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Articles 4201 - 4230 of 9025
Full-Text Articles in Computer Sciences
Modeling Sequential And Basket-Oriented Associations For Top-K Recommendation, Duc-Trong Le Duc Trong
Modeling Sequential And Basket-Oriented Associations For Top-K Recommendation, Duc-Trong Le Duc Trong
Dissertations and Theses Collection (Open Access)
Top-K recommendation is a typical task in Recommender Systems. In traditional approaches, it mainly relies on the modeling of user-item associations, which emphasizes the user-specific factor or personalization. Here, we investigate another direction that models item-item associations, especially with the notions of sequence-aware and basket-level adoptions . Sequences are created by sorting item adoptions chronologically. The associations between items along sequences, referred to as “sequential associations”, indicate the influence of the preceding adoptions on the following adoptions. Considering a basket of items consumed at the same time step (e.g., a session, a day), “basket-oriented associations” imply correlative dependencies among these …
Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta
Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Automatic student assessment plays an important role in education - it provides instant feedback to learners, and at the same time reduces tedious grading workload for instructors. In this paper, we investigate new machine learning techniques for automatic short answer grading (ASAG). The ASAG problem mainly involves assessing short, natural language responses to given questions automatically. While current research in the field has focused either on feature engineering or deep learning, we propose a new approach which combines the advantages of both. More specifically, we propose a Siamese Bidirectional LSTM Neural Network based Regressor in conjunction with handcrafted features for …
Discursive Power In Contemporary Media Systems: A Comparative Framework, Andreas Jungherr, Oliver Posegga, Jisun An
Discursive Power In Contemporary Media Systems: A Comparative Framework, Andreas Jungherr, Oliver Posegga, Jisun An
Research Collection School Of Computing and Information Systems
Contemporary media systems are in transition. The constellation of organizations, groups, and individuals contributing information to national and international news flows has changed as a result of the digital transformation. The 'hybrid media system' has proven to be one of the most instructive concepts addressing this change. Its focus on the mutually dependent interconnections between various types of media organizations, actors, and publics has inspired prolific research. Yet the concept can tempt researchers to sidestep systematic analyses of information flows and actors’ differing degrees of influence by treating media systems as a black box. To enable large-scale, empirical comparative studies …
Fair And Dynamic Data Sharing Framework In Cloud-Assisted Internet Of Everything, Yinbin Miao, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Hongjun Wu, Hongwei Li
Fair And Dynamic Data Sharing Framework In Cloud-Assisted Internet Of Everything, Yinbin Miao, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Hongjun Wu, Hongwei Li
Research Collection School Of Computing and Information Systems
Cloud-assisted Internet of Things (IoT) is increasingly prevalent in our society, for example in home and office environment; hence, it is also known as cloud-assisted Internet of Everything (IoE). While in such a setup, data can be easily shared and disseminated (e.g., between a device, such as Amazon Echo and the cloud, such as Amazon AWS), there are potential security considerations that need to be addressed. Thus, a number of security solutions have been proposed. For example, searchable encryption (SE) has been extensively studied due to its capability to facilitate searching of encrypted data. However, threat models in most existing …
Dependable Machine Intelligence At The Tactical Edge, Archan Misra, Kasthuri Jayarajah, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Randy Tandriansyah Daratan, Shuochao Yao, Tarek Abdelzaher
Dependable Machine Intelligence At The Tactical Edge, Archan Misra, Kasthuri Jayarajah, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Randy Tandriansyah Daratan, Shuochao Yao, Tarek Abdelzaher
Research Collection School Of Computing and Information Systems
