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Research Collection School Of Computing and Information Systems

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Articles 3121 - 3150 of 8479

Full-Text Articles in Computer Sciences

Reinforcement Learning For Zone Based Multiagent Pathfinding Under Uncertainty, Jiajing Ling, Tarun Gupta, Akshat Kumar Oct 2020

Reinforcement Learning For Zone Based Multiagent Pathfinding Under Uncertainty, Jiajing Ling, Tarun Gupta, Akshat Kumar

Research Collection School Of Computing and Information Systems

We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applications of autonomous vehicles such as drone traffic management, we present zone-based path finding (or ZBPF) where agents move among zones, and agents' movements require uncertain travel time. Furthermore, each zone can accommodate multiple agents (as per its capacity). We also develop a simulator for ZBPF which provides a clean interface from the …

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Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh Oct 2020

Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh

Research Collection School Of Computing and Information Systems

Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such …

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European Floating Strike Lookback Options: Alpha Prediction And Generation Using Unsupervised Learning, Tristan Lim, Aldy Gunawan, Chin Sin Ong Oct 2020

European Floating Strike Lookback Options: Alpha Prediction And Generation Using Unsupervised Learning, Tristan Lim, Aldy Gunawan, Chin Sin Ong

Research Collection School Of Computing and Information Systems

This research utilized the intrinsic quality of European floating strike lookback call options, alongside selected return and volatility parameters, in a K-means clustering environment, to recommend an alpha generative trading strategy. The result is an elegant easy-to-use alpha strategy based on the option mechanisms which identifies investment assets with high degree of significance. In an upward trending market, the research had identified European floating strike lookback call option as an evaluative criterion and investable asset, which would both allow investors to predict and profit from alpha opportunities. The findings will be useful for (i) buy-side investors seeking alpha generation and/or …

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Dual-Slam: A Framework For Robust Single Camera Navigation, Huajian Huang, Wen-Yan Lin, Siying Liu, Dong Zhang, Sai-Kit Yeung Oct 2020

Dual-Slam: A Framework For Robust Single Camera Navigation, Huajian Huang, Wen-Yan Lin, Siying Liu, Dong Zhang, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position estimates. This makes SLAM fast but also makes it vulnerable to local pose estimation failures. As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle. This paper attempts to correct this problem. We note that while local pose estimation is ill-conditioned, pose estimation over longer sequences is well-conditioned. Thus, local pose estimation errors eventually manifest themselves as mapping inconsistencies. When this occurs, we save the current map and activate two …

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Hysia: Serving Dnn-Based Video-To-Retail Applications In Cloud, Huaizheng Zhang, Yuanming Li, Qiming Ai, Yong Luo, Yonggang Wen, Yichao Jin, Nguyen Binh Duong Ta Oct 2020

Hysia: Serving Dnn-Based Video-To-Retail Applications In Cloud, Huaizheng Zhang, Yuanming Li, Qiming Ai, Yong Luo, Yonggang Wen, Yichao Jin, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Combining video streaming and online retailing (V2R) has been a growing trend recently. In this paper, we provide practitioners and researchers in multimedia with a cloud-based platform named Hysia for easy development and deployment of V2R applications. The system consists of: 1) a back-end infrastructure providing optimized V2R related services including data engine, model repository, model serving and content matching; and 2) an application layer which enables rapid V2R application prototyping. Hysia addresses industry and academic needs in large-scale multimedia by: 1) seamlessly integrating state-of-the-art libraries including NVIDIA video SDK, Facebook faiss, and gRPC; 2) efficiently utilizing GPU computation; and …

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Federated Topic Discovery: A Semantic Consistent Approach, Yexuan Shi, Yongxin Tong, Zhiyang Su, Di Jiang, Zimu Zhou, Wenbin Zhang Oct 2020

Federated Topic Discovery: A Semantic Consistent Approach, Yexuan Shi, Yongxin Tong, Zhiyang Su, Di Jiang, Zimu Zhou, Wenbin Zhang

Research Collection School Of Computing and Information Systems

General-purpose topic models have widespread industrial applications. Yet high-quality topic modeling is becoming increasingly challenging because accurate models require large amounts of training data typically owned by multiple parties, who are often unwilling to share their sensitive data for collaborative training without guarantees on their data privacy. To enable effective privacy-preserving multiparty topic modeling, we propose a novel federated general-purpose topic model named private and consistent topic discovery (PC-TD). On the one hand, PC-TD seamlessly integrates differential privacy in topic modeling to provide privacy guarantees on sensitive data of different parties. On the other hand, PC-TD exploits multiple sources of …

