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Articles 1 - 30 of 338
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Gpu Accelerated On-The-Fly Reachability Checking, Zhimin Wu, Yang Liu, Jun Sun, Jianqi Shi, Shengchao Qin
Gpu Accelerated On-The-Fly Reachability Checking, Zhimin Wu, Yang Liu, Jun Sun, Jianqi Shi, Shengchao Qin
Research Collection School Of Computing and Information Systems
Model checking suffers from the infamous state space explosion problem. In this paper, we propose an approach, named GPURC, to utilize the Graphics Processing Units (GPUs) to speed up the reachability verification. The key idea is to achieve a dynamic load balancing so that the many cores in GPUs are fully utilized during the state space exploration.To this end, we firstly construct a compact data encoding of the input transition systems to reduce the memory cost and fit the calculation in GPUs. To support a large number of concurrent components, we propose a multi-integer encoding with conflict-release accessing approach. We …
All Your Sessions Are Belong To Us: Investigating Authenticator Leakage Through Backup Channels On Android, Guangdong Bai, Jun Sun, Jianliang Wu, Quanqi Ye, Li Li, Jin Song Dong, Shanqing Guo
All Your Sessions Are Belong To Us: Investigating Authenticator Leakage Through Backup Channels On Android, Guangdong Bai, Jun Sun, Jianliang Wu, Quanqi Ye, Li Li, Jin Song Dong, Shanqing Guo
Research Collection School Of Computing and Information Systems
Security of authentication protocols heavily relies on the confidentiality of credentials (or authenticators) like passwords and session IDs. However, unlike browser-based web applications for which highly evolved browsers manage the authenticators, Android apps have to construct their own management. We find that most apps simply locate their authenticators into the persistent storage and entrust underlying Android OS for mediation. Consequently, these authenticators can be leaked through compromised backup channels. In this work, we conduct the first systematic investigation on this previously overlooked attack vector. We find that nearly all backup apps on Google Play inadvertently expose backup data to any …
On The Unreliability Of Bug Severity Data, Yuan Tian, Nasir Ali, David Lo, Ahmed E. Hassan
On The Unreliability Of Bug Severity Data, Yuan Tian, Nasir Ali, David Lo, Ahmed E. Hassan
Research Collection School Of Computing and Information Systems
Severity levels, e.g., critical and minor, of bugs are often used to prioritize development efforts. Prior research efforts have proposed approaches to automatically assign the severity label to a bug report. All prior efforts verify the accuracy of their approaches using human-assigned bug reports data that is stored in software repositories. However, all prior efforts assume that such human-assigned data is reliable. Hence a perfect automated approach should be able to assign the same severity label as in the repository – achieving a 100% accuracy. Looking at duplicate bug reports (i.e., reports referring to the same problem) from three open-source …
A Layered Hidden Markov Model For Predicting Human Trajectories In A Multi-Floor Building, Qian Li, Hoong Chuin Lau
A Layered Hidden Markov Model For Predicting Human Trajectories In A Multi-Floor Building, Qian Li, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Tracking and modeling huge amount of users’ movement in a multi-floor building by using wireless devices is a challenging task, due to crowd movement complexity and signal sensing accuracy. In this paper, we use Layered Hidden Markov Model (LHMM) to fit the spatial-temporal trajectories (with large number of missing values). We decompose the problem into distinct layers that Hidden Markov Models (HMMs) are operated at different spatial granularities separately. Baum-Welch algorithm and Viterbi algorithm are used for finding the probable location sequences at each layer. By measuring the predicted result of trajectories, we compared the predicted results of both single …
Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng
Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved …
Aesthetic Experience And Acceptance Of Human Computation Games, Xiaohui Wang, Dion Hoe-Lian Goh, Ee-Peng Lim, Adrian Wei Liang Vu
Aesthetic Experience And Acceptance Of Human Computation Games, Xiaohui Wang, Dion Hoe-Lian Goh, Ee-Peng Lim, Adrian Wei Liang Vu
Research Collection School Of Computing and Information Systems
Human computation games (HCGs) are applications that leverage games to solve computational problems that are out reach of the capacity of computers. Game aesthetics are critical for HCG acceptance, and the game elements should motivate users to contribute time and effort. In this paper, we examine the effect of aesthetic experience on intention to use HCGs. A between-subjects experiment was conducted to compare a HCG and a human computation system (HCS). Results demonstrated that HCGs provided a greater sense of aesthetic experience and attracted more intentional usage than HCSs. Implications of this study are discussed.
