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Articles 5401 - 5430 of 8479
Full-Text Articles in Computer Sciences
Cooperation In Delay-Tolerant Networks With Wireless Energy Transfer: Performance Analysis And Optimization, Dusit Niyato, Ping Wang, Hwee-Pink Tan, Walid Saad, Dong In Kim
Cooperation In Delay-Tolerant Networks With Wireless Energy Transfer: Performance Analysis And Optimization, Dusit Niyato, Ping Wang, Hwee-Pink Tan, Walid Saad, Dong In Kim
Research Collection School Of Computing and Information Systems
We consider a delay-tolerant network (DTN) whose mobile nodes are assigned to collect packets from data sources and deliver them to a sink (i.e., a gateway). Each mobile node operates by using energy transferred wirelessly from the gateway. For such a network, two main issues are studied. First, when a mobile node is at the data source, this node must decide on whether to accept the packet received from the data source or not. In contrast, whenever a mobile node is at the gateway, it has to decide on whether to transmit the packets collected from the data sources or …
Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu
Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu
Research Collection School Of Computing and Information Systems
As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …
Fusing Heterogeneous Data For Alzheimer's Disease Classification, P. S. Pillai, Tze-Yun Leong
Fusing Heterogeneous Data For Alzheimer's Disease Classification, P. S. Pillai, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers. This work combines the features from neuroimaging and cerebrospinal fluid studies to distinguish Alzheimer's disease patients from healthy subjects. We apply statistical data fusion techniques on 101 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We examine whether fusion of biomarkers helps to improve diagnostic …
On Mining Lifestyles From User Trip Data, Meng-Fen Chiang, Ee-Peng Lim
On Mining Lifestyles From User Trip Data, Meng-Fen Chiang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Large cities today are facing major challenges in planning and policy formulation to keep their growth sustainable. In this paper, we aim to gain useful insights about people living in a city by developing novel models to mine user lifestyles represented by the users' activity centers. Two models, namely ACMM and ACHMM, have been developed to learn the activity centers of each user using a large dataset of bus and subway train trips performed by passengers in Singapore. We show that ACHMM and ACMM yield similar accuracies in location prediction task. We also propose methods to automatically predict "home", "work" …
Deep Learning For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Yun Zhang, Jianling Sun
Deep Learning For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Yun Zhang, Jianling Sun
Research Collection School Of Computing and Information Systems
Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of …
Event Detection: Exploiting Socio-Physical Interactions In Physical Spaces, Kasthuri Jayarajah, Archan Misra, Xiao-Wen Ruan, Ee-Peng Lim
Event Detection: Exploiting Socio-Physical Interactions In Physical Spaces, Kasthuri Jayarajah, Archan Misra, Xiao-Wen Ruan, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
This paper investigates how digital traces of people's movements and activities in the physical world (e.g., at college campuses and commutes) may be used to detect local, short-lived events in various urban spaces. Past work that use occupancy-related features can only identify high-intensity events (those that cause large-scale disruption in visit patterns). In this paper, we first show how longitudinal traces of the coordinated and group-based movement episodes obtained from individual-level movement data can be used to create a socio-physical network (with edges representing tie strengths among individuals based on their physical world movement & collocation behavior). We then investigate …
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Research Collection School Of Computing and Information Systems
In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we …
Sails: Hybrid Algorithm For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Sails: Hybrid Algorithm For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Research Collection School Of Computing and Information Systems
The Team Orienteering Problem with Time Windows (TOPTW) is the extended version of the Orienteering Problem where each node is limited by a given time window. The objective is to maximize the total collected score from a certain number of paths. In this paper, a hybridization of Simulated Annealing and Iterated Local Search, namely SAILS, is proposed to solve the TOPTW. The efficacy of the proposed algorithm is tested using benchmark instances. The results show that the proposed algorithm is competitive with the state-of-the-art algorithms in the literature. SAILS is able to improve the best known solutions for 19 benchmark …
Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato
Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato
Research Collection School Of Computing and Information Systems
Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized leaerning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding …
Applying Geocaching Principles To Site-Based Citizen Science And Eliciting Reactions Via A Technology Probe, Matthew A. Dunlap, Anthony Tang, Saul Greenberg
Applying Geocaching Principles To Site-Based Citizen Science And Eliciting Reactions Via A Technology Probe, Matthew A. Dunlap, Anthony Tang, Saul Greenberg
Research Collection School Of Computing and Information Systems
Site-based citizen science occurs when volunteers work with scientists to collect data at particular field locations. The benefit is greater data collection at lesser cost. Yet difficulties exist. We developed SCIENCECACHING, a prototype citizen science aid designed to mitigate four specific problems by applying aspects from another thriving location-based activity: geocaching as enabled by mobile devices. Specifically, to ease problems in data collection, SCIENCECACHING treats sites as geocaches: Volunteers find sites opportunistically via geocaching methods and use equipment and other materials pre-stored in cache containers. To ease problems in data validation, SCIENCECACHING flags outlier data as it is entered so …
Identity-Based Lossy Encryption From Learning With Errors, Jingnan He, Bao Li, Xianhui Lu, Dingding Jia, Haiyang Xue, Xiaochao Sun
Identity-Based Lossy Encryption From Learning With Errors, Jingnan He, Bao Li, Xianhui Lu, Dingding Jia, Haiyang Xue, Xiaochao Sun
Research Collection School Of Computing and Information Systems
We extend the notion of lossy encryption to the scenario of identity-based encryption (IBE), and propose a new primitive called identity-based lossy encryption (IBLE). Similar as the case of lossy encryption, we show that IBLE can also achieve selective opening security. Finally, we present a construction of IBLE from the assumption of learning with errors.
Efficacy Of Social Media Utilization By Public Accounting Firms: Findings And Directions For Future Research, B. Eschenbrenner, Fiona Fui-Hoon Nah, V. Telaprolu
Efficacy Of Social Media Utilization By Public Accounting Firms: Findings And Directions For Future Research, B. Eschenbrenner, Fiona Fui-Hoon Nah, V. Telaprolu
Research Collection School Of Computing and Information Systems
Social media presents a new platform for businesses to communicate and interact with others, both internally and externally. Social media may be utilized for activities such as sharing success stories and providing industry updates. Although a plethora of opportunities to achieve business objectives with social media usage exists, the efficacy of its use by public accounting firms is unclear. This article identifies the business objectives that Big 4 and second-tier firms are pursuing with social media. Primary business objectives being fulfilled by social media include Knowledge Sharing, Branding and Marketing, and Socialization and Onboarding. The findings suggest that Big 4 …
Effects Of The Use Of Points, Leaderboards And Badges On In-Game Purchases Of Virtual Goods, Fiona Fui-Hoon Nah, Lakshmi Sushma Daggubati, Amith Tarigonda, Raghu Vinay Nuvvula, Ofir Turel
Effects Of The Use Of Points, Leaderboards And Badges On In-Game Purchases Of Virtual Goods, Fiona Fui-Hoon Nah, Lakshmi Sushma Daggubati, Amith Tarigonda, Raghu Vinay Nuvvula, Ofir Turel
Research Collection School Of Computing and Information Systems
Game design elements are major factors in gamification. In this study, we seek to examine the impact of game design elements on users’ in-game purchases of virtual goods. The purchase of virtual goods due to players’ intrinsic motivation has been studied but little is known about the purchase of virtual goods due to the use of game design elements (i.e., Points, Leaderboards and Badges) built into the games. Extending our knowledge to this realm can help researchers to better understand gamers’ behaviors, and game designers and marketers to better promote and sell virtual goods in online games.
Creating Greater Synergy Between Hci Academia And Practice, Fiona Fui-Hoon Nah, Dennis Galletta, Melinda Knight, James R. Lewis, John Pruitt, Gavriel Salvendy, Hong Sheng, Anna Wichansky
Creating Greater Synergy Between Hci Academia And Practice, Fiona Fui-Hoon Nah, Dennis Galletta, Melinda Knight, James R. Lewis, John Pruitt, Gavriel Salvendy, Hong Sheng, Anna Wichansky
Research Collection School Of Computing and Information Systems
This paper presents perspectives from both academia and practice on how both groups can collaborate and work together to create synergy in the development and advancement of human-computer interaction (HCI). Issues and challenges are highlighted, success cases are offered as examples, and suggestions are provided to further such collaborations.