The paper describes a vision for dependable application of machine learning-based inferencing on resource-constrained edge devices. The high computational overhead of sophisticated deep learning learning techniques imposes a prohibitive overhead, both in terms of energy consumption and sustainable processing throughput, on such resource-constrained edge devices (e.g., audio or video sensors). To overcome these limitations, we propose a ``cognitive edge" paradigm, whereby (a) an edge device first autonomously uses statistical analysis to identify potential collaborative IoT nodes, and (b) the IoT nodes then perform real-time sharing of various intermediate state to improve their individual execution of machine intelligence tasks. We provide …
Cinema: Efficient And Privacy-Preserving Online Medical Primary Diagnosis With Skyline Query, Jianfeng Hua, Hui Zhu, Fengwei Wang, Ximeng Liu, Rongxing Lu, Hao Li, Yeping Zhang
Cinema: Efficient And Privacy-Preserving Online Medical Primary Diagnosis With Skyline Query, Jianfeng Hua, Hui Zhu, Fengwei Wang, Ximeng Liu, Rongxing Lu, Hao Li, Yeping Zhang
Research Collection School Of Computing and Information Systems
Online medical primary diagnosis system, which can provide convenient medical decision support through applying mobile communication and data analysis technology, has been considered as a promising approach to improve the quality of healthcare service. However, it still faces many severe challenges on the privacy of users' health information and the accuracy of diagnosis result, which deter the wide adoption of online medical primary diagnosis system. In this paper, we propose an efficient and privacy-preserving online medical primary diagnosis (CINEMA) framework. Within CINEMA framework, users can access online medical primary diagnosing service accurately without divulging their medical data. Specifically, based on …
The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper focuses on a recent variant of the Orienteering Problem (OP), namely the Capacitated Team OP (CTOP) which arises in the logistics industry. In this problem, each node is associated with a demand that needs to be satisfied and a score that need to be collected. Given a set of homogeneous fleet of vehicles, the objective is to find a path for each vehicle in order to maximize the total collected score, without violating the capacity and time budget. We propose an Iterated Local Search (ILS) algorithm for solving the CTOP. Two strategies, either accepting a new solution as …
To The Attention Of Mobile Software Developers: Guess What, Test Your App!, Luis C. Cruz, Rui Abreu, David Lo
To The Attention Of Mobile Software Developers: Guess What, Test Your App!, Luis C. Cruz, Rui Abreu, David Lo
Research Collection School Of Computing and Information Systems
Software testing is an important phase in the software development lifecycle because it helps in identifying bugs in a software system before it is shipped into the hand of its end users. There are numerous studies on how developers test general-purpose software applications. The idiosyncrasies of mobile software applications, however, set mobile apps apart from general-purpose systems (e.g., desktop, stand-alone applications, web services). This paper investigates working habits and challenges of mobile software developers with respect to testing. A key finding of our exhaustive study, using 1000 Android apps, demonstrates that mobile apps are still tested in a very ad …
Deepreview: Automatic Code Review Using Deep Multi-Instance Learning, Hengyi Li, Shuting Shi, Ferdian Thung, Xuan Huo, Bowen Xu, Ming Li, David Lo
Deepreview: Automatic Code Review Using Deep Multi-Instance Learning, Hengyi Li, Shuting Shi, Ferdian Thung, Xuan Huo, Bowen Xu, Ming Li, David Lo
Research Collection School Of Computing and Information Systems
Code review, an inspection of code changes in order to identify and fix defects before integration, is essential in Software Quality Assurance (SQA). Code review is a time-consuming task since the reviewers need to understand, analysis and provide comments manually. To alleviate the burden of reviewers, automatic code review is needed. However, this task has not been well studied before. To bridge this research gap, in this paper, we formalize automatic code review as a multi-instance learning task that each change consisting of multiple hunks is regarded as a bag, and each hunk is described as an instance. We propose …
Dynamic Student Classification On Memory Networks For Knowledge Tracing, Sein Minn, Michel C. Desmarais, Feida Zhu, Jing Xiao, Jianzong Wang
Dynamic Student Classification On Memory Networks For Knowledge Tracing, Sein Minn, Michel C. Desmarais, Feida Zhu, Jing Xiao, Jianzong Wang
