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Articles 3571 - 3600 of 6848

Full-Text Articles in Physical Sciences and Mathematics

Adaptable Key-Policy Attribute-Based Encryption With Time Interval, Siqi Ma, Junzuo Lai, Deng, Robert H., Xuhua Ding Jun 2016

Adaptable Key-Policy Attribute-Based Encryption With Time Interval, Siqi Ma, Junzuo Lai, Deng, Robert H., Xuhua Ding

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new cryptographic primitive: adaptable KP-ABE with time interval (KP-TIABE), which is an extension of key-policy attribute-based encryption (KP-ABE). Adaptable KP-TIABE specifies a decryption time interval for every ciphertext such that the ciphertext can only be decrypted within this time interval. To be more flexible, the decryption time interval associated with a ciphertext can be adjusted on demand by a semi-trusted server. We propose a formal model for adaptable KP-TIABE, present a concrete adaptable KP-TIABE scheme and prove its security under the security model.

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Indoor Location Error-Detection Via Crowdsourced Multi-Dimensional Mobile Data, Savina Singla, Archan Misra Jun 2016

Indoor Location Error-Detection Via Crowdsourced Multi-Dimensional Mobile Data, Savina Singla, Archan Misra

Research Collection School Of Computing and Information Systems

We explore the use of multi-dimensional mobile sensing data as a means of identifying errors in one or more of those data streams. More specifically, we look at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi fingerprints, by using concurrent readings from Wi-Fi and barometer sensors from a collection of mobile devices. Our key contribution is a novel two-step process: (i) using longitudinal, crowd-sourced readings of (possibly incorrect) Wi-Fi location estimates to statistically estimate the barometer calibration offset of individual mobile devices, and (ii) then, using such offset-corrected barometer readings from devices (that are supposedly collocated) to identify …

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Towards Secure Online Distribution Of Multimedia Codestreams, Swee Won Lo May 2016

Towards Secure Online Distribution Of Multimedia Codestreams, Swee Won Lo

Dissertations and Theses Collection (Open Access)

Multimedia codestreams distributed through open and insecure networks are subjected to attacks such as malicious content tampering and unauthorized accesses. This dissertation first addresses the issue of authentication as a mean to integrity - protect multimedia codestreams against malicious tampering. Two cryptographic-based authentication schemes are proposed to authenticate generic scalable video codestreams with a multi-layered structure. The first scheme combines the salient features of hash-chaining and double error correction coding to achieve loss resiliency with low communication overhead and proxy-transparency. The second scheme further improves computation cost by replacing digital signature with a hash-based message authentication code to achieve packet-level …

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Professor Pang Hwee Hwa Appointed Dean Of Smu School Of Information Systems, Singapore Management University May 2016

Professor Pang Hwee Hwa Appointed Dean Of Smu School Of Information Systems, Singapore Management University

SMU Press Releases

The Singapore Management University (SMU) has announced today the appointment of Professor Pang Hwee Hwa as the next Dean of the SMU School of Information Systems (SIS) with effect from 1 July 2016 for a term of five years. Selected from a global pool of candidates after an extensive and rigorous global search which started in October 2015, Prof Pang’s strong commitment to research in information systems and a passion for excellence in education, make him the ideal candidate to lead the School of Information Systems.

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Graph-Aided Directed Testing Of Android Applications For Checking Runtime Privacy Behaviours, Joseph Joo Keng Chan, Lingxiao Jiang, Kiat Wee Tan, Rajesh Krishna Balan May 2016

Graph-Aided Directed Testing Of Android Applications For Checking Runtime Privacy Behaviours, Joseph Joo Keng Chan, Lingxiao Jiang, Kiat Wee Tan, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

While automated testing of mobile applications is very useful for checking run-time behaviours and specifications, its capability in discovering issues in apps is often limited in practice due to long testing time. A common practice is to randomly and exhaustively explore the whole app test space, which takes a lot of time and resource to achieve good coverage and reach targeted parts of the apps. In this paper, we present MAMBA, a directed testing system for checking privacy in Android apps. MAMBA performs path searches of user events in control-flow graphs of callbacks generated from static analysis of app bytecode. …

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From Lights Out To Lights On, Ravi Chidambaram May 2016

From Lights Out To Lights On, Ravi Chidambaram

Asian Management Insights

How Sunlabob went from providing affordable, sustainable energy in rural Laos to becoming an international turnkey operator and co-developer.

