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Articles 2011 - 2040 of 7258
Full-Text Articles in Computer Sciences
Automated Discussion Analysis - Framework For Knowledge Analysis From Class Discussions, Swapna Gottipati, Venky Shankararaman, Mallikan Gokarn Nitin
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 …
Opinion-Aware Answer Generation For Review-Driven Question Answering In E-Commerce, Yang Deng, Wenxuan Zhang, Wai Lam
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, …
Presentation Of Computer Security Risk Information: Impact Of Framing And Base Size, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Richard Hall, Maggie Cheng
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 …
Meta-Rcnn: Meta Learning For Few-Shot Object Detection, Xiongwei Wu, Doyen Sahoo, Steven Hoi
Meta-Rcnn: Meta Learning For Few-Shot Object Detection, Xiongwei Wu, Doyen Sahoo, Steven Hoi
Research Collection School Of Computing and Information Systems
Despite significant advances in deep learning based object detection in recent years, training effective detectors in a small data regime remains an open challenge. This is very important since labelling training data for object detection is often very expensive and time-consuming. In this paper, we investigate the problem of few-shot object detection, where a detector has access to only limited amounts of annotated data. Based on the meta-learning principle, we propose a new meta-learning framework for object detection named "Meta-RCNN", which learns the ability to perform few-shot detection via meta-learning. Specifically, Meta-RCNN learns an object detector in an episodic learning …
Chapter 3: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Chapter 3: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Open Educational Resources
No abstract provided.
Chapter 2: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Chapter 2: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Open Educational Resources
No abstract provided.
Chapter 5: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Chapter 5: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Open Educational Resources
No abstract provided.
Chapter 1: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Chapter 1: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana
Open Educational Resources
No abstract provided.
Annapurna: An Automated Smartwatch-Based Eating Detection And Food Journaling System, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Annapurna: An Automated Smartwatch-Based Eating Detection And Food Journaling System, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Maintaining a food journal can allow an individual to monitor eating habits, including unhealthy eating sessions, food items causing severe reactions, or portion size related information. However, manually maintaining a food journal can be burdensome. In this paper, we explore the vision of a pervasive, automated, completely unobtrusive, food journaling system using a commodity smartwatch. We present a prototype system — Annapurna— which is composed of three key components: (a) a smartwatch-based gesture recognizer that can robustly identify eating-specific gestures occurring anywhere, (b) a smartwatch-based image captor that obtains a small set of relevant images (containing views of the food …
Tpr: Text-Aware Preference Ranking For Recommender Systems, Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang, Ee-Peng Lim
Tpr: Text-Aware Preference Ranking For Recommender Systems, Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Textual data is common and informative auxiliary information for recommender systems. Most prior art utilizes text for rating prediction, but rare work connects it to top-N recommendation. Moreover, although advanced recommendation models capable of incorporating auxiliary information have been developed, none of these are specifically designed to model textual information, yielding a limited usage scenario for typical user-to-item recommendation. In this work, we present a framework of text-aware preference ranking (TPR) for top-N recommendation, in which we comprehensively model the joint association of user-item interaction and relations between items and associated text. Using the TPR framework, we construct a joint …
Towards Locality-Aware Meta-Learning Of Tail Node Embeddings On Networks, Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang, Steven C. H. Hoi
Towards Locality-Aware Meta-Learning Of Tail Node Embeddings On Networks, Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Network embedding is an active research area due to the prevalence of network-structured data. While the state of the art often learns high-quality embedding vectors for high-degree nodes with abundant structural connectivity, the quality of the embedding vectors for low-degree or tail nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embedding. In this paper, we formulate the goal of learning tail node embeddings as a few-shot regression problem, given the few links on each tail node. In particular, since each node resides …
Gym Usage Behavior & Desired Digital Interventions: An Empirical Study, Meeralakshmi Radhakrishnan, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Gym Usage Behavior & Desired Digital Interventions: An Empirical Study, Meeralakshmi Radhakrishnan, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Understanding individual’s exercise motives, participation patterns in a gym and reasons for dropout are essential for designing strategies to help gym-goers with long-term exercise adherence. In this work, we derive insights on various exercise-related behaviors of gymgoers, including evidence of a significant number of individuals exhibiting early dropout and also describing their attitudes towards digital technologies for sustained gym participation. By utilizing gym visitation data logs of 6513 individuals over a longitudinal period of 16 months in a campus gym, we show the retention and dropout rates of gym-goers. Our data indicates that 32% of the people quit their gym …
The Future Of Work Now: Ai-Driven Transaction Surveillance At Dbs Bank, Thomas H. Davenport, Steven M. Miller
The Future Of Work Now: Ai-Driven Transaction Surveillance At Dbs Bank, Thomas H. Davenport, Steven M. Miller
Research Collection School Of Computing and Information Systems
One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I co-authored the story.