Research Collection School Of Computing and Information Systems
Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classification on Memory Networks (DSCMN) for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in student’s long-term learning process. Experimental results confirm that the proposed model …
Route Planning For A Fleet Of Electric Vehicles With Waiting Times At Charging Stations, Baoxiang Li, Shashi Shekhar Jha, Hoong Chuin Lau
Route Planning For A Fleet Of Electric Vehicles With Waiting Times At Charging Stations, Baoxiang Li, Shashi Shekhar Jha, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Electric Vehicles (EVs) are the next wave of technology in the transportation industry. EVs are increasingly becoming common for personal transport and pushing the boundaries to become the mainstream mode of transportation. Use of such EVs in logistic fleets for delivering customer goods is not far from becoming reality. However, managing such fleet of EVs bring new challenges in terms of battery capacities and charging infrastructure for efficient route planning. Researchers have addressed such issues considering different aspects of the EVs such as linear battery charging/discharging rate, fixed travel times, etc. In this paper, we address the issue of waiting …
Exploring And Visualizing Household Electricity Consumption Patterns In Singapore: A Geospatial Analytics Approach, Yong Ying Tan, Tin Seong Kam
Exploring And Visualizing Household Electricity Consumption Patterns In Singapore: A Geospatial Analytics Approach, Yong Ying Tan, Tin Seong Kam
Research Collection School Of Computing and Information Systems
Despite being a small country-state, electricity consumption in Singa-pore is said to be non-homogeneous, as exploratory data analysis showed that the distributions of electricity consumption differ across and within administrative boundaries and dwelling types. Local indicators of spatial association (LISA) were calculated for public housing postal codes using June 2016 data to discover local clusters of households based on electricity consumption patterns. A detailed walkthrough of the analytical process is outlined to describe the R packages and framework used in the R environment. The LISA results are visualized on three levels: country level, regional level and planning subzone level. At …
Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen
Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen
Research Collection School Of Computing and Information Systems
With the emergence of smart phones and the popularity of GPS, the number of point of interest (POIs) is growing rapidly and spatial keyword search based on POIs has attracted significant attention. In this paper, we study a more sophistic type of spatial keyword searches that considers multiple query points and multiple query keywords, namely Aggregate Keyword Routing (AKR). AKR looks for an aggregate point m together with routes from each query point to m. The aggregate point has to satisfy the aggregate keywords, the routes from query points to the aggregate point have to pass POIs in order to …
Online Collaborative Filtering With Implicit Feedback, Jianwen Yin, Chenghao Liu, Jundong Li, Bing Tian Dai, Yun-Chen Chen, Min Wu, Jianling Sun
Online Collaborative Filtering With Implicit Feedback, Jianwen Yin, Chenghao Liu, Jundong Li, Bing Tian Dai, Yun-Chen Chen, Min Wu, Jianling Sun
Research Collection School Of Computing and Information Systems
Studying recommender systems with implicit feedback has become increasingly important. However, most existing works are designed in an offline setting while online recommendation is quite challenging due to the one-class nature of implicit feedback. In this paper, we propose an online collaborative filtering method for implicit feedback. We highlight three critical issues of existing works. First, when positive feedback arrives sequentially, if we treat all the other missing items for this given user as the negative samples, the mis-classified items will incur a large deviation since some items might appear as the positive feedback in the subsequent rounds. Second, the …
Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras
Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras
Research Collection School Of Computing and Information Systems
An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is …
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Jiashi Feng
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Jiashi Feng
Research Collection School Of Computing and Information Systems