Incorporating Analytics Into A Business Process Modelling Course, Gottipati Swapna, Shankararaman, Venky
Incorporating Analytics Into A Business Process Modelling Course, Gottipati Swapna, Shankararaman, Venky
Research Collection School Of Computing and Information Systems
Embedding analytics is about integrating data analytics into operational systems that are part of an organization’s business processes. Currently, most organizations focus on automation business processes and enhancing productivity. However, going forward, in order to stay competitive, organizations have to go beyond automating their processes, by making them more intelligent, by embedding analytics into their processes and business applications. Therefore, there is need for enhancing the knowledge and skills of BPM professionals with know-how on improving a business process by embedding analytics into the workflow. In this paper contribution, the authors share their experience on how an existing process modelling, …
Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan
Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Ageing in place demands a new paradigm of inhouse caregiving allowing many aspects of daily lives to be tackled by smart appliances and technologies. The important challenges include the effective provision of recommendations by multiple parties of caregiver constituting changes of the user’s behavior. In this multiagent environment, interdependencies between agents become major issues to tackle. This paper presents an approach of dynamic group formation for autonomous caregiving agents to collaborate in recommending different aspects of well-being. The approach supports the agents to regulate the timing of their recommendations, prevent conflicting messages, and cooperate to make more effective persuasions. A …
Bring-Your-Own-Application (Byoa): Optimal Stochastic Application Migration In Mobile Cloud Computing, Jonathan David Chase, Dusit Niyato, Sivadon Chaisiri
Bring-Your-Own-Application (Byoa): Optimal Stochastic Application Migration In Mobile Cloud Computing, Jonathan David Chase, Dusit Niyato, Sivadon Chaisiri
Research Collection School Of Computing and Information Systems
The increasing popularity of using mobile devices in a work context, has led to the need to be able to support more powerful computation. Users no longer remain in an office or at home to conduct their activities, preferring libraries and cafes. In this paper, we consider a mobile cloud computing scenario in which users bring their own mobile devices and are offered a variety of equipment, e.g., desktop computer, smart- TV, or projector, to migrate their applications to, so as to save battery life, improve usability and performance. We formulate a stochastic optimization problem to optimize the allocation of …
Progressive Sequence Matching For Adl Plan Recommendation, Shan Gao, Di Wang, Ah-Hwee Tan, Chunyan Miao
Progressive Sequence Matching For Adl Plan Recommendation, Shan Gao, Di Wang, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
Activities of Daily Living (ADLs) are indicatives of a person’s lifestyle. In particular, daily ADL routines closely relate to a person’s well-being. With the objective of promoting active lifestyles, this paper presents an agent system that provides recommendations of suitable ADL plans (i.e., selected ADL sequences) to individual users based on the more active lifestyles of the others. Specifically, we develop a set of quantitative measures, named wellness scores, spanning the evaluation across the physical, cognitive, emotion, and social aspects based on his or her ADL routines. Then we propose an ADL sequence learning model, named Recommendation ADL ART, or …
Preface: Wi 2015, Ah-Hwee Tan, Yuefeng Li, Ee-Peng Lim, Jie Zhang, Dell Zhang, Julita Vassileva
Preface: Wi 2015, Ah-Hwee Tan, Yuefeng Li, Ee-Peng Lim, Jie Zhang, Dell Zhang, Julita Vassileva
Research Collection School Of Computing and Information Systems
This volume contains the papers selected for presentation at the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI’15), which was held from 6 to 9 December 2015 in Singapore, a city which welcomes people from different parts of the world to work and play. Following the tradition of WI conference in previous years, WI’15 was collocated with 2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’15). Both WI’15 and IAT’15 were sponsored by the IEEE Computer Society, Web Intelligence Consortium (WIC), Association for Computing Machinery (ACM), and the Memetic Computing Society. The two collocated conferences were hosted by the Joint …
Capstone Projects Mining System For Insights And Recommendations, Melvrivk Aik Chun Goh, Swapna Gottipati, Venky Shankararaman
Capstone Projects Mining System For Insights And Recommendations, Melvrivk Aik Chun Goh, Swapna Gottipati, Venky Shankararaman
Research Collection School Of Computing and Information Systems