The Future Of Work Now: Automl At 84.51°And Kroger, Thomas H. Davenport, Steven M. Miller
The Future Of Work Now: Automl At 84.51°And Kroger, Thomas H. Davenport, Steven M. Miller
Research Collection School Of Computing and Information Systems
One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon.
Foodbot: A Goal-Oriented Just-In-Time Healthy Eating Interventions Chatbot, Philips Kokoh Prasetyo, Palakorn Achananuparp, Ee-Peng Lim
Foodbot: A Goal-Oriented Just-In-Time Healthy Eating Interventions Chatbot, Philips Kokoh Prasetyo, Palakorn Achananuparp, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Recent research has identified a few design flaws in popular mobile health (mHealth) applications for promoting healthy eating lifestyle, such as mobile food journals. These include tediousness of manual food logging, inadequate food database coverage, and a lack of healthy dietary goal setting. To address these issues, we present Foodbot, a chatbot-based mHealth application for goal-oriented just-in-time (JIT) healthy eating interventions. Powered by a large-scale food knowledge graph, Foodbot utilizes automatic speech recognition and mobile messaging interface to record food intake. Moreover, Foodbot allows users to set goals and guides their behavior toward the goals via JIT notification prompts, interactive …
Cover Song Identification - A Novel Stem-Based Approach To Improve Song-To-Song Similarity Measurements, Lavonnia Newman, Dhyan Shah, Chandler Vaughn, Faizan Javed
Cover Song Identification - A Novel Stem-Based Approach To Improve Song-To-Song Similarity Measurements, Lavonnia Newman, Dhyan Shah, Chandler Vaughn, Faizan Javed
SMU Data Science Review
Music is incorporated into our daily lives whether intentional or unintentional. It evokes responses and behavior so much so there is an entire study dedicated to the psychology of music. Music creates the mood for dancing, exercising, creative thought or even relaxation. It is a powerful tool that can be used in various venues and through advertisements to influence and guide human reactions. Music is also often "borrowed" in the industry today. The practices of sampling and remixing music in the digital age have made cover song identification an active area of research. While most of this research is focused …
A Novel Low-Power Synchronous Preamble Data Line Chip Design For Oscillator Control Interface, Shih-Lun Chen, Tsun-Kuang Chi, Min-Chun Tuan, Chiung-An Chen, Liang-Hung Wang, Wei-Yuan Chiang, Ming-Yi Lin, Patricia Angela R. Abu
A Novel Low-Power Synchronous Preamble Data Line Chip Design For Oscillator Control Interface, Shih-Lun Chen, Tsun-Kuang Chi, Min-Chun Tuan, Chiung-An Chen, Liang-Hung Wang, Wei-Yuan Chiang, Ming-Yi Lin, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
In this paper, a novel low-power synchronous preamble data line protocol chip design for serial communication is proposed. The serial communication only uses two wires, chip select (CS) and secure digital (SD), to transmit and receive data between two devices. The proposed protocol aims to use a fewer number of wires for the interface, therefore reducing the complexity as well as the area of the chip design. Moreover, it increases the efficiency through a synchronous serial communication-controlled oscillator. The low-power synchronous preamble data line protocol design was successfully verified using a field-programmable gate array (FPGA) as a master device and …
Low Cost Aip Design In 5g Flexible Antenna Phase Array System Application, Wei-Shin Tung, Wei-Yuan Chiang, Chih-Kai Liu, Chiung-An Chen, Pei-Zong Rao, Patricia Angela R. Abu, Wan-Ming Chen, Faisal Asadi, Shih-Lun Chen
Low Cost Aip Design In 5g Flexible Antenna Phase Array System Application, Wei-Shin Tung, Wei-Yuan Chiang, Chih-Kai Liu, Chiung-An Chen, Pei-Zong Rao, Patricia Angela R. Abu, Wan-Ming Chen, Faisal Asadi, Shih-Lun Chen
Department of Information Systems & Computer Science Faculty Publications
In this paper, a low cost 28 GHz Antenna-in-Package (AIP) for a 5G communication system is designed and investigated. The antenna is implemented on a low-cost FR4 substrate with a phase shift control integrated circuit, AnokiWave phasor integrated circuit (IC). The unit cell where the array antenna and IC are integrated in the same plate constructs a flexible phase array system. Using the AIP unit cell, the desired antenna array can be created, such as 2 × 8, 8 × 8 or 2 × 64 arrays. The study design proposed in this study is a 2 × 2 unit cell …
