Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (3441)
- Software Engineering (2143)
- Artificial Intelligence and Robotics (1665)
- Information Security (1057)
- Numerical Analysis and Scientific Computing (1024)
-
- Graphics and Human Computer Interfaces (921)
- Engineering (858)
- Social and Behavioral Sciences (661)
- Business (625)
- Theory and Algorithms (493)
- Computer Engineering (431)
- Operations Research, Systems Engineering and Industrial Engineering (400)
- Programming Languages and Compilers (379)
- OS and Networks (323)
- Communication (297)
- Social Media (240)
- Public Affairs, Public Policy and Public Administration (207)
- Transportation (185)
- Medicine and Health Sciences (178)
- Education (164)
- Management Information Systems (164)
- Data Storage Systems (160)
- E-Commerce (146)
- Health Information Technology (107)
- International and Area Studies (107)
- Asian Studies (106)
- Technology and Innovation (100)
- Digital Communications and Networking (96)
- Keyword
-
- Deep learning (123)
- Machine learning (121)
- Social media (74)
- Artificial intelligence (70)
- Reinforcement learning (69)
-
- Data mining (64)
- Privacy (61)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (54)
- Optimization (53)
- Security (53)
- Visualization (51)
- Software engineering (49)
- Training (49)
- Neural networks (48)
- Online learning (48)
- Task analysis (48)
- Anomaly detection (47)
- Singapore (47)
- Twitter (46)
- Feature extraction (45)
- Blockchain (44)
- Collaboration (44)
- Large Language Models (43)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Machine Learning (38)
- Publication Year
- File Type
Articles 5101 - 5130 of 8481
Full-Text Articles in Computer Sciences
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 …
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 …
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 …
Reinforcement Learning Framework For Modeling Spatial Sequential Decisions Under Uncertainty: (Extended Abstract), Truc Viet Le, Siyuan Liu, Hoong Chuin Lau
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 …
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.
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. …
Leveraging Automated Privacy Checking For Design Of Mobile Privacy Protection Mechanisms, Joseph Joo Keng Chan, Lingxiao Jiang, Kiat Wee Tan, Rajesh Balan
Leveraging Automated Privacy Checking For Design Of Mobile Privacy Protection Mechanisms, Joseph Joo Keng Chan, Lingxiao Jiang, Kiat Wee Tan, Rajesh Balan
Research Collection School Of Computing and Information Systems
While mobile platforms rely on developers to follow good practices in privacy design, developers might not always adhere. In addition, it is often difficult for users to understand the privacy behaviour of their applications without some prolonged usage. To aid in these issues, we describe on-going research to improve privacy protection by utilizing techniques that mine privacy information from application binaries as a grey-box (Automated Privacy Checking). The outputs can then be utilized to improve the users' ability to exercise privacy-motivated discretion. We conducted a user study to observe the effects of presenting information on leak-causing triggers within applications in …
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 …
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 …
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 …
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). …
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 …
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 …
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 …
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 …
Efficient 3d Dental Identification Via Signed Feature Histogram And Learning Keypoint Detection, Zhiyuan Zhang, Sim Heng Ong, Xin Zhong, Kelvin W. C. Foong
Efficient 3d Dental Identification Via Signed Feature Histogram And Learning Keypoint Detection, Zhiyuan Zhang, Sim Heng Ong, Xin Zhong, Kelvin W. C. Foong
Research Collection School Of Computing and Information Systems
Current methods of dental identification are mainly based on 2D dental radiographs which suffer from speed and accuracy limitations. In this paper, we present an efficient dental identification approach based on 3D dental models. We propose a novel shape descriptor, the Signed Feature Histogram (SFH), which is highly discriminative and can be easily computed to describe the local surface. Based on the SFH, a learning keypoint detection method is adopted to accurately detect the desired keypoints on both antemortem (AM) and postmortem (PM) models. For a given PM model, the optimal initial alignment to the AM model to be matched …
You Are Being Watched: Bystanders' Perspective On The Use Of Camera Devices In Public Spaces, Samarth Singhal, Carman Neustaedter, Thecla Schiphorst, Anthony Tang, Abhisekh Patra, Rui Pan
You Are Being Watched: Bystanders' Perspective On The Use Of Camera Devices In Public Spaces, Samarth Singhal, Carman Neustaedter, Thecla Schiphorst, Anthony Tang, Abhisekh Patra, Rui Pan
Research Collection School Of Computing and Information Systems
We are observing an increase in the use of smartphones and wearable devices in public places for streaming and recording video. Yet the use of cameras in these devices can infringe upon the privacy of the people in the surrounding environment by inadvertently capturing them. This paper presents findings from an in-situ exploratory study that investigates bystanders' reactions and feelings towards streaming and recording videos with smartphones and wearable glasses in public spaces. We use the interview results to guide an exploration of design directions for mobile video.
Modeling Human-Like Non-Rationality For Social Agents, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann
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 …
Overcoming Open Source Project Entry Barriers With A Portal For Newcomers, Igor Steinmacher, Tayana U. Conte, Christoph Treude, Marco Aurélio Gerosa
Overcoming Open Source Project Entry Barriers With A Portal For Newcomers, Igor Steinmacher, Tayana U. Conte, Christoph Treude, Marco Aurélio Gerosa
Research Collection School Of Computing and Information Systems
Community-based Open Source Software (OSS) projects are usually self-organized and dynamic, receiving contributions from distributed volunteers. Newcomer are important to the survival, long-term success, and continuity of these communities. However, newcomers face many barriers when making their first contribution to an OSS project, leading in many cases to dropouts. Therefore, a major challenge for OSS projects is to provide ways to support newcomers during their first contribution. In this paper, we propose and evaluate FLOSScoach, a portal created to support newcomers to OSS projects. FLOSScoach was designed based on a conceptual model of barriers created in our previous work. To …
Augmenting Api Documentation With Insights From Stack Overflow, Christoph Treude, Martin P. Robillard
Augmenting Api Documentation With Insights From Stack Overflow, Christoph Treude, Martin P. Robillard
Research Collection School Of Computing and Information Systems
Software developers need access to different kinds of information which is often dispersed among different documentation sources, such as API documentation or Stack Overflow. We present an approach to automatically augment API documentation with “insight sentences” from Stack Overflow— sentences that are related to a particular API type and that provide insight not contained in the API documentation of that type. Based on a development set of 1,574 sentences, we compare the performance of two state-of-the-art summarization techniques as well as a pattern-based approach for insight sentence extraction. We then present SISE, a novel machine learning based approach that uses …
Context-Aware Advertisement Recommendation For High-Speed Social News Feeding, Yuchen Li, Dongxiang Zhang, Ziquan Lan, Kian-Lee Tan
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 …
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 …
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
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 …