Computer Sciences Commons^™

Dynamic Fusion With Intra-And Inter-Modality Attention Flow For Visual Question Answering, Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven C. H. Hoi, Xiaogang Wang, Hongsheng Li

Research Collection School Of Computing and Information Systems

Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that the proposed dynamic intra-modality attention flow conditioned on the other modality can dynamically modulate the intramodality attention of the target modality, which is vital for multimodality feature fusion. Experimental evaluations on the …

View, Like, Comment, Post: Analyzing User Engagement By Topic At 4 Levels Across 5 Social Media Platforms For 53 News Organizations, Kholoud K. Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We evaluate the effects of the topics of social media posts on audiences across five social media platforms (i.e., Facebook, Instagram, Twitter, YouTube, and Reddit) at four levels of user engagement. We collected 3,163,373 social posts from 53 news organizations across five platforms during an 8month period. We analyzed the differences in news organization platform strategies by focusing on topic variations by organization and the corresponding effect on user engagement at four levels. Findings show that topic distribution varies by platform, although there are some topics that are popular across most platforms. User engagement levels vary both by topics and …

Examining Augmented Virtuality Impairment Simulation For Mobile App Accessibility Design, Tsu Wei, Kenny (Zhu Shuwei, Kenny) Choo, Rajesh Krishna Balan, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

With mobile apps rapidly permeating all aspects of daily living with use by all segments of the population, it is crucial to support the evaluation of app usability for specific impaired users to improve app accessibility. In this work, we examine the effects of using our augmented virtuality impairment simulation system–Empath-D–to support experienced designer-developers to redesign a mockup of commonly used mobile application for cataract-impaired users, comparing this with existing tools that aid designing for accessibility. We show that the use of augmented virtuality for assessing usability supports enhanced usability challenge identification, finding more defects and doing so more accurately …

Worker Demographics And Earnings On Amazon Mechanical Turk: An Exploratory Analysis, Kotaro Hara, Kristy Milland, Benjamin V. Hanrahan, Chris Callison-Burch, Abigail Adams, Saiph Savage, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

Prior research reported that workers on Amazon Mechanical Turk (AMT) are underpaid, earning about $2/h. But the prior research did not investigate the difference in wage due to worker characteristics (e.g., country of residence). We present the first data-driven analysis on wage gap on AMT. Using work log data and demographic data collected via online survey, we analyse the gap in wage due to different factors. We show that there is indeed wage gap; for example, workers in the U.S. earn $3.01/h while those in India earn $1.41/h on average.

Celltrademap: Delineating Trade Areas For Urban Commercial Districts With Cellular Networks, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Yunhao Liu, Zheng Yang

Research Collection School Of Computing and Information Systems

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantity where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this paper, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. CellTradeMap extracts robust location information from the irregularly …

Managing The Power And Pitfalls Of Data In Ai, Singapore Management University

Perspectives@SMU

Ethics and education are crucial in maintaining data privacy and augmenting human ability

“It’s difficult to imagine the power that you’re going to have when so many different sorts of data are available.” – Tim Berners-Lee, father of the Internet

“Before Google, and long before Facebook, Bezos had realised that the greatest value of an online company lay in the consumer data it collected.” – George Packer, author for the New Yorker

The Future Robo-Advisor, Catalin Burlacu

MITB Thought Leadership Series

The accelerated digitalisation of both people and business around the world today is having a huge impact on the investment management and advisory space. The addition of new and vastly larger data sets, as well as exponentially more sophisticated analytical tools to turn that data into usable information is constantly changing the way investments are decided on, made and managed.

Distilling Managerial Insights And Lessons From Ai Projects At Singapore's Changi Airport (Part 2), Steve Lee, Steven M. Miller

Asian Management Insights

Since 2017, Changi Airport group (CAG) has initiated a host of pilot projects that use connective and intelligent technologies to enable its move towards digital transformation and SMART Airport Vision. This has resulted in a first wave of deployment of AI and Machine Learning-enabled applications across various functions that can better sense, analyse, predict, and interact with people.