An Enhancement Of Age And Gender Classification Accuracy With Hybrid Handcrafted And Deep Features Using Hierarchical Extreme Learning Machine, Mohammad Javidan Darugar
An Enhancement Of Age And Gender Classification Accuracy With Hybrid Handcrafted And Deep Features Using Hierarchical Extreme Learning Machine, Mohammad Javidan Darugar
Student Works (2020-2029)
Age and gender classification are some of the essential algorithms that have many use cases in our everyday life. For example, in robotics, field robots can interact with a human base on their gender in data analysis, to have statistics about age and gender of audiences in social events, YouTube video analysis, and many other applications. In this research, we have addressed limitations in deep neural networks, which by overcoming this limitation, we can gain better accuracy and performance. Our study has several other possible applications which are not limited only to age and gender classification. This dissertation is about …
Machine Learning Applications For Drug Repurposing, Hansaim Lim
Machine Learning Applications For Drug Repurposing, Hansaim Lim
Dissertations, Theses, and Capstone Projects
The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …
An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
Research Collection School Of Computing and Information Systems
Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory …
Research Directions For Sharing Economy Issues, Robert J. Kauffman, Maurizio Naldi
Research Directions For Sharing Economy Issues, Robert J. Kauffman, Maurizio Naldi
Research Collection School Of Computing and Information Systems
The sharing economy proposes a new approach to designing and delivering products and services, that aims at avoiding waste, improving efficiency, and favoring bottom-up change. In this research commentary, we survey the current state of things and propose some directions for research. We first describe the industries, products, and services currently representing the sharing paradigm, the technology platforms enabling it, the business models driving it, and the regulatory issues. We envisage that promising areas of research should include: (1) devising more efficient algorithms; (2) considering ecological and prosocial objective functions; (3) dealing with regulatory issues; (4) expanding the span of …
Deepstyle: User Style Embedding For Authorship Attribution Of Short Texts, Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang, Ee-Peng Lim
Deepstyle: User Style Embedding For Authorship Attribution Of Short Texts, Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have shown that deep learning methods could achieve significant accuracy improvement for the AA task. Nevertheless, most of these proposed methods represent user posts using a single type of features (e.g., word bi-grams) and adopt a text classification approach to address the task. Furthermore, these methods offer very limited explainability of the AA results. In this paper, we address these limitations by proposing DeepStyle, a novel embedding-based framework that learns the representations …
Weakly Paired Multi-Domain Image Translation, M.Y. Zhang, Zhiwu Huang, D.P. Paudel, J. Thoma, Gool L. Van
Weakly Paired Multi-Domain Image Translation, M.Y. Zhang, Zhiwu Huang, D.P. Paudel, J. Thoma, Gool L. Van
Research Collection School Of Computing and Information Systems
In this paper, we aim at studying the new problem of weakly paired multi-domain image translation. To this end, we collect a dataset that contains weakly paired images from multiple domains. Two images are considered to be weakly paired if they are captured from nearby locations and share an overlapping field of view. These images are possibly captured by two asynchronous cameras—often resulting in images from separate domains, e.g. summer and winter. Major motivations for using weakly paired images are: (i) performance improvement towards that of paired data; (ii) cheap labels and abundant data availability. For the first time in …
Social Influence Attentive Neural Network For Friend-Enhanced Recommendation, Yuanfu Lu, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang, Leyu Lin
Social Influence Attentive Neural Network For Friend-Enhanced Recommendation, Yuanfu Lu, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang, Leyu Lin
Research Collection School Of Computing and Information Systems
