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Singapore Management University

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Full-Text Articles in Databases and Information Systems

A Business Zone Recommender System Based On Facebook And Urban Planning Data, Jovian Lin, Richard Jayadi Oentaryo, Ee Peng Lim, Casey Vu, Adrian Wei Liang Vu, Philips Kokoh And Prasetyo Mar 2016

A Business Zone Recommender System Based On Facebook And Urban Planning Data, Jovian Lin, Richard Jayadi Oentaryo, Ee Peng Lim, Casey Vu, Adrian Wei Liang Vu, Philips Kokoh And Prasetyo

Research Collection School Of Computing and Information Systems

We present ZoneRec—a zone recommendation system for physical businesses in an urban city,which uses both public business data from Facebook and urban planning data. The systemconsists of machine learning algorithms that take in a business’ metadata and outputs a list ofrecommended zones to establish the business in. We evaluate our system using data of foodbusinesses in Singapore and assess the contribution of different feature groups to therecommendation quality.

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Campus-Scale Mobile Crowd-Tasking: Deployment And Behavioral Insights, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Nikita Jaiman, Randy Tandriansiyah, Cen Chen, Hoong Chuin Lau, Deepthi Chander, Koustuv Dasgupta Mar 2016

Campus-Scale Mobile Crowd-Tasking: Deployment And Behavioral Insights, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Nikita Jaiman, Randy Tandriansiyah, Cen Chen, Hoong Chuin Lau, Deepthi Chander, Koustuv Dasgupta

Research Collection School Of Computing and Information Systems

Mobile crowd-tasking markets are growing at an unprecedented rate with increasing number of smartphone users. Such platforms differ from their online counterparts in that they demand physical mobility and can benefit from smartphone processors and sensors for verification purposes. Despite the importance of such mobile crowd-tasking markets, little is known about the labor supply dynamics and mobility patterns of the users. In this paper we design, develop and experiment with a realwporld mobile crowd-tasking platform, called TA$Ker. Our contributions are two-fold: (a) We develop TA$Ker, a system that allows us to empirically study the worker responses to push vs. pull …

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Learning To Find Topic Experts In Twitter Via Different Relations, Wei Wei, Gao Cong, Chunyan Miao, Feida Zhu, Guohui Li Mar 2016

Learning To Find Topic Experts In Twitter Via Different Relations, Wei Wei, Gao Cong, Chunyan Miao, Feida Zhu, Guohui Li

Research Collection School Of Computing and Information Systems

Expert finding has become a hot topic along with the flourishing of social networks, such as micro-blogging services like Twitter. Finding experts in Twitter is an important problem because tweets from experts are valuable sources that carry rich information (e.g., trends) in various domains. However, previous methods cannot be directly applied to Twitter expert finding problem. Recently, several attempts use the relations among users and Twitter Lists for expert finding. Nevertheless, these approaches only partially utilize such relations. To this end, we develop a probabilistic method to jointly exploit three types of relations (i.e., follower relation, user-list relation and list-list …

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Copyright Law And The Supply Of Creative Work: Evidence From The Movies, Ivan Paak Liang Png, Qiu-Hong Wang Feb 2016

Copyright Law And The Supply Of Creative Work: Evidence From The Movies, Ivan Paak Liang Png, Qiu-Hong Wang

Research Collection School Of Computing and Information Systems

There is almost no empirical evidence on the extent to whichcopyright law works in the sense of increasing the production of creative work.Here, we study the impact of two major changes in copyright law – the extensionof copyright term and the European Rental Directive – on the production ofmovies. In a panel of 23 OECD countries, among which 19 extendedcopyright term at various times between 1991–2005, we found no statisticallyrobust evidence that copyright term extension was associated with higher movie production.In a panel of 17 European countries between 1991–2005, wefound no statistically robust evidence that compliance with the RentalDirective was …

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Multiagent-Based Route Guidance For Increasing The Chance Of Arrival On Time, Zhiguang Cao, Hongliang Guo, Jie Zhang, Ulrich Fastenrath Feb 2016

Multiagent-Based Route Guidance For Increasing The Chance Of Arrival On Time, Zhiguang Cao, Hongliang Guo, Jie Zhang, Ulrich Fastenrath