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Multi-Modal Cooking Workflow Construction For Food Recipes, Liangming Pan, Jingjing Chen, Jianlong Wu, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Yugang Jiang, Tat-Seng Chua Oct 2020

Multi-Modal Cooking Workflow Construction For Food Recipes, Liangming Pan, Jingjing Chen, Jianlong Wu, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Yugang Jiang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe. This is a non-trivial task that involves common-sense reasoning. However, existing efforts rely on hand-crafted features to extract the workflow graph from recipes due to the lack of large-scale labeled datasets. Moreover, they fail to utilize the cooking images, which constitute an important part of food recipes. In this paper, we build MM-ReS, the first large-scale dataset for cooking workflow construction, consisting of 9,850 recipes with human-labeled workflow graphs. Cooking steps …

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Compact Bilinear Augmented Query Structured Attention For Sport Highlights Classification, Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Qing Liu, Xiaojun Hu Oct 2020

Compact Bilinear Augmented Query Structured Attention For Sport Highlights Classification, Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Qing Liu, Xiaojun Hu

Research Collection School Of Computing and Information Systems

Understanding fine-grained activities, such as sport highlights, is a problem being overlooked and receives considerably less research attention. Potential reasons include absences of specific fine-grained action benchmark datasets, research preferences to general supercategorical activities classification, and challenges of large visual similarities between fine-grained actions. To tackle these, we collect and manually annotate two sport highlights datasets, i.e., Basketball8 & Soccer-10, for fine-grained action classification. Sample clips in the datasets are annotated with professional sub-categorical actions like “dunk”, “goalkeeping” and etc. We also propose a Compact Bilinear Augmented Query Structured Attention (CBA-QSA) module and stack it on top of general three-dimensional …

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Cross-Domain Cross-Modal Food Transfer, Bin Zhu, Chong-Wah Ngo, Jingjing Chen Oct 2020

Cross-Domain Cross-Modal Food Transfer, Bin Zhu, Chong-Wah Ngo, Jingjing Chen

Research Collection School Of Computing and Information Systems

The recent works in cross-modal image-to-recipe retrieval pave a new way to scale up food recognition. By learning the joint space between food images and recipes, food recognition is boiled down as a retrieval problem by evaluating the similarity of embedded features. The major drawback, nevertheless, is the difficulty in applying an already-trained model to recognize different cuisines of dishes unknown to the model. In general, model updating with new training examples, in the form of image-recipe pairs, is required to adapt a model to new cooking styles in a cuisine. Nevertheless, in practice, acquiring sufficient number of image-recipe pairs …

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Interpretable Embedding For Ad-Hoc Video Search, Jiaxin Wu, Chong-Wah Ngo Oct 2020

Interpretable Embedding For Ad-Hoc Video Search, Jiaxin Wu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Answering query with semantic concepts has long been the mainstream approach for video search. Until recently, its performance is surpassed by concept-free approach, which embeds queries in a joint space as videos. Nevertheless, the embedded features as well as search results are not interpretable, hindering subsequent steps in video browsing and query reformulation. This paper integrates feature embedding and concept interpretation into a neural network for unified dual-task learning. In this way, an embedding is associated with a list of semantic concepts as an interpretation of video content. This paper empirically demonstrates that, by using either the embedding features or …

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Person-Level Action Recognition In Complex Events Via Tsd-Tsm Networks, Yanbin Hao, Zi-Niu Liu, Hao Zhang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo Oct 2020

Person-Level Action Recognition In Complex Events Via Tsd-Tsm Networks, Yanbin Hao, Zi-Niu Liu, Hao Zhang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The task of person-level action recognition in complex events aims to densely detect pedestrians and individually predict their actions from surveillance videos. In this paper, we present a simple yet efficient pipeline for this task, referred to as TSD-TSM networks. Firstly, we adopt the TSD detector for the pedestrian localization on each single keyframe. Secondly, we generate the sequential ROIs for a person proposal by replicating the adjusted bounding box coordinates around the keyframe. Particularly, we propose to conduct straddling expansion and region squaring on the original bounding box of a person proposal to widen the potential space of motion …

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Co-Design And Evaluation Of An Intelligent Decision Support System For Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Badia Oct 2020

Co-Design And Evaluation Of An Intelligent Decision Support System For Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Badia

Research Collection School Of Computing and Information Systems

Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist's evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspectives with quantitative information on patient's exercise motions). As a result, we co-designed and developed an intelligent decision support system that automatically identifies salient features of assessment using reinforcement learning to assess the quality of motion and generate patient-specific analysis. We evaluated this system with …