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A Horizon Decomposition Approach For The Capacitated Lot-Sizing Problem With Setup Times, Ioannis Fragkos, Zeger Degraeve, Bert De Reyck May 2016

A Horizon Decomposition Approach For The Capacitated Lot-Sizing Problem With Setup Times, Ioannis Fragkos, Zeger Degraeve, Bert De Reyck

Research Collection Lee Kong Chian School Of Business

We introduce horizon decomposition in the context of Dantzig-Wolfe decomposition, and apply it to the capacitated lot-sizing problem with setup times. We partition the problem horizon in contiguous overlapping intervals and create subproblems identical to the original problem, but of smaller size. The user has the flexibility to regulate the size of the master problem and the subproblem via two scalar parameters. We investigate empirically which parameter configurations are efficient, and assess their robustness at different problem classes. Our branch-and-price algorithm outperforms state-of-the-art branch-and-cut solvers when tested to a new data set of challenging instances that we generated. Our methodology …

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Joint Search By Social And Spatial Proximity [Extended Abstract], Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis May 2016

Joint Search By Social And Spatial Proximity [Extended Abstract], Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

The diffusion of social networks introduces new challengesand opportunities for advanced services, especially so with their ongoingaddition of location-based features. We show how applications like company andfriend recommendation could significantly benefit from incorporating social andspatial proximity, and study a query type that captures these twofold semantics.We develop highly scalable algorithms for its processing, and use real socialnetwork data to empirically verify their efficiency and efficacy.

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Anonymous Identity-Based Broadcast Encryption With Chosen-Ciphertext Security, Kai He, Jian Weng, Jia-Nan Liu, Joseph K. Liu, Wei Liu, Deng, Robert H. May 2016

Anonymous Identity-Based Broadcast Encryption With Chosen-Ciphertext Security, Kai He, Jian Weng, Jia-Nan Liu, Joseph K. Liu, Wei Liu, Deng, Robert H.

Research Collection School Of Computing and Information Systems

In this paper, we propose the first identity-based broadcast encryption scheme, which can simultaneously achieves confidentiality and full anonymity against adaptive chosen-ciphertext attacks under a standard assumption. In addition, two further desirable features are also provided: one is fully-collusion resistant which means that even if all users outside of receivers S collude they cannot obtain any information about the plaintext. The other one is stateless which means that the users in the system do not need to update their private keys when the other users join or leave our system. In particular, our scheme is highly efficient, where the public …

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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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Approximate Inference Using Dc Programming For Collective Graphical Models, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon May 2016

Approximate Inference Using Dc Programming For Collective Graphical Models, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon

Research Collection School Of Computing and Information Systems

Collective graphical models (CGMs) provide a framework for reasoning about a population of independent and identically distributed individuals when only noisy and aggregate observations are given. Previous approaches for inference in CGMs work on a junction-tree representation, thereby highly limiting their scalability. To remedy this, we show how the Bethe entropy approximation naturally arises for the inference problem in CGMs. We reformulate the resulting optimization problem as a difference-of-convex functions program that can capture different types of CGM noise models. Using the concave-convex procedure, we then develop a scalable message-passing algorithm. Empirically, our approach is highly scalable and accurate for …

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Reinforcement Learning Framework For Modeling Spatial Sequential Decisions Under Uncertainty: (Extended Abstract), Truc Viet Le, Siyuan Liu, Hoong Chuin Lau May 2016