Ai Gets Real At Singapore's Changi Airport (Part 1), Steve Lee, Steven M. Miller

Asian Management Insights

Ranked as the best airport for seven consecutive years, Singapore’s Changi Airport is lauded the world over for the efficient, safe, pleasurable and seamless service it offers the millions of passengers that pass through its facilities annually. Much of Changi Airport’s success can be attributed to the organisation’s customer-oriented business focus and deeply embedded culture of service excellence, combined with a host of advanced technologies operating invisibly in the background. The framework for this technology enablement is Changi Airport Group’s (CAG’s) SMART Airport Vision—an enterprise-wide approach to connective technologies that leverages sensors, data fusion, data analytics, and artificial intelligence (AI), …

On-The-Fly Android Static Analysis With Applications In Vulnerability Discovery, Daoyuan Wu

Dissertations and Theses Collection (Open Access)

Static analysis is a common program analysis technique extensively used in the software security field. Widely-used static analysis tools for Android, e.g., Amandroid and FlowDroid, perform the whole-app analysis which is comprehensive yet at the cost of huge overheads. In this dissertation, we make a first attempt to explore a novel on-demand analysis that creatively leverages bytecode search to guide inter-procedural analysis on the fly or just in time, and develop such on-the-fly analysis into a tool, called BackDroid, for Android apps. We further explore how the core technique of on-the-fly static analysis in BackDroid can enable different vulnerability studies …

Socially-Enriched Multimedia Data Co-Clustering, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Heterogeneous data co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by the empirical weighting of the multimodal features. This chapter explains how to use the Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART) generalized heterogeneous fusion adaptive resonance theory for clustering large-scale web multimedia documents. Specifically, GHF-ART is designed to handle multimedia data with an arbitrarily rich level of meta-information. For handling …

Clustering And Its Extensions In The Social Media Domain, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

This chapter summarizes existing clustering and related approaches for the identified challenges as described in Sect. 1.2 and presents the key branches of social media mining applications where clustering holds a potential. Specifically, several important types of clustering algorithms are first illustrated, including clustering, semi-supervised clustering, heterogeneous data co-clustering, and online clustering. Subsequently, Sect. 2.5 presents a review on existing techniques that help decide the value of the predefined number of clusters (required by most clustering algorithms) automatically and highlights the clustering algorithms that do not require such a parameter. It better illustrates the challenge of input parameter sensitivity of …

Concluding Remarks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

This chapter summarizes the major contributions in this book and discusses their possible positions and requirements in some future scenarios. Section 8.1 follows the book structure to revisit the key contributions of this book in both theories and applications. The developed algorithms, such as the VA-ARTs for hyperparameter adaptation and the GHF-ART for multimedia representation and fusion, and the four applications, such as clustering and retrieving socially enriched multimedia data, are concentrated using one paragraph and three paragraphs, respectively. In Sect. 8.2, the roles of the proposed ART-embodied algorithms in social media clustering tasks are highlighted, and their possible evolutions …

Community Discovery In Heterogeneous Social Networks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a priori, do not address the weighting problem for fusing heterogeneous types of links, and have a heavy computational cost. This chapter studies the commonly used social links of users and explores the feasibility of the proposed heterogeneous data co-clustering algorithm GHF-ART, as introduced in Sect. 3.6, for discovering user communities in social networks. Contrary to the existing algorithms proposed for this task, GHF-ART performs real-time matching of patterns and one-pass learning, which guarantees …

Pinchlist: Leveraging Pinch Gestures For Hierarchical List Navigation On Smartphones, Teng Han, Jie Liu, Khalad Hasan, Mingming Fan, Junhyeok Kim, Jiannan Li, Xiangmin Fan, Feng Tian, Edward Lank, Pourang Irani

