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Full-Text Articles in Entire DC Network
The Design Of Assistive Location-Based Technologies For People With Ambulatory Disabilities: A Formative Study, Kotaro Hara, Christine Chen, Jon E. Froehlich
The Design Of Assistive Location-Based Technologies For People With Ambulatory Disabilities: A Formative Study, Kotaro Hara, Christine Chen, Jon E. Froehlich
Research Collection School Of Computing and Information Systems
In this paper, we investigate how people with mobility impairments assess and evaluate accessibility in the built environment and the role of current and emerging location-based technologies therein. We conducted a three-part formative study with 20 mobility impaired participants: a semi-structured interview (Part 1), a participatory design activity (Part 2), and a design probe activity (Part 3). Part 2 and 3 actively engaged our participants in exploring and designing the future of what we call assistive location-based technologies (ALTs) location-based technologies that specifically incorporate accessibility features to support navigating, searching, and exploring the physical world. Our Part 1 findings highlight …
Learning Adversary Behavior In Security Games: A Pac Model Perspective, Arunesh Sinha, Debarun Kar, Milind Tambe
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 …
Joint Search By Social And Spatial Proximity [Extended Abstract], Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis
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.
Anonymous Identity-Based Broadcast Encryption With Chosen-Ciphertext Security, Kai He, Jian Weng, Jia-Nan Liu, Joseph K. Liu, Wei Liu, Deng, Robert H.
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 …
Hdidx: High-Dimensional Indexing For Efficient Approximate Nearest Neighbor Search, Ji Wan, Sheng Tang, Yongdong Zhang, Jintao Li, Pengcheng Wu, Steven C. H. Hoi
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.
Graph-Aided Directed Testing Of Android Applications For Checking Runtime Privacy Behaviours, Joseph Joo Keng Chan, Lingxiao Jiang, Kiat Wee Tan, Rajesh Krishna Balan
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. …
Mining Social Ties Beyond Homophily, Hongwei Liang, Ke Wang, Feida Zhu
Mining Social Ties Beyond Homophily, Hongwei Liang, Ke Wang, Feida Zhu
Research Collection School Of Computing and Information Systems
Summarizing patterns of connections or social tiesin a social network, in terms of attributes information on nodesand edges, holds a key to the understanding of how the actorsinteract and form relationships. We formalize this problem asmining top-k group relationships (GRs), which captures strongsocial ties between groups of actors. While existing works focuson patterns that follow from the well known homophily principle,we are interested in social ties that do not follow from homophily,thus, provide new insights. Finding top-k GRs faces new challenges:it requires a novel ranking metric because traditionalmetrics favor patterns that are expected from the homophilyprinciple; it requires an innovative …
Mining And Clustering Mobility Evolution Patterns From Social Media For Urban Informatics, Chien-Cheng Chen, Meng-Fen Chiang, Wen-Chih Peng
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 …
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
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 …
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
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 …
Learning To Query: Focused Web Page Harvesting For Entity Aspects, Yuan Fang, Vincent W. Zheng, Kevin Chen-Chuan Chang
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 …
Online Passive-Aggressive Active Learning, Jing Lu, Peilin Zhao, Steven C. H. Hoi
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 …
On Unravelling Opinions Of Issue Specific-Silent Users In Social Media, Wei Gong, Ee-Peng Lim, Feida Zhu, Pei Hua Cher
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 …
Efficient Verifiable Computation Of Linear And Quadratic Functions Over Encrypted Data, Ngoc Hieu Tran, Hwee Hwa Pang, Robert H. Deng
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 …
Euclidean Co-Embedding Of Ordinal Data For Multi-Type Visualization, Dung D. Le, Hady W. Lauw
Euclidean Co-Embedding Of Ordinal Data For Multi-Type Visualization, Dung D. Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data.
Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau
Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We study the problem of optimizing the trajectories of agents moving over a network given their preferences over which nodes to visit subject to operational constraints on the network. In our running example, a theme park manager optimizes which attractions to include in a day-pass to maximize the pass’s appeal to visitors while keeping operational costs within budget. The first challenge in this combinatorial optimization problem is that it involves quantities (expected visit frequencies of each attraction) that cannot be expressed analytically, for which we use the Sample Average Approximation. The second challenge is that while sampling is typically done …
Approximate Inference Using Dc Programming For Collective Graphical Models, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon
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 …
Temporal Kernel Descriptors For Learning With Time-Sensitive Patterns, Doyen Sahoo, Abhishek Sharma, Hoi, Steven C. H., Peilin Zhao
Temporal Kernel Descriptors For Learning With Time-Sensitive Patterns, Doyen Sahoo, Abhishek Sharma, Hoi, Steven C. H., Peilin Zhao
Research Collection School Of Computing and Information Systems
Detecting temporal patterns is one of the most prevalent challenges while mining data. Often, timestamps or information about when certain instances or events occurred can provide us with critical information to recognize temporal patterns. Unfortunately, most existing techniques are not able to fully extract useful temporal information based on the time (especially at different resolutions of time). They miss out on 3 crucial factors: (i) they do not distinguish between timestamp features (which have cyclical or periodic properties) and ordinary features; (ii) they are not able to detect patterns exhibited at different resolutions of time (e.g. different patterns at the …
Online Sparse Passive Aggressive Learning With Kernels, Jing Lu, Peilin Zhao, Hoi, Steven C. H.