In this paper, we present a classification based system to discover knowledge and trends in higher education students’ projects. Essentially, the educational capstone projects provide an opportunity for students to apply what they have learned and prepare themselves for industry needs. Therefore mining such projects gives insights of students’ experiences as well as industry project requirements and trends. In particular, we mine capstone projects executed by Information Systems students to discover patterns and insights related to people, organization, domain, industry needs and time. We build a capstone projects mining system (CPMS) based on classification models that leverage text mining, natural …
Differentially Private Subspace Clustering, Yining Wang, Yu-Xiang Wang, Aarti Singh
Differentially Private Subspace Clustering, Yining Wang, Yu-Xiang Wang, Aarti Singh
Research Collection School Of Computing and Information Systems
Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple “clusters” so that data points in a single cluster lie approximately on a low-dimensional linear subspace. It is originally motivated by 3D motion segmentation in computer vision, but has recently been generically applied to a wide range of statistical machine learning problems, which often involves sensitive datasets about human subjects. This raises a dire concern for data privacy. In this work, we build on the framework of differential privacy and present two provably private subspace clustering algorithms. We demonstrate via both theory and experiments that …
Content-Based Visual Landmark Search Via Multimodal Hypergraph Learning, Lei Zhu, Jialie Shen, Hai Jin, Ran Zheng, Liang Xie
Content-Based Visual Landmark Search Via Multimodal Hypergraph Learning, Lei Zhu, Jialie Shen, Hai Jin, Ran Zheng, Liang Xie
Research Collection School Of Computing and Information Systems
While content-based landmark image search has recently received a lot of attention and became a very active domain, it still remains a challenging problem. Among the various reasons, high diverse visual content is the most significant one. It is common that for the same landmark, images with a wide range of visual appearances can be found from different sources and different landmarks may share very similar sets of images. As a consequence, it is very hard to accurately estimate the similarities between the landmarks purely based on single type of visual feature. Moreover, the relationships between landmark images can be …
Robust Execution Strategies For Project Scheduling With Unreliable Resources And Stochastic Durations, Na Fu, Hoong Chuin Lau, Pradeep Varakantham
Robust Execution Strategies For Project Scheduling With Unreliable Resources And Stochastic Durations, Na Fu, Hoong Chuin Lau, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
The resource-constrained project scheduling problem with minimum and maximum time lags (RCPSP/max) is a general model for resource scheduling in many real-world problems (such as manufacturing and construction engineering). We consider RCPSP/max problems where the durations of activities are stochastic and resources can have unforeseen breakdowns. Given a level of allowable risk, (Formula presented.), our mechanisms aim to compute the minimum robust makespan execution strategy. Robust makespan for an execution strategy is any makespan value that has a risk less than (Formula presented.). The risk for a makespan value, (Formula presented.) given an execution strategy, is the probability that a …
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Research Collection School Of Computing and Information Systems
Crowdsourcing has shown great potential in obtaining large-scale and cheap labels for different tasks. However, obtaining reliable labels is challenging due to several reasons, such as noisy annotators, limited budget and so on. The state-of-the-art approaches, either suffer in some noisy scenarios, or rely on unlimited resources to acquire reliable labels. In this article, we adopt the learning with expert~(AKA worker in crowdsourcing) advice framework to robustly infer accurate labels by considering the reliability of each worker. However, in order to accurately predict the reliability of each worker, traditional learning with expert advice will consult with external oracles~(AKA domain experts) …
A Benchmark And Comparative Study Of Video-Based Face Recognition On Cox Face Database, Zhiwu Huang, S. Shan, R. Wang, H. Zhang, S. Lao, A. Kuerban, X. Chen
A Benchmark And Comparative Study Of Video-Based Face Recognition On Cox Face Database, Zhiwu Huang, S. Shan, R. Wang, H. Zhang, S. Lao, A. Kuerban, X. Chen
Research Collection School Of Computing and Information Systems
Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected …
Bl-Mle: Block-Level Message-Locked Encryption For Secure Large File Deduplication, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo
Bl-Mle: Block-Level Message-Locked Encryption For Secure Large File Deduplication, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo
Research Collection School Of Computing and Information Systems
