Utilizing Computational Trust To Identify Rumor Spreaders On Twitter,
2018
Singapore Management University
Utilizing Computational Trust To Identify Rumor Spreaders On Twitter, Bhavtosh Rath, Wei Gao, Jing Ma, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
Ubiquitous use of social media such as microblogging platforms opens unprecedented chances for false information to diffuse online. Facing the challenges in such a so-called “post-fact” era, it is very important for intelligent systems to not only check the veracity of information but also verify the authenticity of the users who spread the information, especially in time-critical situations such as real-world emergencies, where urgent measures have to be taken for stopping the spread of fake information. In this work, we propose a novel machine-learning-based approach for automatic identification of the users who spread rumorous information on Twitter by leveraging computational …
Using Smart Card Data To Model Commuters’ Responses Upon Unexpected Train Delays,
2018
Singapore Management University
Using Smart Card Data To Model Commuters’ Responses Upon Unexpected Train Delays, Xiancai Tian, Baihua Zheng
Research Collection School Of Computing and Information Systems
The mass rapid transit (MRT) network is playing an increasingly important role in Singapore's transit network, thanks to its advantages of higher capacity and faster speed. Unfortunately, due to aging infrastructure, increasing demand, and other reasons like adverse weather condition, commuters in Singapore recently have been facing increasing unexpected train delays (UTDs), which has become a source of frustration for both commuters and operators. Most, if not all, existing works on delay management do not consider commuters' behavior. We dedicate this paper to the study of commuters' behavior during UTDs. We adopt a data-driven approach to analyzing the six-month' real …
Data Mining Approach To The Detection Of Suicide In Social Media: A Case Study Of Singapore,
2018
Singapore Management University
Data Mining Approach To The Detection Of Suicide In Social Media: A Case Study Of Singapore, Jane H. K. Seah, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In this research, we focus on the social phenomenon of suicide. Specifically, we perform social sensing on digital traces obtained from Reddit. We analyze the posts and comments in that are related to depression and suicide. We perform natural language processing to better understand different aspects of human life that relate to suicide.
Cross Euclidean-To-Riemannian Metric Learning With Application To Face Recognition From Video,
2018
Singapore Management University
Cross Euclidean-To-Riemannian Metric Learning With Application To Face Recognition From Video, Zhiwu Huang, R. Wang, S. Shan, Gool L Van
Research Collection School Of Computing and Information Systems
Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from learning a discriminant Riemannian metric which encodes the non-linear geometry of the underlying Riemannian manifolds. In this paper, we propose a novel metric learning framework to learn a distance metric across a Euclidean space and a Riemannian manifold to fuse average appearance and pattern variation of faces within one video. The proposed metric learning framework can handle three typical tasks of video-based face recognition: Video-to-Still, Still-to-Video and Video-to-Video settings. To accomplish this new framework, by exploiting typical Riemannian …
Deep Unsupervised Pixelization,
2018
Singapore Management University
Deep Unsupervised Pixelization, Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, Tien-Tsin Wong
Research Collection School Of Computing and Information Systems
In this paper, we present a novel unsupervised learning method for pixelization. Due to the difficulty in creating pixel art, preparing the paired training data for supervised learning is impractical. Instead, we propose an unsupervised learning framework to circumvent such difficulty. We leverage the dual nature of the pixelization and depixelization, and model these two tasks in the same network in a bi-directional manner with the input itself as training supervision. These two tasks are modeled as a cascaded network which consists of three stages for different purposes. GridNet transfers the input image into multi-scale grid-structured images with different aliasing …
Active Matting,
2018
Singapore Management University
Active Matting, Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin, Rynson Lau
Research Collection School Of Computing and Information Systems
Image matting is an ill-posed problem. It requires a user input trimap or some strokes to obtain an alpha matte of the foreground object. A fine user input is essential to obtain a good result, which is either time consuming or suitable for experienced users who know where to place the strokes. In this paper, we explore the intrinsic relationship between the user input and the matting algorithm to address the problem of where and when the user should provide the input. Our aim is to discover the most informative sequence of regions for user input in order to produce …
A Simple Proximal Stochastic Gradient Method For Nonsmooth Nonconvex Optimization,
2018
Singapore Management University
A Simple Proximal Stochastic Gradient Method For Nonsmooth Nonconvex Optimization, Zhize Li, Jian Li
Research Collection School Of Computing and Information Systems