Reinforcement Learning Framework For Modeling Spatial Sequential Decisions Under Uncertainty: (Extended Abstract), Truc Viet Le, Siyuan Liu, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider the problem of trajectory prediction, where a trajectory is an ordered sequence of location visits and corresponding timestamps. The problem arises when an agent makes sequential decisions to visit a set of spatial locations of interest. Each location bears a stochastic utility and the agent has a limited budget to spend. Given the agent's observed partial trajectory, our goal is to predict the remaining trajectory. We propose a solution framework to the problem considering both the uncertainty of utility and the budget constraint. We use reinforcement learning (RL) to model the underlying decision processes and inverse RL to …

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Learning To Rank For Bug Report Assignee Recommendation, Yuan Tian, Withthige Dinusha Ruchira Wijedasa, David Lo, Claire Le Goues May 2016

Learning To Rank For Bug Report Assignee Recommendation, Yuan Tian, Withthige Dinusha Ruchira Wijedasa, David Lo, Claire Le Goues

Research Collection School Of Computing and Information Systems

Projects receive a large number of bug reports, and resolving these reports take considerable time and human resources. To aid developers in the resolution of bug reports, various automated techniques have been proposed to identify and recommend developers to address newly reported bugs. Two families of bug assignee recommendation techniques include those that recommend developers who have fixed similar bugs before (a.k.a. activity-based techniques) and those recommend suitable developers based on the location of the bug (a.k.a. location-based techniques). Previously, each of these techniques has been investigated separately. In this work, we propose a unified model that combines information from …

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Deeper Look Into Bug Fixes: Patterns, Replacements, Deletions, And Additions, Mauricio Soto, Ferdian Thung, Chu-Pan Wong, Claire Le Goues, David Lo May 2016

Deeper Look Into Bug Fixes: Patterns, Replacements, Deletions, And Additions, Mauricio Soto, Ferdian Thung, Chu-Pan Wong, Claire Le Goues, David Lo

Research Collection School Of Computing and Information Systems

Many implementations of research techniques that automatically repair software bugs target programs written in C. Work that targets Java often begins from or compares to direct translations of such techniques to a Java context. However, Java and C are very different languages, and Java should be studied to inform the construction of repair approaches to target it. We conduct a large-scale study of bugfixing commits in Java projects, focusing on assumptions underlying common search-based repair approaches. We make observations that can be leveraged to guide high quality automatic software repair to target Java specifically, including common and uncommon statement modifications …

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Semantic Proximity Search On Graphs With Metagraph-Based Learning, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Kevin Chen-Chuan Chang, Xiao-Li Li May 2016

Semantic Proximity Search On Graphs With Metagraph-Based Learning, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Kevin Chen-Chuan Chang, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Given ubiquitous graph data such as the Web and social networks, proximity search on graphs has been an active research topic. The task boils down to measuring the proximity between two nodes on a graph. Although most earlier studies deal with homogeneous or bipartite graphs only, many real-world graphs are heterogeneous with objects of various types, giving rise to different semantic classes of proximity. For instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct classes of proximity. Thus, it becomes inadequate to only measure a …

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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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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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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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Approximating The Performance Of A "Last Mile" Transportation System, Hai Wang, Amedeo Odoni May 2016

Approximating The Performance Of A "Last Mile" Transportation System, Hai Wang, Amedeo Odoni

Research Collection School Of Computing and Information Systems

The Last Mile Problem (LMP) refers to the provision of travel service from the nearest public transportation node to a home or office. We study the supply side of this problem in a stochastic setting, with batch demands resulting from the arrival of groups of passengers who request last-mile service at urban rail stations or bus stops. Closedform approximations are derived for the performance of Last Mile Transportations Systems as a function of the fundamental design parameters of such systems. An initial set of results is obtained for the case in which a fleet of vehicles of unit capacity provides …

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On Unravelling Opinions Of Issue Specific-Silent Users In Social Media, Wei Gong, Ee-Peng Lim, Feida Zhu, Pei Hua Cher May 2016

