2016

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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Patient Engagement In The Medical Facility Waiting Room Using Gamified Healthcare Information Delivery, Raheel Hassan, Nathan W. Twyman, Fiona Fui-Hoon Nah, Keng Siau Jun 2016

Patient Engagement In The Medical Facility Waiting Room Using Gamified Healthcare Information Delivery, Raheel Hassan, Nathan W. Twyman, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

This study explores the proposition that medical facility waiting rooms are an opportune setting to engage with and educate patients while they are waiting for care. In collaboration with emergency department (ED) personnel, we developed ER Hero, a tablet-based application for waiting rooms that introduces patients to ED professionals and operations through mini-games and story-like interaction. We evaluated this prototype with human participants to determine how well it performed when compared to paper-based information disclosure presenting the same information. Participants using the application exhibited increased ED knowledge, decreased nervousness, and increased interest. The gamified application outperformed a paper-based approach on …

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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 Jun 2016

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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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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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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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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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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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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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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Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li Jun 2016

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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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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Context-Aware Advertisement Recommendation For High-Speed Social News Feeding, Yuchen Li, Dongxiang Zhang, Ziquan Lan, Kian-Lee Tan May 2016

Context-Aware Advertisement Recommendation For High-Speed Social News Feeding, Yuchen Li, Dongxiang Zhang, Ziquan Lan, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Social media advertising is a multi-billion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for each user based on their personal interests. However, as user interests often evolve slowly, the user may end up receiving repetitive ads. In this paper, we propose a context-aware advertising framework that takes into account the relatively static personal interests as well as the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we first propose …

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Modeling Autobiographical Memory In Human-Like Autonomous Agents, Di Wang, Ah-Hwee Tan, Chunyan Miao May 2016

Modeling Autobiographical Memory In Human-Like Autonomous Agents, Di Wang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories of its own and to wander in mind. Our model, named Autobiographical Memory-Adaptive Resonance Theory network (AM-ART), is designed to capture autobiographical memories, comprising pictorial snapshots of one’s life experiences together with the associated context, namely time, location, people, activity, and emotion. In terms of both network structure and dynamics, AM-ART coincides with the autobiographical memory …

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#Greysanatomy Vs. #Yankees: Demographics And Hashtag Use On Twitter, Jisun An, Ingmar Weber May 2016

#Greysanatomy Vs. #Yankees: Demographics And Hashtag Use On Twitter, Jisun An, Ingmar Weber

Research Collection School Of Computing and Information Systems

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also …

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Learning To Query: Focused Web Page Harvesting For Entity Aspects, Yuan Fang, Vincent W. Zheng, Kevin Chen-Chuan Chang May 2016

Learning To Query: Focused Web Page Harvesting For Entity Aspects, Yuan Fang, Vincent W. Zheng, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

As the Web hosts rich information about real-world entities, our information quests become increasingly entity centric. In this paper, we study the problem of focused harvesting of Web pages for entity aspects, to support downstream applications such as business analytics and building a vertical portal. Given that search engines are the de facto gateways to assess information on the Web, we recognize the essence of our problem as Learning to Query (L2Q) - to intelligently select queries so that we can harvest pages, via a search engine, focused on an entity aspect of interest. Thus, it is crucial to quantify …

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Using Abstractions To Solve Opportunistic Crime Security Games At Scale, Chao Zhang, Victor Bucarey, Ayan Mukhopadhyay, Arunesh Sinha, Qian. Yundi, Yevgeniy Vorobeychik, Milind Tambe May 2016

Using Abstractions To Solve Opportunistic Crime Security Games At Scale, Chao Zhang, Victor Bucarey, Ayan Mukhopadhyay, Arunesh Sinha, Qian. Yundi, Yevgeniy Vorobeychik, Milind Tambe

Research Collection School Of Computing and Information Systems

In this paper, we aim to deter urban crime by recommending optimal police patrol strategies against opportunistic criminals in large scale urban problems. While previous work has tried to learn criminals' behavior from real world data and generate patrol strategies against opportunistic crimes, it cannot scale up to large-scale urban problems. Our first contribution is a game abstraction framework that can handle opportunistic crimes in large-scale urban areas. In this game abstraction framework, we model the interaction between officers and opportunistic criminals as a game with discrete targets. By merging similar targets, we obtain an abstract game with fewer total …

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Capture: A New Predictive Anti-Poaching Tool For Wildlife Protection, Thanh H. Nguyen, Arunesh Sinha, Shahrzad Gholami, Andrew Plumptre, Lucas Joppa, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Rob Critchlow May 2016

Capture: A New Predictive Anti-Poaching Tool For Wildlife Protection, Thanh H. Nguyen, Arunesh Sinha, Shahrzad Gholami, Andrew Plumptre, Lucas Joppa, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Rob Critchlow

Research Collection School Of Computing and Information Systems

Wildlife poaching presents a serious extinction threat to many animalspecies. Agencies (“defenders”) focused on protecting suchanimals need tools that help analyze, model and predict poacheractivities, so they can more effectively combat such poaching; suchtools could also assist in planning effective defender patrols, buildingon the previous security games research.To that end, we have built a new predictive anti-poaching tool,CAPTURE (Comprehensive Anti-Poaching tool with Temporaland observation Uncertainty REasoning). CAPTURE providesfour main contributions. First, CAPTURE’s modeling of poachersprovides significant advances over previous models from behavioralgame theory and conservation biology. This accounts for:(i) the defender’s imperfect detection of poaching signs; (ii) complextemporal dependencies in …