Research Collection School Of Computing and Information Systems

Intensive exploration and navigation of hierarchical lists on smartphones can be tedious and time-consuming as it often requires users to frequently switch between multiple views. To overcome this limitation, we present PinchList, a novel interaction design that leverages pinch gestures to support seamless exploration of multi-level list items in hierarchical views. With PinchList, sub-lists are accessed with a pinch-out gesture whereas a pinch-in gesture navigates back to the previous level. Additionally, pinch and flick gestures are used to navigate lists consisting of more than two levels. We conduct a user study to refine the design parameters of PinchList such as …

A Blockchain-Based Location Privacy-Preserving Crowdsensing System, Mengmeng Yang, Tianqing Zhu, Kaitai Liang, Wanlei Zhou, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the support of portable electronic devices and crowdsensing, a new class of mobile applications based on the Internet of Things (IoT) application is emerging. Crowdsensing enables workers with mobile devices to travel to specified locations and collect data, then send it back to the requester for rewards. However, the majority of the existing crowdsensing systems are based on centralized servers, which are prone to a high chance of attack, intrusion, and manipulation. Further, during the process of transmitting information to and from the service server, the worker's location is usually exposed. This raises the potential risk of a privacy …

Adversarial Sample Detection For Deep Neural Network Through Model Mutation Testing, Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, Zhang Peixin

Research Collection School Of Computing and Information Systems

No abstract provided.

Towards Personalized Data-Driven Bundle Design With Qos Constraint, Mustafa Misir, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we study the bundle design problem for offering personalized bundles of services using historical consumer redemption data. The problem studied here is for an operator managing multiple service providers, each responsible for an attraction, in a leisure park. Given the specific structure of interactions between service providers, consumers and the operator, a bundle of services is beneficial for the operator when the bundle is underutilized by service consumers. Such revenue structure is commonly seen in the cable television and leisure industries, creating strong incentives for the operator to design bundles containing lots of not-so-popular services. However, as …

The Challenge Of Collaborative Iot-Based Inferencing In Adversarial Settings, Archan Misra, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Kasthuri Jayarajah

Research Collection School Of Computing and Information Systems

In many practical environments, resource-constrained IoT nodes are deployed with varying degrees of redundancy/overlap--i.e., their data streams possess significant spatiotemporal correlation. We posit that collaborative inferencing, whereby individual nodes adjust their inferencing pipelines to incorporate such correlated observations from other nodes, can improve both inferencing accuracy and performance metrics (such as latency and energy overheads). However, such collaborative models are vulnerable to adversarial behavior by one or more nodes, and thus require mechanisms that identify and inoculate against such malicious behavior. We use a dataset of 8 outdoor cameras to (a) demonstrate that such collaborative inferencing can improve people counting …

A Homophily-Free Community Detection Framework For Trajectories With Delayed Responses, Chung-Kyun Han, Shih-Fen Cheng, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

No abstract provided.

Bilateral Liability-Based Contracts In Information Security Outsourcing, Kai-Lung Hui, Ping Fan Ke, Yuxi Yao, Wei Thoo Yue

Research Collection School Of Computing and Information Systems

We study the efficiency of bilateral liability-based contracts in managed security services (MSSs). We model MSS as a collaborative service with the protection quality shaped by the contribution of both the service provider and the client. We adopt the negligence concept from the legal profession to design two novel contracts: threshold-based liability contract and variable liability contract. We find that they can achieve the first best outcome when postbreach effort verification is feasible. More importantly, they are more efficient than a multilateral contract when the MSS provider assumes limited liability. Our results show that bilateral liability-based contracts can work in …

Graph Based Optimization For Multiagent Cooperation, Arambam James Singh, Akshat Kumar

Research Collection School Of Computing and Information Systems

We address the problem of solving math programs defined over a graph where nodes represent agents and edges represent interaction among agents. The objective and constraint functions of this program model the task agent team must perform and the domain constraints. In this multiagent setting, no single agent observes the complete objective and all the constraints of the program. Thus, we develop a distributed message-passing approach to solve this optimization problem. We focus on the class of graph structured linear and quadratic programs (LPs/QPs) which can model important multiagent coordination frameworks such as distributed constraint optimization (DCOP). For DCOPs, our …

Re-Org: An Online Repositioning Guidance Agent, Muralidhar Konda, Pradeep Varakantham, Aayush Saxena, Meghna Lowalekar

Research Collection School Of Computing and Information Systems

No abstract provided.