Research Collection School Of Computing and Information Systems

Transportation and mobility are central to sustainable urban development, where multiagent-based route guidance is widely applied. Traditional multiagent-based route guidance always seeks LET (least expected travel time) paths. However, drivers usually have specific expectations, i.e., tight or loose deadlines, which may not be all met by LET paths. We thus adopt and extend the probability tail model that aims to maximize the probability of reaching destinations before deadlines. Specifically, we propose a decentralized multiagent approach, where infrastructure agents locally collect intentions of concerned vehicle agents and formulate route guidance as a route assignment problem, to guarantee their arrival on time. …

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Online Learning Of Arima For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun Feb 2016

Online Learning Of Arima For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun

Research Collection School Of Computing and Information Systems

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. The idea of our ARIMA method is to …

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Online Cross-Modal Hashing For Web Image Retrieval, Liang Xie, Jialie Shen, Lei Zhu Feb 2016

Online Cross-Modal Hashing For Web Image Retrieval, Liang Xie, Jialie Shen, Lei Zhu

Research Collection School Of Computing and Information Systems

Cross-modal hashing (CMH) is an efficient technique for the fast retrieval of web image data, and it has gained a lot of attentions recently. However, traditional CMH methods usually apply batch learning for generating hash functions and codes. They are inefficient for the retrieval of web images which usually have streaming fashion. Online learning can be exploited for CMH. But existing online hashing methods still cannot solve two essential problems: Efficient updating of hash codes and analysis of cross-modal correlation. In this paper, we propose Online Cross-modal Hashing (OCMH) which can effectively address the above two problems by learning the …

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Online Advertising, Retail Platform Openness, And Long Tail Sellers, Jianqing Chen, Zhiling Guo Feb 2016

Online Advertising, Retail Platform Openness, And Long Tail Sellers, Jianqing Chen, Zhiling Guo

Research Collection School Of Computing and Information Systems

No abstract provided.

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Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu Feb 2016

Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

Distance metric learning (DML) is an important technique to improve similarity search in content-based image retrieval. Despite being studied extensively, most existing DML approaches typically adopt a single-modal learning framework that learns the distance metric on either a single feature type or a combined feature space where multiple types of features are simply concatenated. Such single-modal DML methods suffer from some critical limitations: (i) some type of features may significantly dominate the others in the DML task due to diverse feature representations; and (ii) learning a distance metric on the combined high-dimensional feature space can be extremely time-consuming using the …

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Exploring Heterogeneous Features For Query-Focused Summarization Of Categorized Community Answers, Wei Wei, Zhaoyan Ming, Liqiang Nie, Guohui Li, Jianjun Li, Feida Zhu, Tianfeng Shang, Changyin Luo Feb 2016

Exploring Heterogeneous Features For Query-Focused Summarization Of Categorized Community Answers, Wei Wei, Zhaoyan Ming, Liqiang Nie, Guohui Li, Jianjun Li, Feida Zhu, Tianfeng Shang, Changyin Luo

Research Collection School Of Computing and Information Systems

Community-based question answering (cQA) is a popular type of online knowledge-sharing web service where users ask questions and obtain answers contributed by others. To enhance knowledge sharing, cQA also provides users with a retrieval function to access the historical question-answer pairs (QAs). However, it is still ineffective in that the retrieval result is typically a ranking list of potentially relevant QAs, rather than a succinct and informative answer. To alleviate the problem, this paper proposes a three-level scheme, which aims to generate a query-focused summary-style answer in terms of two factors, i.e., novelty and redundancy. Specifically, we first retrieve a …

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Efficient Collective Spatial Keyword Query Processing On Road Networks, Yunjun Gao, Jingwen Zhao, Baihua Zheng, Gang Chen Feb 2016

Efficient Collective Spatial Keyword Query Processing On Road Networks, Yunjun Gao, Jingwen Zhao, Baihua Zheng, Gang Chen

Research Collection School Of Computing and Information Systems

The collective spatial keyword query (CSKQ), an important variant of spatial keyword queries, aims to find a set of the objects that collectively cover users' queried keywords, and those objects are close to the query location and have small inter-object distances. Existing works only focus on the CSKQ problem in the Euclidean space, although we observe that, in many real-life applications, the closeness of two spatial objects is measured by their road network distance. Thus, existing methods cannot solve the problem of network-based CSKQ efficiently. In this paper, we study the problem of collective spatial keyword query processing on road …