Deduplication is a popular technique widely used to save storage spaces in the cloud. To achieve secure deduplication of encrypted files, Bellare et al. formalized a new cryptographic primitive named message-locked encryption (MLE) in Eurocrypt 2013. Although an MLE scheme can be extended to obtain secure deduplication for large files, it requires a lot of metadata maintained by the end user and the cloud server. In this paper, we propose a new approach to achieve more efficient deduplication for (encrypted) large files. Our approach, named block-level message-locked encryption (BL-MLE), can achieve file-level and block-level deduplication, block key management, and proof …
A Misspecification Test For Logit Based Route Choice Models, Tien Mai, Emma Frejinger, Fabian Bastin
A Misspecification Test For Logit Based Route Choice Models, Tien Mai, Emma Frejinger, Fabian Bastin
Research Collection School Of Computing and Information Systems
The multinomial logit (MNL) model is often used for analyzing route choices in real networks in spite of the fact that path utilities are believed to be correlated. Yet, statistical tests for model misspecification are rarely used. This paper shows how the information matrix test for model misspecification proposed byWhite (1982) can be applied to test path-based and link-based MNL route choice models.We present a Monte Carlo experiment using simulated data to assess the size and the power of the test and to compare its performance with the IIA (Hausman and McFadden, 1984) and McFadden–Train Lagrange multiplier (McFadden and Train, …
On Neighborhood Effects In Location-Based Social Networks, Thanh-Nam Doan, Freddy Chong-Tat Chua, Ee-Peng Lim
On Neighborhood Effects In Location-Based Social Networks, Thanh-Nam Doan, Freddy Chong-Tat Chua, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In this paper, we analyze factors that determine the check-in decisions of users on venues using a location-based social network dataset. Based on a Foursquare dataset constructed from Singapore-based users, we devise a stringent criteria to identify the actual home locations of a subset of users. Using these users' check-ins, we aim to ascertain the neighborhood effect on the venues visited, compared with the activity level of users. We further formulate the check-in count prediction and check-in prediction tasks. A comprehensive set of features have been defined and they encompass information from users, venues, their neighbors, and friendship networks. We …
Supercnn: A Superpixelwise Convolutional Neural Network For Salient Object Detection, Shengfeng He, Rynson W.H. Lau, Wenxi Liu, Zhe Huang, Qingxiong Yang
Supercnn: A Superpixelwise Convolutional Neural Network For Salient Object Detection, Shengfeng He, Rynson W.H. Lau, Wenxi Liu, Zhe Huang, Qingxiong Yang
Research Collection School Of Computing and Information Systems
Existing computational models for salient object detection primarily rely on hand-crafted features, which are only able to capture low-level contrast information. In this paper, we learn the hierarchical contrast features by formulating salient object detection as a binary labeling problem using deep learning techniques. A novel superpixelwise convolutional neural network approach, called SuperCNN, is proposed to learn the internal representations of saliency in an efficient manner. In contrast to the classical convolutional networks, SuperCNN has four main properties. First, the proposed method is able to learn the hierarchical contrast features, as it is fed by two meaningful superpixel sequences, which …
On Top-K Selection In Multi-Armed Bandits And Hidden Bipartite Graphs, Wei Cao, Jian Li, Yufei Tao, Zhize Li
On Top-K Selection In Multi-Armed Bandits And Hidden Bipartite Graphs, Wei Cao, Jian Li, Yufei Tao, Zhize Li
Research Collection School Of Computing and Information Systems
This paper discusses how to efficiently choose from $n$ unknown distributions the $k$ ones whose means are the greatest by a certain metric, up to a small relative error. We study the topic under two standard settings---multi-armed bandits and hidden bipartite graphs---which differ in the nature of the input distributions. In the former setting, each distribution can be sampled (in the i.i.d. manner) an arbitrary number of times, whereas in the latter, each distribution is defined on a population of a finite size $m$ (and hence, is fully revealed after m samples). For both settings, we prove lower bounds on …
Fast Reinforcement Learning Under Uncertainties With Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Fast Reinforcement Learning Under Uncertainties With Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Using feedback signals from the environment, a reinforcement learning (RL) system typically discovers action policies that recommend actions effective to the states based on a Q-value function. However, uncertainties over the estimation of the Q-values can delay the convergence of RL. For fast RL convergence by accounting for such uncertainties, this paper proposes several enhancements to the estimation and learning of the Q-value using a self-organizing neural network. Specifically, a temporal difference method known as Q-learning is complemented by a Q-value Polarization procedure, which contrasts the Q-values using feedback signals on the effect of the recommended actions. The polarized Q-values …
