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Articles 2131 - 2160 of 6720

Full-Text Articles in Physical Sciences and Mathematics

An Essential Applied Statistical Analysis Course Using Rstudio With Project-Based Learning For Data Science, Aldy Gunawan, Michelle L. F. Cheong, Johnson Poh Dec 2018

An Essential Applied Statistical Analysis Course Using Rstudio With Project-Based Learning For Data Science, Aldy Gunawan, Michelle L. F. Cheong, Johnson Poh

Research Collection School Of Computing and Information Systems

This paper presents a newpostgraduate level course, named Applied Statistical Analysis with R. Wepresent the course structure, teaching methodology including the assessmentframework and student feedback. The course covers the basic concepts ofstatistics, the knowledge of applying statistical theory in analyzing real dataand the skill of developing statistical applications with R programminglanguage. The first half of each lesson is dedicated to teaching students thestatistical concepts while the second half focuses on the practical aspects ofimplementing the concepts within the RStudio console. The Project-BasedLearning (PBL) approach is adopted to encourage students to apply the knowledgegained to solve real world problems, answer complex …

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Applying Design Thinking To Student Outreach Projects: Experiences From An Information Systems School, Swapna Gottipati, Venky Shankararaman, Alan Megargel Dec 2018

Applying Design Thinking To Student Outreach Projects: Experiences From An Information Systems School, Swapna Gottipati, Venky Shankararaman, Alan Megargel

Research Collection School Of Computing and Information Systems

As countries turn into Smart Nations, Infocom Technology plays a key role in enhancing their competitiveness through high skilled workforces. Reaching to younger generations and attracting them to computing programs such as Information Systems (IS) and Computer Science (CS) is a key challenge faced by universities. Many high quality students from junior colleges either don’t choose IS programs or choose IS programs as their last option during the application process. A School of Information Systems (SIS) from a large metropolitan university decided to implement an innovative outreach program to attract high quality high school aka Junior College (JC) students. JC …

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Better Inpatient Health Quality At Lower Cost: Should I Participate In The Online Healthcare Community First?, Kai Luo, Qiu-Hong Wang, Hock Hai Teo, Xi Chen Dec 2018

Better Inpatient Health Quality At Lower Cost: Should I Participate In The Online Healthcare Community First?, Kai Luo, Qiu-Hong Wang, Hock Hai Teo, Xi Chen

Research Collection School Of Computing and Information Systems

As policy makers across the globe look to health information technology (HIT) as a meansof improving the efficiency of the healthcare systems, it has sparked significant interestin understanding how HIT might help achieve that. While researchers have examined anddocumented the efficiency-improving effect of various institution HITs (e.g., electronicclinic pathways and telemedicine), the impacts of consumer HITs such as onlinehealthcare communities have been generally overlooked. Utilizing two unique datasetsfrom both an online healthcare community and a general hospital, we study the impactof online healthcare community on offline inpatient care efficiency. Through rigorousanalysis, we find that communications between physicians and patients on …

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Utilizing Computational Trust To Identify Rumor Spreaders On Twitter, Bhavtosh Rath, Wei Gao, Jing Ma, Jaideep Srivastava Dec 2018

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 …

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Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang Dec 2018

Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang

Research Collection School Of Computing and Information Systems

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep …

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On Learning Psycholinguistics Tools For English-Based Creole Languages Using Social Media Data, Pei-Chi Lo, Ee-Peng Lim Dec 2018

On Learning Psycholinguistics Tools For English-Based Creole Languages Using Social Media Data, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The Linguistic Inquiry and Word Count (LIWC) tool is a psycholinguistics tool that has been widely used in both psychology and sociology research, and the LIWC scores derived from user-generated content are known to be good features for personality prediction [1], [2]. LIWC, however, is language specific as it relies on counting the percentage of predefined dictionary words occurring in the content. For content written in English Creoles which are languages based on English, the original English LIWC may not perform optimally due to its lack of words which are only used in the English Creoles. In this paper, we …

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Deep Unsupervised Pixelization, Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, Tien-Tsin Wong Dec 2018

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 …

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Active Matting, Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin, Rynson Lau Dec 2018

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 …

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Cross Euclidean-To-Riemannian Metric Learning With Application To Face Recognition From Video, Zhiwu Huang, R. Wang, S. Shan, Gool L Van Dec 2018

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 …

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Integrating Node Embeddings And Biological Annotations For Genes To Predict Disease-Gene Associations, Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu, Xiao-Li Li Dec 2018

Integrating Node Embeddings And Biological Annotations For Genes To Predict Disease-Gene Associations, Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Background: Predicting disease causative genes (or simply, disease genes) has played critical roles in understandingthe genetic basis of human diseases and further providing disease treatment guidelines. While various computationalmethods have been proposed for disease gene prediction, with the recent increasing availability of biologicalinformation for genes, it is highly motivated to leverage these valuable data sources and extract useful information foraccurately predicting disease genes. Results: We present an integrative framework called N2VKO to predict disease genes. Firstly, we learn the nodeembeddings from protein-protein interaction (PPI) network for genes by adapting the well-known representationlearning method node2vec. Secondly, we combine the learned node …

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A Simple Proximal Stochastic Gradient Method For Nonsmooth Nonconvex Optimization, Zhize Li, Jian Li Dec 2018