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Negative Factor: Improving Regular-Expression Matching In Strings, Xiaochun Yang, Tao Qiu, Bin Wang, Baihua Zheng, Yaoshu Wang, Chen Li Feb 2016

Negative Factor: Improving Regular-Expression Matching In Strings, Xiaochun Yang, Tao Qiu, Bin Wang, Baihua Zheng, Yaoshu Wang, Chen Li

Research Collection School Of Computing and Information Systems

The problem of finding matches of a regular expression (RE) on a string exists in many applications such as text editing, biosequence search, and shell commands. Existing techniques first identify candidates using substrings in the RE, then verify each of them using an automaton. These techniques become inefficient when there are many candidate occurrences that need to be verified. In this paper we propose a novel technique that prunes false negatives by utilizing negative factors, which are substrings that cannot appear in an answer. A main advantage of the technique is that it can be integrated with many existing algorithms …

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Mobile App Tagging, Ning Chen, Steven C. H. Hoi, Shaohua Li, Xiaokui Xiao Feb 2016

Mobile App Tagging, Ning Chen, Steven C. H. Hoi, Shaohua Li, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. …

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Accurate Online Video Tagging Via Probabilistic Hybrid Modeling, Jialie Shen, Meng Wang, Tat-Seng Chua Feb 2016

Accurate Online Video Tagging Via Probabilistic Hybrid Modeling, Jialie Shen, Meng Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Accurate video tagging has been becoming increasingly crucial for online video management and search. This article documents a novel framework called comprehensive video tagger (CVTagger) to facilitate accurate tag-based video annotation. The system applies both multimodal and temporal properties combined with a novel classification framework with hierarchical structure based on multilayer concept model and regression analysis. The advanced architecture enables effective incorporation of both video concept dependency and temporal dynamics. Using a large-scale test collection containing 50,000 YouTube videos, a set of empirical studies have been carried out and experimental results demonstrate various advantages of CVTagger over the state-of-the-art techniques.

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Insights From Machine-Learned Diet Success Prediction, Ingmar Weber, Palakorn Achananuparp Jan 2016

Insights From Machine-Learned Diet Success Prediction, Ingmar Weber, Palakorn Achananuparp

Research Collection School Of Computing and Information Systems

To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider “quantified self“ movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the …

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A Study On Singapore Haze, Bingtian Dai, Kasthuri Jayarajah, Ee-Peng Lim, Archan Misra, Shriguru Nayak Jan 2016

A Study On Singapore Haze, Bingtian Dai, Kasthuri Jayarajah, Ee-Peng Lim, Archan Misra, Shriguru Nayak

Research Collection School Of Computing and Information Systems

In 2015, Singaporean have experienced one of the worse air pollution crises in history. With datasets from a well-known photo sharing social network, we analyze how this haze affects Singaporean's daily life. We will share our preliminary results in this paper.

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Towards A Science Of Security Games, Thanh Hong Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, Milind Tambe Jan 2016

Towards A Science Of Security Games, Thanh Hong Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, Milind Tambe

Research Collection School Of Computing and Information Systems

Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types.Computational game theory can help design such security schedules. Indeed, casting the problem as a Bayesian Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to real-world use-inspired research in the …

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A Tool-Free Calibration Method For Turntable-Based 3d Scanning Systems, Xufang Pang, Rynson W.H. Lau, Zhan Song, Shengfeng He, Shengfeng He Jan 2016

A Tool-Free Calibration Method For Turntable-Based 3d Scanning Systems, Xufang Pang, Rynson W.H. Lau, Zhan Song, Shengfeng He, Shengfeng He

Research Collection School Of Computing and Information Systems

Turntable-based 3D scanners are popular but require calibration of the turntable axis. Existing methods for turntable calibration typically make use of specially designed tools, such as a chessboard or criterion sphere, which users must manually install and dismount. In this article, the authors propose an automatic method to calibrate the turntable axis without any calibration tools. Given a scan sequence of the input object, they first recover the initial rotation axis from an automatic registration step. Then they apply an iterative procedure to obtain the optimized turntable axis. This iterative procedure alternates between two steps: refining the initial pose of …