With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called FriendEnhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change the recommendation paradigm, making users to pay more attention to enhanced social factors. In this paper, we first formulate the FER problem, and propose a novel Social Influence Attentive Neural network (SIAN) solution. In order to …
Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation, Yugang Ji, Mingyang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia, Chuan Shi
Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation, Yugang Ji, Mingyang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia, Chuan Shi
Research Collection School Of Computing and Information Systems
In the scenario of next-item recommendation, previous methods attempt to model user preferences by capturing the evolution of sequential interactions. However, their sequential expression is often limited, without modeling complex dynamics that short-term demands can often be influenced by long-term habits. Moreover, few of them take into account the heterogeneous types of interaction between users and items. In this paper, we model such complex data as a Temporal Heterogeneous Interaction Graph (THIG) and learn both user and item embeddings on THIGs to address next-item recommendation. The main challenges involve two aspects: the complex dynamics and rich heterogeneity of interactions. We …
Time-Warped Sparse Non-Negative Factorization For Functional Data Analysis, Chen Zhang, Steven C. H. Hoi, Fugee Tsung
Time-Warped Sparse Non-Negative Factorization For Functional Data Analysis, Chen Zhang, Steven C. H. Hoi, Fugee Tsung
Research Collection School Of Computing and Information Systems
This article proposes a novel time-warped sparse non-negative factorization method for functional data analysis. The proposed method on the one hand guarantees the extracted basis functions and their coefficients to be positive and interpretable, and on the other hand is able to handle weakly correlated functions with different features. Furthermore, the method incorporates time warping into factorization and hence allows the extracted basis functions of different samples to have temporal deformations. An efficient framework of estimation algorithms is proposed based on a greedy variable selection approach. Numerical studies together with case studies on real-world data demonstrate the efficacy and applicability …
Valid Time Rdf, Hsien-Tseng Wang
Valid Time Rdf, Hsien-Tseng Wang
Dissertations, Theses, and Capstone Projects
The Semantic Web aims at building a foundation of semantic-based data models and languages for not only manipulating data and knowledge, but also supporting decision making by machines. Naturally, time-varying data and knowledge are required in Semantic Web applications to incorporate time and further reason about it. However, the original specifications of Resource Description Framework (RDF) and Web Ontology Language (OWL) do not include constructs for handling time-varying data and knowledge. For simplicity, RDF model is confined to binary predicates, hence some form of reification is needed to represent higher-arity predicates. To this date, there are many proposals extending RDF …
London Heathrow Airport Uses Real-Time Analytics For Improving Operations, Xiaojia Guo, Yael Grushka-Cockayne, Bert De Reyck
London Heathrow Airport Uses Real-Time Analytics For Improving Operations, Xiaojia Guo, Yael Grushka-Cockayne, Bert De Reyck
Research Collection Lee Kong Chian School Of Business
Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. …
The Gap Of Semantic Parsing: A Survey On Automatic Math Word Problem Solvers, Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen
The Gap Of Semantic Parsing: A Survey On Automatic Math Word Problem Solvers, Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen
Research Collection School Of Computing and Information Systems
Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics. Despite the long history dated back to the 1960s, MWPs have regained intensive attention in the past few years with the advancement of Artificial Intelligence (AI). Solving MWPs successfully is considered as a milestone towards general AI. Many systems have claimed promising results in self-crafted and small-scale datasets. However, when applied on large and diverse datasets, none of the proposed methods in the literature achieves high precision, revealing that current MWP solvers still have much room for improvement. This motivated …