Pptds: A Privacy-Preserving Truth Discovery Scheme In Crowd Sensing Systems, Chuan Zhang, Liehuang Zhu, Chang Xu, Kashif Sharif, Ximeng Liu

Research Collection School Of Computing and Information Systems

Benefiting from the fast development of human-carried mobile devices, crowd sensing has become an emerging paradigm to sense and collect data. However, reliability of sensory data provided by participating users is still a major concern. To address this reliability challenge, truth discovery is an effective technology to improve data accuracy, and has garnered significant attention. Nevertheless, many of state of art works in truth discovery, either failed to address the protection of participants' privacy or incurred tremendous overhead on the user side. In this paper, we first propose a privacy-preserving truth discovery scheme, named PPTDS-I, which is implemented on two …

Unifying Knowledge Graph Learning And Recommendation: Towards A Better Understanding Of User Preferences, Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow level of entity raw data or embeddings. This may lead to suboptimal performance, since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system. In this paper, we jointly learn the model of recommendation …

Designated-Server Identity-Based Authenticated Encryption With Keyword Search For Encrypted Emails, Hongbo Li, Qiong Huang, Jian Shen, Guomin Yang, Willy Susilo

Research Collection School Of Computing and Information Systems

In encrypted email system, how to search over encrypted cloud emails without decryption is an important and practical problem. Public key encryption with keyword search (PEKS) is an efficient solution to it. However, PEKS suffers from the complex key management problem in the public key infrastructure. Its variant in the identity-based setting addresses the drawback, however, almost all the schemes does not resist against offline keyword guessing attacks (KGA) by inside adversaries. In this work we introduce the notion of designated-server identity-based authenticated encryption with keyword search (dIBAEKS), in which the email sender authenticates the message while encrypting so that …

Multimodal Review Generation For Recommender Systems, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Key to recommender systems is learning user preferences, which are expressed through various modalities. In online reviews, for instance, this manifests in numerical rating, textual content, as well as visual images. In this work, we hypothesize that modelling these modalities jointly would result in a more holistic representation of a review towards more accurate recommendations. Therefore, we propose Multimodal Review Generation (MRG), a neural approach that simultaneously models a rating prediction component and a review text generation component. We hypothesize that the shared user and item representations would augment the rating prediction with richer information from review text, while sensitizing …

Robust Factorization Machine: A Doubly Capped Norms Minimization, Chenghao Liu, Teng Zhang, Jundong Li, Jianwen Yin, Peilin Zhao, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Factorization Machine (FM) is a general supervised learning framework for many AI applications due to its powerful capability of feature engineering. Despite being extensively studied, existing FM methods have several limitations in common. First of all, most existing FM methods often adopt the squared loss in the modeling process, which can be very sensitive when the data for learning contains noises and outliers. Second, some recent FM variants often explore the low-rank structure of the feature interactions matrix by relaxing the low-rank minimization problem as a trace norm minimization, which cannot always achieve a tight approximation to the original one. …

Project Sidewalk: A Web-Based Crowdsourcing Tool For Collecting Sidewalk Accessibility Data At Scale, Manaswi Saha, Michael Saugstad, Hanuma Maddali, Aileen Zeng, Ryan Holland, Steven Bower, Aditya Dash, Sage Chen, Anthony Li, Kotaro Hara, Jon Froehlich

Research Collection School Of Computing and Information Systems

We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement …

Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this …