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Smart Ambient Sound Analysis Via Structured Statistical Modeling, Jialie Shen, Liqiang Nie, Tat Seng Chua Jan 2016

Smart Ambient Sound Analysis Via Structured Statistical Modeling, Jialie Shen, Liqiang Nie, Tat Seng Chua

Research Collection School Of Computing and Information Systems

In this paper, we introduce a novel framework called SASA (Smart Ambient Sound Analyser) to support different ambient audio mining tasks (e.g., audio classification and location estimation). To gain comprehensive ambient sound modelling, SASA extracts a variety of acoustic features from different sound components (e.g., music, voice and background), and translates them into structured information. This significantly enhances quality of audio content representation. Further, distinguished from existing approaches, SASA’s multilayered architecture seamlessly integrates mixture models and aPEGASOS (adaptive PEGASOS) SVM algorithm into a unified classification framework. The approach can leverage complimentary strengths of both models. Experimental results based on three …

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On Analyzing Geotagged Tweets For Location-Based Patterns, Philips Kokoh Prasetyo, Palakorn Achananuparp, Ee Peng Lim Jan 2016

On Analyzing Geotagged Tweets For Location-Based Patterns, Philips Kokoh Prasetyo, Palakorn Achananuparp, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Geotagged social media is becoming highly popular as social media access is now made very easy through a wide range of mobile apps which automatically detect and augment social media posts with geo-locations. In this paper, we analyze two kinds of location-based patterns. The first is the association between location attributes and the locations of user tweets. The second is location association pattern which comprises a pair of locations that are co-visited by users. We demonstrate that through tracking the Twitter data of Singapore-based users, we are able to reveal association between users tweeting from school locations and the school …

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Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan Jan 2016

Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan

Research Collection School Of Computing and Information Systems

We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper’s product level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper’s in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab (to …

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Privacy-Preserving And Verifiable Data Aggregation, Ngoc Hieu Tran, Robert H. Deng, Hwee Hwa Pang Jan 2016

Privacy-Preserving And Verifiable Data Aggregation, Ngoc Hieu Tran, Robert H. Deng, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

There are several recent research studies on privacy-preserving aggregation of time series data, where an aggregator computes an aggregation of multiple users' data without learning each individual's private input value. However, none of the existing schemes allows the aggregation result to be verified for integrity. In this paper, we present a new data aggregation scheme that protects user privacy as well as integrity of the aggregation. Towards this end, we first propose an aggregate signature scheme in a multi-user setting without using bilinear maps. We then extend the aggregate signature scheme into a solution for privacy-preserving and verifiable data aggregation. …

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Data Analytics On Consumer Behavior In Omni-Channel Retail Banking, Card And Payment Services, Geng Dan Jan 2016

Data Analytics On Consumer Behavior In Omni-Channel Retail Banking, Card And Payment Services, Geng Dan

Research Collection School Of Computing and Information Systems

Innovations in financial services have created challenges for banks that Information Systems (IS) research can address. My interests involve transaction cost theory, substitution and complementarity theory, and consumer informedness theory to understand consumer behavior and firm performance in the omni-channel world of digital banking. At a high level, my research inquiry asks: How can financial institutions take advantage of the deep insights that data analytics and management science modeling create on consumer behavior and channel management decision-making? And how can changes in payments and services in retail banking be understood in spatial and temporal terms? I am working on three …

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Online Arima Algorithms For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun Jan 2016

Online Arima Algorithms For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun

Research Collection School Of Computing and Information Systems

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. The idea of our ARIMA method is to …

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On Detecting Maximal Quasi Antagonistic Communities In Signed Graphs, Ming Gao, Ee-Peng Lim, David Lo, Philips Kokoh Prasetyo Jan 2016

On Detecting Maximal Quasi Antagonistic Communities In Signed Graphs, Ming Gao, Ee-Peng Lim, David Lo, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