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White-Box Fairness Testing Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, Dai Ting Oct 2020

White-Box Fairness Testing Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, Dai Ting

Research Collection School Of Computing and Information Systems

Although deep neural networks (DNNs) have demonstrated astonishing performance in many applications, there are still concerns on their dependability. One desirable property of DNN for applications with societal impact is fairness (i.e., non-discrimination). In this work, we propose a scalable approach for searching individual discriminatory instances of DNN. Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable than existing methods. Experimental results show that our approach explores the search space more effectively (9 times) and generates much more individual discriminatory instances (25 times) using much less time (half …

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Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn Oct 2020

Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn

Research Collection School Of Computing and Information Systems

Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware. …

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Efficient Ciphertext-Policy Attribute-Based Encryption With Blackbox Traceability, Shengmin Xu, Jiaming Yuan, Guowen Xu, Yingjiu Li, Ximeng Liu, Yinghui Zhang, Zuobin Yang Oct 2020

Efficient Ciphertext-Policy Attribute-Based Encryption With Blackbox Traceability, Shengmin Xu, Jiaming Yuan, Guowen Xu, Yingjiu Li, Ximeng Liu, Yinghui Zhang, Zuobin Yang

Research Collection School Of Computing and Information Systems

Traitor tracing scheme is a paradigm to classify the users who illegal use of their decryption keys in cryptosystems. In the ciphertext-policy attribute-based cryptosystem, the decryption key usually contains the users’ attributes, while the real identities are hidden. The decryption key with hidden identities enables malicious users to intentionally leak decryption keys or embed the decryption keys in the decryption device to gain illegal profits with a little risk of being discovered. To mitigate this problem, the concept of blackbox traceability in the ciphertext-policy attribute-based scheme was proposed to identify the malicious user via observing the I/O streams of the …

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Hierarchical Identity-Based Signature In Polynomial Rings, Zhichao Yang, Dung H. Duong, Willy Susilo, Guomin Yang, Chao Li, Rongmao Chen Oct 2020

Hierarchical Identity-Based Signature In Polynomial Rings, Zhichao Yang, Dung H. Duong, Willy Susilo, Guomin Yang, Chao Li, Rongmao Chen

Research Collection School Of Computing and Information Systems

Hierarchical identity-based signature (HIBS) plays a core role in a large community as it significantly reduces the workload of the root private key generator. To make HIBS still available and secure in post-quantum era, constructing lattice-based schemes is a promising option. In this paper, we present an efficient HIBS scheme in polynomial rings. Although there are many lattice-based signatures proposed in recent years, to the best of our knowledge, our HIBS scheme is the first ring-based construction. In the center of our construction are two new algorithms to extend lattice trapdoors to higher dimensions, which are non-trivial and of independent …

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Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua Oct 2020

Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose …

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Two-Stage Photograph Cartoonization Via Line Tracing, Simin Li, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He Oct 2020

Two-Stage Photograph Cartoonization Via Line Tracing, Simin Li, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He

Research Collection School Of Computing and Information Systems

Cartoon is highly abstracted with clear edges, which makes it unique from the other art forms. In this paper, we focus on the essential cartoon factors of abstraction and edges, aiming to cartoonize real-world photographs like an artist. To this end, we propose a two-stage network, each stage explicitly targets at producing abstracted shading and crisp edges respectively. In the first abstraction stage, we propose a novel unsupervised bilateral flattening loss, which allows generating high-quality smoothing results in a label-free manner. Together with two other semantic-aware losses, the abstraction stage imposes different forms of regularization for creating cartoon-like flattened images. …

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Activity River: Visualizing Planned And Logged Personal Activities For Reflection, Bon Adriel Aseniero, Charles Perin, Wesley Willett, Anthony Tang, Sheelagh Carpendale Oct 2020

Activity River: Visualizing Planned And Logged Personal Activities For Reflection, Bon Adriel Aseniero, Charles Perin, Wesley Willett, Anthony Tang, Sheelagh Carpendale

Research Collection School Of Computing and Information Systems

We present Activity River, a personal visualization tool which enables individuals to plan, log, and reflect on their self-defined activities. We are interested in supporting this type of reflective practice as prior work has shown that reflection can help people plan and manage their time effectively. Hence, we designed Activity River based on five design goals (visualize historical and contextual data, facilitate comparison of goals and achievements, engage viewers with delightful visuals, support authorship, and enable flexible planning and logging) which we distilled from the Information Visualization and Human-Computer Interaction literature. To explore our approach's strengths and limitations, we conducted …