We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth finite-sum problems. In particular, the objective function is given by the summation of a differentiable (possibly nonconvex) component, together with a possibly non-differentiable but convex component. We propose a proximal stochastic gradient algorithm based on variance reduction, called ProxSVRG+. Our main contribution lies in the analysis of ProxSVRG+. It recovers several existing convergence results and improves/generalizes them (in terms of the number of stochastic gradient oracle calls and proximal oracle calls). In particular, ProxSVRG+ generalizes the best results given by the SCSG algorithm, recently proposed by [Lei et al., NIPS'17] for …
Living With Artificial Intelligence – Developing A Theory On Trust In Health Chatbots,
2018
Singapore Management University
Living With Artificial Intelligence – Developing A Theory On Trust In Health Chatbots, Weiyu Wang, Keng Siau
Research Collection School Of Computing and Information Systems
The current world is AI-filled and AI-fueled. Humans need to be able to live in harmony with AI. In this research, we aim to develop a theory on Trust between human and health chatbots. With the development of intelligent personal assistants, chatbots are becoming common and ubiquitous. Chatbots can behave as a conversational partner, complete information acquisition, and provide responses to inquiries. Chatbots have been widely used in the healthcare area, supporting physicians and assisting patients. The potential threats to privacy issues and the unpredictable performances of the chatbots hindered people’s trust and adoption of the new technology. This research-in-progress …
Improving Strategic It Investment Decisions By Reducing Information Asymmetry,
2018
University of South Florida
Improving Strategic It Investment Decisions By Reducing Information Asymmetry, Thomas P. Stablein
USF Tampa Graduate Theses and Dissertations
The unprecedented ubiquity with which technological advancements, such as blockchain, the Internet of things (IoT), big data, machine learning, and artificial intelligence (AI), are impacting the world has forced large organizations to rethink their information technology roadmaps. Their decisions about how they invest in technology have become more important. It is against this backdrop that companies must decide how much to invest in their aging technologies versus these new potentially transformational ones. A decision is only as good as the information available to the decision-makers when they make it. This research project seeks to understand the effects that information asymmetry …
Enhancing The Design Of A Cybersecurity Risk Management Solution For Communities Of Trust,
2018
University of South Florida
Enhancing The Design Of A Cybersecurity Risk Management Solution For Communities Of Trust, James E. Fulford Jr.
USF Tampa Graduate Theses and Dissertations
Research into cybersecurity risks and various methods of evaluating those threats has become an increasingly important area of academic and practitioner investigations. Of particular interest in this field is enhancing the designs and informing capabilities of cybersecurity risk management solutions for users who desire to understand how organizations are impacted when such risks are exploited. Many of the cybersecurity risk management solutions are extremely technical and require their users to have a commensurate level of technical acumen. In the situation evaluated during this research project, the founders of the company being researched had created a highly technical risk management solution …
Performance Indicators Analysis Inside A Call Center Using A Simulation Program,
2018
University of Tirana
Performance Indicators Analysis Inside A Call Center Using A Simulation Program, Ditila Ekmekçiu, Markela Muça, Adrian Naço
International Journal of Business and Technology
This paper deals with and shows the results of different performance indicators analyses made utilizing the help of Simulation and concentrated on dimensioning problems of handling calls capacity in a call center. The goal is to measure the reactivity of the call center’s performance to potential changes of critical variables. The literature related to the employment of this kind of instrument in call centers is reviewed, and the method that this problem is treated momentarily is precisely described. The technique used to obtain this paper’s goal implicated a simulation model using Arena Contact Center software that worked as a key …
Modelling Business And Management Systems Using Fuzzy Cognitive Maps: A Critical Overview,
2018
University of Patras
Modelling Business And Management Systems Using Fuzzy Cognitive Maps: A Critical Overview, Peter P. Groumpos
International Journal of Business and Technology
A critical overview of modelling Business and Management (B&M) Systems using Fuzzy Cognitive Maps is presented. A limited but illustrative number of specific applications of Fuzzy Cognitive Maps in diverse B&M systems, such as e business, performance assessment, decision making, human resources management, planning and investment decision making processes is provided and briefly analyzed. The limited survey is given in a table with statics of using FCMs in B&M systems during the last 15 years. The limited survey shows that the applications of Fuzzy Cognitive Maps to today’s Business and Management studies has been steadily increased especially during the last …
Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning,
2018
Singapore Management University
Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Research Collection School Of Computing and Information Systems
Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings,
2018
Singapore Management University
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou
Research Collection School Of Computing and Information Systems