A Bayesian Recommender Model For User Rating And Review Profiling, Mingming Jiang, Dandan Song, Lejian Liao, Feida Zhu
A Bayesian Recommender Model For User Rating And Review Profiling, Mingming Jiang, Dandan Song, Lejian Liao, Feida Zhu
Research Collection School Of Computing and Information Systems
Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews accompanied by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles' information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering (CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with "user attitudes" (i.e., abstract rating patterns) over the same distribution, our method achieves …
A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
Research Collection School Of Computing and Information Systems
We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in …
Mylife: An Online Personal Memory Album, Di Wang, Ah-Hwee Tan
Mylife: An Online Personal Memory Album, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In this demo, we illustrate the formation, retrieval, and playback of autobiographical memory in an online personal memory album named MyLife. The memory in MyLife consists of pictorial snapshots of one's life together with the associated context, namely time, location, people, activity, imagery, and emotion. MyLife allows direct import of memories from other online personal photo repositories. For memory retrieval, users can use not only exact cues, but also partial, vague, inaccurate, and random ones. The retrieved memories are then played back as a movie-like slide show with various visual effects and background music. MyLife holds high potential in both …
Preface To Wi-Iat 2015 Workshops And Demo/Posters, Ah-Hwee Tan, Yuefeng Li
Preface To Wi-Iat 2015 Workshops And Demo/Posters, Ah-Hwee Tan, Yuefeng Li
Research Collection School Of Computing and Information Systems
This volume contains the papers selected for presentation at the workshops and demonstration/poster track as part of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI’15) and 2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’15) held from 6 to 9 December 2015 in Singapore.
Silver Assistants For Aging-In-Place, Di Wang, Budhitama Subagdja, Yilin Kang, Ah-Hwee Tan
Silver Assistants For Aging-In-Place, Di Wang, Budhitama Subagdja, Yilin Kang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In this demo, we present an assembly of silver assistants for supporting Aging-In-Place (AIP). The virtual agents are designed to serve around the clock to complement human care within the intelligent home environment. Residing in different platforms with ubiquitous access, the agents collaboratively provide holistic care to the elderly users. The demonstration is shown in a 3-D virtual home replicating a typical 5-room apartment in Singapore. Sensory inputs are stored in a knowledge base named Situation Awareness Model (SAM). Therefore, the capabilities of the agents can always be extended by expanding the knowledge defined in SAM. Using the simulation system, …
Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan
Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Ageing in place demands a new paradigm of inhouse caregiving allowing many aspects of daily lives to be tackled by smart appliances and technologies. The important challenges include the effective provision of recommendations by multiple parties of caregiver constituting changes of the user's behavior. In this multiagent environment, interdependencies between agents become major issues to tackle. This paper presents an approach of dynamic group formation for autonomous caregiving agents to collaborate in recommending different aspects of well-being. The approach supports the agents to regulate the timing of their recommendations, prevent conflicting messages, and cooperate to make more effective persuasions. A …
Modeling Social Media Content With Word Vectors For Recommendation, Ying Ding, Jing Jiang
Modeling Social Media Content With Word Vectors For Recommendation, Ying Ding, Jing Jiang
Research Collection School Of Computing and Information Systems
In social media, recommender systems are becoming more and more important. Different techniques have been designed for recommendations under various scenarios, but many of them do not use user-generated content, which potentially reflects users’ opinions and interests. Although a few studies have tried to combine user-generated content with rating or adoption data, they mostly reply on lexical similarity to calculate textual similarity. However, in social media, a diverse range of words is used. This renders the traditional ways of calculating textual similarity ineffective. In this work, we apply vector representation of words to measure the semantic similarity between text. We …