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The Virtual Reality Questionnaire Toolkit, Martin Feick, Niko Kleer, Anthony Tang, Anthonio Kruger Oct 2020

The Virtual Reality Questionnaire Toolkit, Martin Feick, Niko Kleer, Anthony Tang, Anthonio Kruger

Research Collection School Of Computing and Information Systems

In this work, we present the VRQuestionnaire Toolkit, which enables the research community to easily collect subjective measures within virtual reality (VR). We contribute a highly customizable and reusable open-source toolkit which can be integrated in existing VR projects rapidly. The toolkit comes with a pre-installed set of standard questionnaires such as NASA TLX, SSQ and SUS Presence questionnaire. Our system aims to lower the entry barrier to use questionnaires in VR and to significantly reduce development time and cost needed to run pre-, in between- and post-study questionnaires.

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Zoomwalls: Dynamic Walls That Simulate Haptic Infrastructure For Room-Scale Vr World, Yan Yixian, Kazuki Takashima, Anthony Tang, Takayuki Tanno, Kazuyuki Fujita, Yoshifumi Kitamura Oct 2020

Zoomwalls: Dynamic Walls That Simulate Haptic Infrastructure For Room-Scale Vr World, Yan Yixian, Kazuki Takashima, Anthony Tang, Takayuki Tanno, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

We focus on the problem of simulating the haptic infrastructure of a virtual environment (i.e. walls, doors). Our approach relies on multiple ZoomWalls---autonomous robotic encounter-type haptic wall-shaped props---that coordinate to provide haptic feedback for room-scale virtual reality. Based on a user's movement through the physical space, ZoomWall props are coordinated through a predict-and-dispatch architecture to provide just-in-time haptic feedback for objects the user is about to touch. To refine our system, we conducted simulation studies of different prediction algorithms, which helped us to refine our algorithmic approach to realize the physical ZoomWall prototype. Finally, we evaluated our system through a …

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Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan Oct 2020

Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan

Research Collection School Of Computing and Information Systems

This Innovative Practice Full Paper discusses our course design on software architecture to meet the learning expectations of two groups of software engineers. Software engineers with working experiences frequently find themselves the need to upskill in their lifelong learning journey. Their learning expectations are shaped not just by their need to know but also other learning characteristics such as their working experiences. In many cases, we design courses based on the required learning outcomes and assessment criteria. In this paper, we wish to find out whether our course design on software architecture has met the learning expectations of our students …

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Experience Report On The Use Of Technology To Manage Capstone Course Projects, Benjamin Gan, Eng Lieh Ouh Oct 2020

Experience Report On The Use Of Technology To Manage Capstone Course Projects, Benjamin Gan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This full paper presents an experience report describing lessons learnt from innovative practice use of technologies in an undergraduate computing capstone course. At our school, around fifty-five teams comprising of around 300 students take this course every year. With fifty-five teams, we needed a system to schedule presentations; improve communications; collaborate between stakeholders; share knowledge; monitor progress; team up students; match students to projects; improve grading process; showcase posters; and track improvements using analytics. The Learning Management Systems (LMS) is great to manage course content and grade submission. On the other hand, students are required to conduct agile sprint reviews …

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Sentiment Analysis For Software Engineering: How Far Can Pre-Trained Transformer Models Go?, Ting Zhang, Bowen Xu, Thung Ferdian, Stefanus Agus Haryono, David Lo, Lingxiao Jiang Oct 2020

Sentiment Analysis For Software Engineering: How Far Can Pre-Trained Transformer Models Go?, Ting Zhang, Bowen Xu, Thung Ferdian, Stefanus Agus Haryono, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Extensive research has been conducted on sentiment analysis for software engineering (SA4SE). Researchers have invested much effort in developing customized tools (e.g., SentiStrength-SE, SentiCR) to classify the sentiment polarity for Software Engineering (SE) specific contents (e.g., discussions in Stack Overflow and code review comments). Even so, there is still much room for improvement. Recently, pre-trained Transformer-based models (e.g., BERT, XLNet) have brought considerable breakthroughs in the field of natural language processing (NLP). In this work, we conducted a systematic evaluation of five existing SA4SE tools and variants of four state-of-the-art pre-trained Transformer-based models on six SE datasets. Our work is …

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Crossasr: Efficient Differential Testing Of Automatic Speech Recognition Via Text-To-Speech, Muhammad Hilmi Asyrofi, Thung Ferdian, David Lo, Lingxiao Jiang Oct 2020