Due to their aptitude in both accurate data processing and human comprehensible reasoning, neural fuzzy inference systems have been widely adopted in various application domains as decision support systems. Especially in real-world scenarios such as decision making in financial transactions, the human experts may be more interested in knowing the comprehensive reasons of certain advices provided by a decision support system in addition to how confident the system is on such advices. In this paper, we apply an integrated autonomous computational model termed genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS) to predict underpricing in initial public …
Vpsearch: Achieving Verifiability For Privacy-Preserving Multi-Keyword Search Over Encrypted Cloud Data,
2018
Shandong University
Vpsearch: Achieving Verifiability For Privacy-Preserving Multi-Keyword Search Over Encrypted Cloud Data, Zhiguo Wan, Robert H. Deng
Research Collection School Of Computing and Information Systems
Although cloud computing offers elastic computation and storage resources, it poses challenges on verifiability of computations and data privacy. In this work we investigate verifiability for privacy-preserving multi-keyword search over outsourced documents. As the cloud server may return incorrect results due to system faults or incentive to reduce computation cost, it is critical to offer verifiability of search results and privacy protection for outsourced data at the same time. To fulfill these requirements, we design aVerifiablePrivacy-preserving keywordSearch scheme, called VPSearch, by integrating an adapted homomorphic MAC technique with a privacy-preserving multi-keyword search scheme. The proposed scheme enables the client to …
River: A Real-Time Influence Monitoring System On Social Media Stream,
2018
Singapore Management University
River: A Real-Time Influence Monitoring System On Social Media Stream, Mo Sha, Yuchen Li, Yanhao Wang, Wentian Guo, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Social networks generate a massive amount of interaction data among users in the form of streams. To facilitate social network users to consume the continuously generated stream and identify preferred viral social contents, we present a real-time monitoring system called River to track a small set of influential social contents from high-speed streams in this demo. River has four novel features which distinguish itself from existing social monitoring systems: (1) River extracts a set of contents which collectively have the most significant influence coverage while reducing the influence overlaps; (2) River is topic-based and monitors the contents which are relevant …
Unsupervised User Identity Linkage Via Factoid Embedding,
2018
Singapore Management University
Unsupervised User Identity Linkage Via Factoid Embedding, Wei Xie, Xin Mu, Roy Ka Wei Lee, Feida Zhu, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
User identity linkage (UIL), the problem of matching user account across multiple online social networks (OSNs), is widely studied and important to many real-world applications. Most existing UIL solutions adopt a supervised or semisupervised approach which generally suffer from scarcity of labeled data. In this paper, we propose Factoid Embedding, a novel framework that adopts an unsupervised approach. It is designed to cope with different profile attributes, content types and network links of different OSNs. The key idea is that each piece of information about a user identity describes the real identity owner, and thus distinguishes the owner from other …
Jobsense: A Data-Driven Career Knowledge Exploration Framework And System,
2018
Singapore Management University
Jobsense: A Data-Driven Career Knowledge Exploration Framework And System, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
Today’s job market sees rapid changes due to technology and business model disruptions. To fully tap on one’s potential in career development, one has to acquire job and skill knowledge through working on different jobs. Another approach is to seek consultation with career coaches who are trained to offer career advice in various industry sectors. The above two approaches, nevertheless, suffer from several shortcomings. The on-the-job career development approach is highly inefficient for today’s fast changing job market. The latter career coach assisted approach could help to speed up knowledge acquisition but it relies on expertise of career coaches but …
Linky: Visualizing User Identity Linkage Results For Multiple Online Social Networks (Demo),
2018
Singapore Management University
Linky: Visualizing User Identity Linkage Results For Multiple Online Social Networks (Demo), Roy Ka-Wei Lee, Ming Shan Hee, Philips Kokoh Prasetyo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
User identity linkage across online social networks is an emerging research topic that has attracted attention in recent years. Many user identity linkage methods have been proposed so far and most of them utilize user profile, content and network information to determine if two social media accounts belong to the same person. In most cases, user identity linkage methods are evaluated by performing some prediction tasks with the results presented using some overall accuracy measures. However, the methods are rarely compared at the individual user level where a predicted matched (or linked) pair of user identities from different online social …
Improving Multi-Label Emotion Classification Via Sentiment Classification With Dual Attention Transfer Network,
2018
Singapore Management University
Improving Multi-Label Emotion Classification Via Sentiment Classification With Dual Attention Transfer Network, Jianfei Yu, Luis Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Research Collection School Of Computing and Information Systems
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.