On Unravelling Opinions Of Issue Specific-Silent Users In Social Media, Wei Gong, Ee-Peng Lim, Feida Zhu, Pei Hua Cher

Research Collection School Of Computing and Information Systems

Social media has become a popular platform for people toshare opinions. Among the social media mining researchprojects that study user opinions and issues, most focus onanalyzing posted and shared content. They could run into thedanger of non-representative findings as the opinions of userswho do not post content are overlooked, which often happensin today’s marketing, recommendation, and social sensing research.For a more complete and representative profiling ofuser opinions on various topical issues, we need to investigatethe opinions of the users even when they stay silent onthese issues. We call these users the issue specific-silent users(i-silent users). To study them and their …

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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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Hdidx: High-Dimensional Indexing For Efficient Approximate Nearest Neighbor Search, Ji Wan, Sheng Tang, Yongdong Zhang, Jintao Li, Pengcheng Wu, Steven C. H. Hoi May 2016

Hdidx: High-Dimensional Indexing For Efficient Approximate Nearest Neighbor Search, Ji Wan, Sheng Tang, Yongdong Zhang, Jintao Li, Pengcheng Wu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity.

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Online Sparse Passive Aggressive Learning With Kernels, Jing Lu, Peilin Zhao, Hoi, Steven C. H. May 2016

Online Sparse Passive Aggressive Learning With Kernels, Jing Lu, Peilin Zhao, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Conventional online kernel methods often yield an unboundedlarge number of support vectors, making them inefficient and non-scalable forlarge-scale applications. Recent studies on bounded kernel-based onlinelearning have attempted to overcome this shortcoming. Although they can boundthe number of support vectors at each iteration, most of them fail to bound thenumber of support vectors for the final output solution which is often obtainedby averaging the series of solutions over all the iterations. In this paper, wepropose a novel kernel-based online learning method, Sparse Passive Aggressivelearning (SPA), which can output a final solution with a bounded number ofsupport vectors. The key idea of …

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Mining And Clustering Mobility Evolution Patterns From Social Media For Urban Informatics, Chien-Cheng Chen, Meng-Fen Chiang, Wen-Chih Peng May 2016

Mining And Clustering Mobility Evolution Patterns From Social Media For Urban Informatics, Chien-Cheng Chen, Meng-Fen Chiang, Wen-Chih Peng

Research Collection School Of Computing and Information Systems

In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are …

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Online Passive-Aggressive Active Learning, Jing Lu, Peilin Zhao, Steven C. H. Hoi May 2016

Online Passive-Aggressive Active Learning, Jing Lu, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

We investigate online active learning techniques for online classification tasks. Unlike traditional supervised learning approaches, either batch or online learning, which often require to request class labels of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, aiming to maximize classification performance with minimal human labelling effort during the entire online learning task. In this paper, we present a new family of online active learning algorithms called Passive-Aggressive Active (PAA) learning algorithms by adapting the Passive-Aggressive algorithms in online active learning settings. Unlike conventional Perceptron-based approaches that employ only the …

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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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Stabilized Annotations For Mobile Remote Assistance, Omid Fakourfar, Kevin Ta, Richard Tang, Scott Bateman, Anthony Tang May 2016

Stabilized Annotations For Mobile Remote Assistance, Omid Fakourfar, Kevin Ta, Richard Tang, Scott Bateman, Anthony Tang

Research Collection School Of Computing and Information Systems

Recent mobile technology has provided new opportunities for creating remote assistance systems. However, mobile support systems present a particular challenge: both the camera and display are held by the user, leading to shaky video. When pointing or drawing annotations, this means that the desired target often moves, causing the gesture to lose its intended meaning. To address this problem, we investigate annotation stabilization techniques, which allow annotations to stick to their intended location. We studied two annotation systems, using three different forms of annotations, with both tablets and head-mounted displays. Our analysis suggests that stabilized annotations and head-mounted displays are …

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