Crossasr: Efficient Differential Testing Of Automatic Speech Recognition Via Text-To-Speech, Muhammad Hilmi Asyrofi, Thung Ferdian, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Automatic speech recognition (ASR) systems are ubiquitous parts of modern life. It can be found in our smartphones, desktops, and smart home systems. To ensure its correctness in recognizing speeches, ASR needs to be tested. Testing ASR requires test cases in the form of audio files and their transcribed texts. Building these test cases manually, however, is tedious and time-consuming.To deal with the aforementioned challenge, in this work, we propose CrossASR, an approach that capitalizes the existing Text-To-Speech (TTS) systems to automatically generate test cases for ASR systems. CrossASR is a differential testing solution that compares outputs of multiple ASR …

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Using Student Perceptions To Design Smart Class Participation Tools: A Technology Framework, Swapna Gottipati, Shankararaman, Venky, Mark Wei Jie Ng Oct 2020

Using Student Perceptions To Design Smart Class Participation Tools: A Technology Framework, Swapna Gottipati, Shankararaman, Venky, Mark Wei Jie Ng

Research Collection School Of Computing and Information Systems

Our research full paper studies the perceptions of students and proposes technology design framework for smart class participation tools. Participation in classroom discussions has been observed to improve student comprehension and performance. In our contemporary tertiary educational context, student participation is characterised in mainly two forms; inclass discussions and online forums. Both forms of participation generate voluminous amounts of knowledge. However, the present difficulty in capturing and analysing these forms of participation leads to loss of knowledge and insights, which otherwise could be very useful. This study aims to analyse ways to improve data capture as well as data analysis …

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Automated Discussion Analysis - Framework For Knowledge Analysis From Class Discussions, Swapna Gottipati, Venky Shankararaman, Mallikan Gokarn Nitin Oct 2020

Automated Discussion Analysis - Framework For Knowledge Analysis From Class Discussions, Swapna Gottipati, Venky Shankararaman, Mallikan Gokarn Nitin

Research Collection School Of Computing and Information Systems

This research full paper, describes knowledge management of class discussions using an analytics based framework. Discussions, either live classroom or through online forums, when used as a teaching method can help stimulate critical thinking. It allows the teacher to explore in-depth the key concepts covered in the course, motivates students to articulate their ideas clearly and challenge the students to think more deeply. Analysing the discussions helps instructors gain better insights on the personal and collaborative learning behaviour of students. However, knowledge from in-class discussions and online forums is not effectively captured and mined due to lack of appropriate automated …

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Opinion-Aware Answer Generation For Review-Driven Question Answering In E-Commerce, Yang Deng, Wenxuan Zhang, Wai Lam Oct 2020

Opinion-Aware Answer Generation For Review-Driven Question Answering In E-Commerce, Yang Deng, Wenxuan Zhang, Wai Lam

Research Collection School Of Computing and Information Systems

Product-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product reviews. Nevertheless, the rich information about personal opinions in product reviews, which is essential to answer those product-specific questions, is underutilized in current generation-based review-driven QA studies. There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, …

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We Mind Your Well-Being: Preventing Depression In Uncertain Social Networks By Sequential Interventions, Aye Phye Phye Aung, Xinrun Wang, Bo An, Xiaoli Li Oct 2020

We Mind Your Well-Being: Preventing Depression In Uncertain Social Networks By Sequential Interventions, Aye Phye Phye Aung, Xinrun Wang, Bo An, Xiaoli Li

Research Collection School Of Computing and Information Systems

Mental health has become a major concern according to WHO who estimates that more than 350 million people worldwide are affected by depression. Studies have shown that interventions and social support can reduce stress and depression. However, counselling centers do not have enough resources to provide counselling and social support to all the participants in their interest. This paper helps social support organizations (e.g., university counselling centers) sequentially select the participants for interventions. Unfortunately, previous works do not consider emotion propagation from other neighbours of the influencees and initial uncertainties of mental states and influence. Moreover, they fail to scale …

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Presentation Of Computer Security Risk Information: Impact Of Framing And Base Size, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Richard Hall, Maggie Cheng Oct 2020

Presentation Of Computer Security Risk Information: Impact Of Framing And Base Size, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Richard Hall, Maggie Cheng

Research Collection School Of Computing and Information Systems

This research explores how the presentation of computer security risks impacts users’ risk perceptions and behavior. It draws on Prospect Theory to generate hypotheses related to users’ decision-making in the computer security context. A 2 × 3 mixed factorial experimental design (N = 178) was carried out and the results show that framing and base size of information on computer security risks influence users’ perceived risk and risk-taking behavior. More specifically, negative framing and large base size increase users’ perceived risk and reduce users’ risk-taking behavior. The findings from this research suggest that using negative framing and large base size …

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