SPIDER (Stochastic Path Integrated Differential EstimatoR) is an efficient gradient estimation technique developed for non-convex stochastic optimization. Although having been shown to attain nearly optimal computational complexity bounds, the SPIDERtype methods are limited to linear metric spaces. In this paper, we introduce the Riemannian SPIDER (R-SPIDER) method as a novel nonlinear-metric extension of SPIDER for efficient non-convex optimization on Riemannian manifolds. We prove that for finitesum problems with n components, R-SPIDER converges to an -accuracy stationary point within O min n + √ n 2 , 1 3 stochastic gradient evaluations, which is sharper in magnitude than the prior Riemannian …
Revocable Attribute-Based Encryption With Decryption Key Exposure Resistance And Ciphertext Delegation, Shengmin Xu, Guomin Yang, Yi Mu
Revocable Attribute-Based Encryption With Decryption Key Exposure Resistance And Ciphertext Delegation, Shengmin Xu, Guomin Yang, Yi Mu
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) enables fine-grained access control over encrypted data. A practical and popular approach for handing revocation in ABE is to use the indirect revocation mechanism, in which a key generation centre (KGC) periodically broadcasts key update information for all data users over a public channel. Unfortunately, existing RABE schemes are vulnerable to decryption key exposure attack which has been well studied in the identity-based setting. In this paper, we introduce a new notion for RABE called re-randomizable piecewise key generation by allowing a data user to re-randmomize the combined secret key and the key update to obtain the …
Perception Coordination Network: A Neuro Framework For Multimodal Concept Acquisition And Binding, You-Lu Xing, Xiao-Feng Shi, Fu-Rao Shen, Jin-Xi Zhao, Jing-Xin Pan, Ah-Hwee Tan
Perception Coordination Network: A Neuro Framework For Multimodal Concept Acquisition And Binding, You-Lu Xing, Xiao-Feng Shi, Fu-Rao Shen, Jin-Xi Zhao, Jing-Xin Pan, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
To simulate the concept acquisition and binding of different senses in the brain, a biologically inspired neural network model named perception coordination network (PCN) is proposed. It is a hierarchical structure, which is functionally divided into the primary sensory area (PSA), the primary sensory association area (SAA), and the higher order association area (HAA). The PSA contains feature neurons which respond to many elementary features, e.g., colors, shapes, syllables, and basic flavors. The SAA contains primary concept neurons which combine the elementary features in the PSA to represent unimodal concept of objects, e.g., the image of an apple, the Chinese …
Wiwear: Wearable Sensing Via Directional Wifi Energy Harvesting, Huy Vu Tran, Archan Misra, Jie Xiong, Rajesh Krishna Balan
Wiwear: Wearable Sensing Via Directional Wifi Energy Harvesting, Huy Vu Tran, Archan Misra, Jie Xiong, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Energy harvesting, from a diverse set of modes such as light or motion, has been viewed as the key to developing batteryless sensing devices. In this paper, we develop the nascent idea of harvesting RF energy from WiFi transmissions, applying it to power a prototype wearable device that captures and transmits accelerometer sensor data. Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~23 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what …
Peeling Back The (Onion) Layers Of The Dark Web, Singapore Management University
Peeling Back The (Onion) Layers Of The Dark Web, Singapore Management University
Perspectives@SMU
Manoeuvring the minefield of risk and exposure
Quantum Computing Is Here To Stay, Manoj Thulasidas
Quantum Computing Is Here To Stay, Manoj Thulasidas
MITB Thought Leadership Series
QUANTUM COMPUTING is emerging from university laboratories and entering the industry arena at a painfully slow pace. The measured and deliberate progress is understandable given its complexity and promise. The stakes are high, because quantum computing presents the tantalising prospect of solving problems previously considered completely insoluble
Functionality & Privacy In Mobile Applications - Who's Going To Win The Game, Debin Gao
Functionality & Privacy In Mobile Applications - Who's Going To Win The Game, Debin Gao
MITB Thought Leadership Series
MOBILE APPS have brought so much convenience and fun into our lives. From route planning to grocery shopping, reserving flights and hiring bicycles, to the action games we play to pass the time on public transport.