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 …

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Using Smart Card Data To Model Commuters’ Responses Upon Unexpected Train Delays, Xiancai Tian, Baihua Zheng Dec 2018

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 …

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An Economic Analysis Of Incentivized Positive Reviews, Jianqing Chen, Zhiling Guo, Jian Huang Dec 2018

An Economic Analysis Of Incentivized Positive Reviews, Jianqing Chen, Zhiling Guo, Jian Huang

Research Collection School Of Computing and Information Systems

It becomes increasingly popular that some large online retailers such as Amazon open their platforms to allow third-party retail competitors to sell on their own platforms. We develop an analytical model to examine this retailer marketplace model and its business impact. We assume that a leading retailer has both valuation advantage that may come from its reputation and information advantage that may come from its brand awareness. We find that the availability of relatively low-cost advertising through social media or search engine can effectively reduce the leading retailer's information advantage, and thus be an important driving force for its strategic …

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A Cloud-Based Data Gathering And Processing System For Intelligent Demand Forecasting, Colin K. L. Tay, Kyong Jin Shim Dec 2018

A Cloud-Based Data Gathering And Processing System For Intelligent Demand Forecasting, Colin K. L. Tay, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Demand forecasting has been a challenging problem especially for products with short life cycles such as electronic goods and fashion items. Additionally, in the presence of limited past or historical data as well as the need for fast turnaround for forecast, producing timely and accurate demand forecast can be extremely challenging. In this study, we describe a cloud-based data gathering and processing system for intelligent demand forecasting.

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Data Mining Approach To The Detection Of Suicide In Social Media: A Case Study Of Singapore, Jane H. K. Seah, Kyong Jin Shim Dec 2018

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.

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Attention-Based Lstm-Cnns For Uncertainty Identification On Chinese Social Media Texts, Binyang Li, Kaiming Zhou, Wei Gao, Xu Han Han, Linna Zhou Dec 2018

Attention-Based Lstm-Cnns For Uncertainty Identification On Chinese Social Media Texts, Binyang Li, Kaiming Zhou, Wei Gao, Xu Han Han, Linna Zhou

Research Collection School Of Computing and Information Systems

Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the …

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Improving Strategic It Investment Decisions By Reducing Information Asymmetry, Thomas P. Stablein Nov 2018

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 …

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Enhancing The Design Of A Cybersecurity Risk Management Solution For Communities Of Trust, James E. Fulford Jr. Nov 2018

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 …

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Performance Indicators Analysis Inside A Call Center Using A Simulation Program, Ditila Ekmekçiu, Markela Muça, Adrian Naço Nov 2018

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 …

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Modelling Business And Management Systems Using Fuzzy Cognitive Maps: A Critical Overview, Peter P. Groumpos Nov 2018

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 …

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Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang Nov 2018

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 …

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Cross-Border Interbank Payments And Settlements: Emerging Opportunities For Digital Transformation, Yi Meng Lau, Et Al Nov 2018

Cross-Border Interbank Payments And Settlements: Emerging Opportunities For Digital Transformation, Yi Meng Lau, Et Al

Research Collection School Of Computing and Information Systems

The report “Cross-Border Interbank Payments and Settlements” is a cross-jurisdictional industry collaboration between Canada, Singapore and the United Kingdom to examine the existing challenges and frictions that arise when undertaking crossborder payments. This report explores proposals for new and more efficient models for processing cross-border transactions.

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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 Nov 2018

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 …

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Unsupervised User Identity Linkage Via Factoid Embedding, Wei Xie, Xin Mu, Roy Ka Wei Lee, Feida Zhu, Ee-Peng Lim Nov 2018

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 …

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Vpsearch: Achieving Verifiability For Privacy-Preserving Multi-Keyword Search Over Encrypted Cloud Data, Zhiguo Wan, Robert H. Deng Nov 2018

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 …

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Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang Nov 2018

Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang

Research Collection School Of Computing and Information Systems

Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good …

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Jobsense: A Data-Driven Career Knowledge Exploration Framework And System, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo Nov 2018

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 …

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Imaginary People Representing Real Numbers: Generating Personas From Online Social Media Data, Jisun An, Haewoon Kwak, Soongyo Jung, Joni Salminen, M. Admad, Bernard J. Jansen Nov 2018

Imaginary People Representing Real Numbers: Generating Personas From Online Social Media Data, Jisun An, Haewoon Kwak, Soongyo Jung, Joni Salminen, M. Admad, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. …

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Joint Representation Learning Of Cross-Lingual Words And Entities Via Attentive Distant Supervision, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong Nov 2018

Joint Representation Learning Of Cross-Lingual Words And Entities Via Attentive Distant Supervision, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong

Research Collection School Of Computing and Information Systems

Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and crosslingual attention to further reduce noises. We conducted a series of experiments on …

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Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan Nov 2018

Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent video captioning methods have made great progress by deep learning approaches with convolutional neural networks (CNN) and recurrent neural networks (RNN). While there are techniques that use memory networks for sentence decoding, few work has leveraged on the memory component to learn and generalize the temporal structure in video. In this paper, we propose a new method, namely Generalized Video Memory (GVM), utilizing a memory model for enhancing video description generation. Based on a class of self-organizing neural networks, GVM’s model is able to learn new video features incrementally. The learned generalized memory is further exploited to decode the …

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