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Learning Adversary Behavior In Security Games: A Pac Model Perspective, Arunesh Sinha, Debarun Kar, Milind Tambe May 2016

Learning Adversary Behavior In Security Games: A Pac Model Perspective, Arunesh Sinha, Debarun Kar, Milind Tambe

Research Collection School Of Computing and Information Systems

Recent applications of Stackelberg Security Games (SSG), from wildlife crime to urban crime, have employed machine learning tools to learn and predict adversary behavior using available data about defender-adversary interactions. Given these recent developments, this paper commits to an approach of directly learning the response function of the adversary. Using the PAC model, this paper lays a firm theoretical foundation for learning in SSGs (e.g., theoretically answer questions about the numbers of samples required to learn adversary behavior) and provides utility guarantees when the learned adversary model is used to plan the defender's strategy. The paper also aims to answer …

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Are You Charlie Or Ahmed? Cultural Pluralism In Charlie Hebdo Response On Twitter, Jisun An, Haewoon Kwak, Yelena Mejova, Sonia Alonso Saenz De Oger, Braulio Gomez Fortes May 2016

Are You Charlie Or Ahmed? Cultural Pluralism In Charlie Hebdo Response On Twitter, Jisun An, Haewoon Kwak, Yelena Mejova, Sonia Alonso Saenz De Oger, Braulio Gomez Fortes

Research Collection School Of Computing and Information Systems

We study the response to the Charlie Hebdo shootings of January 7, 2015 on Twitter across the globe. We ask whether the stances on the issue of freedom of speech can be modeled using established sociological theories, including Huntington’s culturalist Clash of Civilizations, and those taking into consideration social context, including Density and Interdependence theories. We find support for Huntington’s culturalist explanation, in that the established traditions and norms of one’s “civilization” predetermine some of one’s opinion. However, at an individual level, we also find social context to play a significant role, with non-Arabs living in Arab countries using #JeSuisAhmed …

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Modeling Human-Like Non-Rationality For Social Agents, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann May 2016

Modeling Human-Like Non-Rationality For Social Agents, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann

Research Collection School Of Computing and Information Systems

Humans are not rational beings. Deviations from rationality in human thinking are currently well documented [25] as non-reducible to rational pursuit of egoistic benefit or its occasional distortion with temporary emotional excitation, as it is often assumed. This occurs not only outside conceptual reasoning or rational goal realization but also subconsciously and often in certainty that they did not and could not take place ‘in my case’. Non-rationality can no longer be perceived as a rare affective abnormality in otherwise rational thinking, but as a systemic, permanent quality, ’a design feature’ of human cognition. While social psychology has systematically addressed …

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An Autonomous Agent For Learning Spatiotemporal Models Of Human Daily Activities, Shan Gao, Ah-Hwee Tan May 2016

An Autonomous Agent For Learning Spatiotemporal Models Of Human Daily Activities, Shan Gao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Activities of Daily Living (ADLs) refer to activities performed by individuals on a daily basis. As ADLs are indicatives of a person’s habits, lifestyle, and well being, learning the knowledge of people’s ADL routine has great values in the healthcare and consumer domains. In this paper, we propose an autonomous agent, named Agent for Spatia-Temporal Activity Pattern Modeling (ASTAPM), being able to learn spatial and temporal patterns of human ADLs. ASTAPM utilises a self-organizing neural network model named Spatiotemporal - Adaptive Resonance Theory (ST-ART). ST-ART is capable of integrating multimodal contextual information, involving the time and space, wherein the ADL …

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Fast Weighted Histograms For Bilateral Filtering And Nearest Neighbor Searching, Shengfeng He, Qingxiong Yang, Rynson W. H. Lau, Ming-Hsuan Yang May 2016

Fast Weighted Histograms For Bilateral Filtering And Nearest Neighbor Searching, Shengfeng He, Qingxiong Yang, Rynson W. H. Lau, Ming-Hsuan Yang

Research Collection School Of Computing and Information Systems

The locality sensitive histogram (LSH) injects spatial information into the local histogram in an efficient manner, and has been demonstrated to be very effective for visual tracking. In this paper, we explore the application of this efficient histogram in two important problems. We first extend the LSH to linear time bilateral filtering, and then propose a new type of histogram for efficiently computing edge-preserving nearest neighbor fields (NNFs). While the existing histogram-based bilateral filtering methods are the state of the art for efficient grayscale image processing, they are limited to box spatial filter kernels only. In our first application, we …

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Efficient Verifiable Computation Of Linear And Quadratic Functions Over Encrypted Data, Ngoc Hieu Tran, Hwee Hwa Pang, Robert H. Deng May 2016

Efficient Verifiable Computation Of Linear And Quadratic Functions Over Encrypted Data, Ngoc Hieu Tran, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

In data outsourcing, a client stores a large amount of data on an untrusted server; subsequently, the client can request the server to compute a function on any subset of the data. This setting naturally leads to two security requirements: confidentiality of input data, and authenticity of computations. Existing approaches that satisfy both requirements simultaneously are built on fully homomorphic encryption, which involves expensive computation on the server and client and hence is impractical. In this paper, we propose two verifiable homomorphic encryption schemes that do not rely on fully homomorphic encryption. The first is a simple and efficient scheme …

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