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 …
Mobile Big Data Analytics Using Deep Learning And Apache Spark, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Zhu Han
Mobile Big Data Analytics Using Deep Learning And Apache Spark, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Zhu Han
Research Collection School Of Computing and Information Systems
The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, …
A Core Task Abstraction Approach To Hierarchical Reinforcement Learning [Extended Abstract], Zhuoru Li, Akshay Narayan, Tze-Yun Leong
A Core Task Abstraction Approach To Hierarchical Reinforcement Learning [Extended Abstract], Zhuoru Li, Akshay Narayan, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
We propose a new, core task abstraction (CTA) approach to learning the relevant transition functions in model-based hierarchical reinforcement learning. CTA exploits contextual independences of the state variables conditional on the task-specific actions; its promising performance is demonstrated through a set of benchmark problems.
Learning To Rank For Bug Report Assignee Recommendation, Yuan Tian, Withthige Dinusha Ruchira Wijedasa, David Lo, Claire Le Goues
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 …
Domain-Specific Cross-Language Relevant Question Retrieval, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo, Qingye Wang, Shanping Li
Domain-Specific Cross-Language Relevant Question Retrieval, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo, Qingye Wang, Shanping Li
Research Collection School Of Computing and Information Systems
In software development process, developers often seek solutions to the technical problems they encounter by searching relevant questions on Q&A sites. When developers fail to find solutions on Q&A sites in their native language (e.g., Chinese), they could translate their query and search on the Q&A sites in another language (e.g., English). However, developers who are non-native English speakers often are not comfortable to ask or search questions in English, as they do not know the proper translation of the Chinese technical words into the English technical words. Furthermore, the process of manually formulating cross-language queries and determining the weight …
How Android App Developers Manage Power Consumption? An Empirical Study By Mining Power Management Commits, Lingfeng Bao, David Lo, Xin Xia, Xinyu Wang, Cong Tian
How Android App Developers Manage Power Consumption? An Empirical Study By Mining Power Management Commits, Lingfeng Bao, David Lo, Xin Xia, Xinyu Wang, Cong Tian
Research Collection School Of Computing and Information Systems
As Android platform becomes more and more popular, a large amount of Android applications have been developed. When developers design and implement Android applications, power consumption management is an important factor to consider since it affects the usability of the applications. Thus, it is important to help developers adopt proper strategies to manage power consumption. Interestingly, today, there is a large number of Android application repositories made publicly available in sites such as GitHub. These repositories can be mined to help crystalize common power management activities that developers do. These in turn can be used to help other developers to …
Deeper Look Into Bug Fixes: Patterns, Replacements, Deletions, And Additions, Mauricio Soto, Ferdian Thung, Chu-Pan Wong, Claire Le Goues, David Lo
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 …
Robust Influence Maximization, Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar
Robust Influence Maximization, Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar
Research Collection School Of Computing and Information Systems
Influence Maximization is the problem of finding a fixed size set of nodes, which will maximize the expected number of influenced nodes in a social network. The number of influenced nodes is dependent on the influence strength of edges that can be very noisy. The noise in the influence strengths can be modeled using a random noise or adversarial noise model. It has been shown that all random processes that independently affect edges of the graph can be absorbed into the activation probabilities themselves and hence random noise can be captured within the independent cascade model. On the other hand, …
A Key-Insulated Cp-Abe With Key Exposure Accountability For Secure Data Sharing In The Cloud, Hanshu Hong, Zhixin Sun, Ximeng Liu
A Key-Insulated Cp-Abe With Key Exposure Accountability For Secure Data Sharing In The Cloud, Hanshu Hong, Zhixin Sun, Ximeng Liu
Research Collection School Of Computing and Information Systems
ABE has become an effective tool for data protection in cloud computing. However, since users possessing the same attributes share the same private keys, there exist some malicious users exposing their private keys deliberately for illegal data sharing without being detected, which will threaten the security of the cloud system. Such issues remain in many current ABE schemes since the private keys are rarely associated with any user specific identifiers. In order to achieve user accountability as well as provide key exposure protection, in this paper, we propose a key-insulated ciphertext policy attribute based encryption with key exposure accountability (KI-CPABE-KEA). …
Efspredictor: Predicting Configuration Bugs With Ensemble Feature Selection, Bowen Xu, David Lo, Xin Xia, Ashish Sureka, Shanping Li
Efspredictor: Predicting Configuration Bugs With Ensemble Feature Selection, Bowen Xu, David Lo, Xin Xia, Ashish Sureka, Shanping Li
Research Collection School Of Computing and Information Systems
The configuration of a system determines the system behavior and wrong configuration settings can adversely impact system's availability, performance, and correctness. We refer to these wrong configuration settings as configuration bugs. The importance of configuration bugs has prompted many researchers to study it, and past studies can be grouped into three categories: detection, localization, and fixing of configuration bugs. In the work, we focus on the detection of configuration bugs, in particular, we follow the line-of-work that tries to predict if a bug report is caused by a wrong configuration setting. Automatically prediction of whether a bug is a configuration …
Attacks And Defence On Android Free Floating Windows, Lingyun Ying, Yao Cheng, Yemian Lu, Yacong Gu, Purui Su, Dengguo Feng
Attacks And Defence On Android Free Floating Windows, Lingyun Ying, Yao Cheng, Yemian Lu, Yacong Gu, Purui Su, Dengguo Feng
Research Collection School Of Computing and Information Systems
Nowadays, the popular Android is so closely involved in people's daily lives that people rely on Android to perform critical operations and trust Android with sensitive information. It is of great importance to guarantee the usability and security of Android which, however, is such a huge system that a potential threat may arise from any part of it. In this paper, we focus on the Free Floating window (FF window) which is a category of windows that can appear freely above any other applications. It can share the screen space with other FF windows, dialogs, and activities. An FF window …
Stabilized Annotations For Mobile Remote Assistance, Omid Fakourfar, Kevin Ta, Richard Tang, Scott Bateman, Anthony Tang
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 …