Many networks can be modeled as signed graphs. These include social networks, and relationships/interactions networks. Detecting sub-structures in such networks helps us understand user behavior, predict links, and recommend products. In this paper, we detect dense sub-structures from a signed graph, called quasi antagonistic communities (QACs). An antagonistic community consists of two groups of users expressing positive relationships within each group but negative relationships across groups. Instead of requiring complete set of negative links across its groups, a QAC allows a small number of inter-group negative links to be missing. We propose an algorithm, Mascot, to find all maximal quasi …

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Top-K Dominating Queries On Incomplete Data, Xiaoye Miao, Yunjun Gao, Baihua Zheng, Gang Chen, Huiyong Cui Jan 2016

Top-K Dominating Queries On Incomplete Data, Xiaoye Miao, Yunjun Gao, Baihua Zheng, Gang Chen, Huiyong Cui

Research Collection School Of Computing and Information Systems

The top-k dominating (TKD) query returns the k objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-k queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first time, we carry out a systematic study of TKD queries on incomplete data, which involves the data having some missing dimensional value(s). We formalize this problem, and propose a suite of efficient algorithms for …

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Investigating The Influence Of Offline Friendship On Twitter Networking Behaviors, Young Soo Kim, Felicia Natali, Feida Zhu, Ee-Peng Lim Jan 2016

Investigating The Influence Of Offline Friendship On Twitter Networking Behaviors, Young Soo Kim, Felicia Natali, Feida Zhu, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We investigate the influence of offline friendship in three specific areas of Twitter networking behaviors: (a) network structure, (b) Twitter content and (c) interaction on Twitter. We observe some interesting findings through the empirical analysis of 2193 pairs of users who are online friends. When these pairs of users know each other offline, they are more likely to (1) respond to the online gesture of friendship from their friend, (2) share mutual online friends, (3) distribute and gather information in their friend’s Twitter network, (4) pay attention to their friend’s tweets, (5) post tweets that might be of interest to …

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Posting Topics ≠ Reading Topics: On Discovering Posting And Reading Topics In Social Media, Wei Gong, Ee-Peng Lim, Feida Zhu Jan 2016

Posting Topics ≠ Reading Topics: On Discovering Posting And Reading Topics In Social Media, Wei Gong, Ee-Peng Lim, Feida Zhu

Research Collection School Of Computing and Information Systems

Social media users make decisions about what content to post and read. As posted content is often visible to others, users are likely to impose self-censorship when deciding what content to post. On the other hand, such a concern may not apply to reading social media content. As a result, the topics of content that a user posted and read can be different and this has major implications to the applications that require personalization. To better determine and profile social media users’ topic interests, we conduct a user survey in Twitter. In this survey, participants chose the topics they like …

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A Comparison Of Fundamental Network Formation Principles Between Offline And Online Friends On Twitter, Felicia Natali, Feida Zhu Jan 2016

A Comparison Of Fundamental Network Formation Principles Between Offline And Online Friends On Twitter, Felicia Natali, Feida Zhu

Research Collection School Of Computing and Information Systems

We investigate the differences between how some of the fundamental principles of network formation apply among offline friends and how they apply among online friends on Twitter. We consider three fundamental principles of network formation proposed by Schaefer et al.: reciprocity, popularity, and triadic closure. Overall, we discover that these principles mainly apply to offline friends on Twitter. Based on how these principles apply to offline versus online friends, we formulate rules to predict offline friendship on Twitter. We compare our algorithm with popular machine learning algorithms and Xiewei’s random walk algorithm. Our algorithm beats the machine learning algorithms on …

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Information Source Detection Via Maximum A Posteriori Estimation, Biao Chang, Feida Zhu, Enhong Chen, Qi. Liu Jan 2016

Information Source Detection Via Maximum A Posteriori Estimation, Biao Chang, Feida Zhu, Enhong Chen, Qi. Liu

Research Collection School Of Computing and Information Systems

The problem of information source detection, whose goal is to identify the source of a piece of information from a diffusion process (e.g., computer virus, rumor, epidemic, and so on), has attracted ever-increasing attention from research community in recent years. Although various methods have been proposed, such as those based on centrality, spectral and belief propagation, the existing solutions still suffer from high time complexity and inadequate effectiveness. To this end, we revisit this problem in the paper and present a comprehensive study from the perspective of likelihood approximation. Different from many previous works, we consider both infected and uninfected …

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