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Research Collection School Of Computing and Information Systems
The rise of Initial Coin Offerings (ICO) in recent times and their potential for investment opportunities have investors spending a lot of time researching ICOs or having to follow the crowd. This paper aims to explore four broad factors of ICOs: identity, credibility, investor sentiment, and price movement to develop a framework that is useful in determining ICO quality. Research is shown using data sources including public forums, chat groups, web sites, white papers as well as smart contract details. Finally, a system, based on the framework, is proposed that can be used to detect and regulate ICO activities and …
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Research Collection School Of Computing and Information Systems
Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed …
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Research Collection School Of Computing and Information Systems
Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed …
An Artificial Bee Colony-Based Hybrid Approach For Waste Collection Problem With Midway Disposal Pattern, Qu Wei, Zhaoxia Guo, Hoong Chuin Lau, Zhenggang He
An Artificial Bee Colony-Based Hybrid Approach For Waste Collection Problem With Midway Disposal Pattern, Qu Wei, Zhaoxia Guo, Hoong Chuin Lau, Zhenggang He
Research Collection School Of Computing and Information Systems
This paper investigates a waste collection problem with the consideration of midway disposal pattern. An artificial bee colony (ABC)-based hybrid approach is developed to handle this problem, in which the hybrid ABC algorithm is proposed to generate the better optimum-seeking performance while a heuristic procedure is proposed to select the disposal trip dynamically and calculate the carbon emissions in waste collection process. The effectiveness of the proposed approach is validated by numerical experiments. Experimental results show that the proposed hybrid approach can solve the investigated problem effectively. The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four popular …
Careful-Packing: A Practical And Scalable Anti-Tampering Software Protection Enforced By Trusted Computing, Flavio Toffalini, Martín Ochoa, Jun Sun, Jianying Zhou
Careful-Packing: A Practical And Scalable Anti-Tampering Software Protection Enforced By Trusted Computing, Flavio Toffalini, Martín Ochoa, Jun Sun, Jianying Zhou
Research Collection School Of Computing and Information Systems
Ensuring the correct behaviour of an application is a critical security issue. One of the most popular ways to modify the intended behaviour of a program is to tamper its binary. Several solutions have been proposed to solve this problem, including trusted computing and anti-tampering techniques. Both can substantially increase security, and yet both have limitations. In this work, we propose an approach which combines trusted computing technologies and anti-tampering techniques, and that synergistically overcomes some of their inherent limitations. In our approach critical software regions are protected by leveraging on trusted computing technologies and cryptographic packing, without introducing additional …
Suaa: A Secure User Authentication Scheme With Anonymity For The Single & Multi-Server Environments, Nassoro M. R. Lwamo, Liehuang Zhu, Chang Xu, Kashif Sharif, Ximeng Liu, Chuan Zhang
Suaa: A Secure User Authentication Scheme With Anonymity For The Single & Multi-Server Environments, Nassoro M. R. Lwamo, Liehuang Zhu, Chang Xu, Kashif Sharif, Ximeng Liu, Chuan Zhang
Research Collection School Of Computing and Information Systems
The rapid increase in user base and technological penetration has enabled the use of a wide range of devices and applications. The services are rendered to these devices from single-server or highly distributed server environments, irrespective of their location. As the information exchanged between servers and clients is private, numerous forms of attacks can be launched to compromise it. To ensure the security, privacy, and availability of the services, different authentication schemes have been proposed for both single-server and multi-server environments. The primary performance objective of such schemes is to prevent most (if not all) attacks, with minimal computational costs …
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Research Collection School Of Computing and Information Systems
Context: Software development is increasingly dependent on existing libraries. Developers need help to find suitable library APIs. Although many studies have been proposed to recommend relevant functional APIs that can be invoked for implementing a functionality, few studies have paid attention to an orthogonal need associated with event-driven programming frameworks, such as the Android framework. In addition to invoking functional APIs, Android developers need to know where to place functional code according to various events that may be triggered within the framework.Objective: This paper aims to develop an API recommendation engine for Android application development that can recommend both (1) …
Fine-Grained Geolocation Of Tweets In Temporal Proximity, Wen Haw Chong, Ee Peng Lim
Fine-Grained Geolocation Of Tweets In Temporal Proximity, Wen Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) fromwhich they were posted. This explicitly recovers the venue context that is essential for applications such aslocation-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets that arecontained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the sameuser and within a short time interval. This scenario arises from two observations: (1) It is quite common thatusers post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocatea tweet, …