Facilitating Image Search With A Scalable And Compact Semantic Mapping, Meng Wang, Weisheng Li, Dong Liu, Bingbing Ni, Jialie Shen, Shuicheng Yan
Facilitating Image Search With A Scalable And Compact Semantic Mapping, Meng Wang, Weisheng Li, Dong Liu, Bingbing Ni, Jialie Shen, Shuicheng Yan
Research Collection School Of Computing and Information Systems
This paper introduces a novel approach to facilitating image search based on a compact semantic embedding. A novel method is developed to explicitly map concepts and image contents into a unified latent semantic space for the representation of semantic concept prototypes. Then, a linear embedding matrix is learned that maps images into the semantic space, such that each image is closer to its relevant concept prototype than other prototypes. In our approach, the semantic concepts equated with query keywords and the images mapped into the vicinity of the prototype are retrieved by our scheme. In addition, a computationally efficient method …
Apparatus And Method For Determining The Location Of A Mobile Device Using Multiple Wireless Access Points, Kyle Jamieson, Jie Xiong
Apparatus And Method For Determining The Location Of A Mobile Device Using Multiple Wireless Access Points, Kyle Jamieson, Jie Xiong
Research Collection School Of Computing and Information Systems
A method and apparatus are provided for determining the location of a mobile device using multiple wireless access points, each wireless access point comprising multiple antennas. The method comprises receiving a communication signal from the mobile device at said multiple antennas of said multiple wireless access points. For each wireless access point, angle-of-arrival information of the received communication signal at the wireless access point is determined, based on a difference in phase of the received signal between different antennas. The determined angle-of-arrival information for the received communication signal from the mobile device is then collected from each of the multiple …
A Study On The Geographical Distribution Of Brazil’S Prestigious Software Developers, Fernando Figueira Filho, Marcelo Gattermann Perin, Christoph Treude, Sabrina Marczak, Leandro De Almeida Melo, Igor Marques Da Silva, Lucas Bibiano Dos Santos
A Study On The Geographical Distribution Of Brazil’S Prestigious Software Developers, Fernando Figueira Filho, Marcelo Gattermann Perin, Christoph Treude, Sabrina Marczak, Leandro De Almeida Melo, Igor Marques Da Silva, Lucas Bibiano Dos Santos
Research Collection School Of Computing and Information Systems
Brazil is an emerging economy with many IT initiatives from public and private sectors. To evaluate the progress of such initiatives, we study the geographical distribution of software developers in Brazil, in particular which of the Brazilian states succeed the most in attracting and nurturing them. We compare the prestige of developers with socio-economic data and find that (i) prestigious developers tend to be located in the most economically developed regions of Brazil, (ii) they are likely to follow others in the same state they are located in, (iii) they are likely to follow other prestigious developers, and (iv) they …
Topic Modeling With Document Relative Similarities, Jianguang Du, Jing Jiang, Dandan Song, Lejian Liao
Topic Modeling With Document Relative Similarities, Jianguang Du, Jing Jiang, Dandan Song, Lejian Liao
Research Collection School Of Computing and Information Systems
Topic modeling has been widely used in text mining. Previous topic models such as Latent Dirichlet Allocation (LDA) are successful in learning hidden topics but they do not take into account metadata of documents. To tackle this problem, many augmented topic models have been proposed to jointly model text and metadata. But most existing models handle only categorical and numerical types of metadata. We identify another type of metadata that can be more natural to obtain in some scenarios. These are relative similarities among documents. In this paper, we propose a general model that links LDA with constraints derived from …
Detection And Classification Of Malicious Javascript Via Attack Behavior Modelling, Yinxing Xue, Junjie Wang, Yang Liu, Hao Xiao, Jun Sun, Mahinthan Chandramohan
Detection And Classification Of Malicious Javascript Via Attack Behavior Modelling, Yinxing Xue, Junjie Wang, Yang Liu, Hao Xiao, Jun Sun, Mahinthan Chandramohan
Research Collection School Of Computing and Information Systems
Existing malicious JavaScript (JS) detection tools and commercial anti-virus tools mostly use feature-based or signature-based approaches to detect JS malware. These tools are weak in resistance to obfuscation and JS malware variants, not mentioning about providing detailed information of attack behaviors. Such limitations root in the incapability of capturing attack behaviors in these approches. In this paper, we propose to use Deterministic Finite Automaton (DFA) to abstract and summarize common behaviors of malicious JS of the same attack type. We propose an automatic behavior learning framework, named JS∗ , to learn DFA from dynamic execution traces of JS malware, where …
On Multipath Link Characterization And Adaptation For Device-Free Human Detection, Zimu Zhou, Zheng Yang, Chenshu Wu, Yunhao Liu, Lionel M. Ni
On Multipath Link Characterization And Adaptation For Device-Free Human Detection, Zimu Zhou, Zheng Yang, Chenshu Wu, Yunhao Liu, Lionel M. Ni
Research Collection School Of Computing and Information Systems
No abstract provided.
Privacy-Preserving Offloading Of Mobile App To The Public Cloud, Yue Duan, Mu Zhang, Heng Yin, Yuzhe Tang
Privacy-Preserving Offloading Of Mobile App To The Public Cloud, Yue Duan, Mu Zhang, Heng Yin, Yuzhe Tang
Research Collection School Of Computing and Information Systems
To support intensive computations on resource-restricting mobile devices, studies have been made to enable the offloading of a part of a mobile program to the cloud. However, none of the existing approaches considers user privacy when transmitting code and data off the device, resulting in potential privacy breach. In this paper, we present the design and implementation of a system that automatically performs fine-grained privacy-preserving Android app offloading. It utilizes static analysis and bytecode instrumentation techniques to ensure transparent and efficient Android app offloading while preserving user privacy. We evaluate the effectiveness and performance of our system using two Android …
Log-Euclidean Metric Learning On Symmetric Positive Definite Manifold With Application To Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Li, X. Chen
Log-Euclidean Metric Learning On Symmetric Positive Definite Manifold With Application To Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Li, X. Chen
Research Collection School Of Computing and Information Systems
The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with Log-Euclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geometrical structure of the space of SPD matrix logarithms but also brings low efficiency especially when the dimensionality of SPD matrix is high. To overcome this limitation, we propose a novel metric learning approach to work directly on logarithms of SPD matrices. Specifically, our method aims to learn a tangent map that can directly transform the …
Optimizing Selection Of Competing Features Via Feedback-Directed Evolutionary Algorithms, Tian Huat Tan, Yinxing Xue, Manman Chen, Jun Sun, Yang Liu, Jin Song Dong Dong
Optimizing Selection Of Competing Features Via Feedback-Directed Evolutionary Algorithms, Tian Huat Tan, Yinxing Xue, Manman Chen, Jun Sun, Yang Liu, Jin Song Dong Dong
Research Collection School Of Computing and Information Systems
Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalities and competing variabilities of the product variants in software family or Software Product Line (SPL). A key challenge for deriving a new product is to find a set of features that do not have inconsistencies or conflicts, yet optimize multiple objectives (e.g., minimizing cost and maximizing number of features), which are often competing with each other. Existing works have attempted to make use of evolutionary algorithms (EAs) to address this problem. In …
Reliability Assessment For Distributed Systems Via Communication Abstraction And Refinement, Lin Gui, Jun Sun, Yang Liu, Jin Song Dong
Reliability Assessment For Distributed Systems Via Communication Abstraction And Refinement, Lin Gui, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Distributed systems like cloud-based services are ever more popular. Assessing the reliability of distributed systems is highly non-trivial. Particularly, the order of executions among distributed components adds a dimension of non-determinism, which invalidates existing reliability assessment methods based on Markov chains. Probabilistic model checking based on models like Markov decision processes is designed to deal with scenarios involving both probabilistic behavior (e.g., reliabilities of system components) and non-determinism. However, its application is currently limited by state space explosion, which makes reliability assessment of distributed system particularly difficult. In this work, we improve the probabilistic model checking through a method of …
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) with outsourced decryption not only enables fine-grained sharing of encrypted data, but also overcomes the efficiency drawback (in terms of ciphertext size and decryption cost) of the standard ABE schemes. In particular, an ABE scheme with outsourced decryption allows a third party (e.g., a cloud server) to transform an ABE ciphertext into a (short) El Gamal-type ciphertext using a public transformation key provided by a user so that the latter can be decrypted much more efficiently than the former by the user. However, a shortcoming of the original outsourced ABE scheme is that the correctness of the …
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) with outsourced decryption not only enables fine-grained sharing of encrypted data, but also overcomes the efficiency drawback (in terms of ciphertext size and decryption cost) of the standard ABE schemes. In particular, an ABE scheme with outsourced decryption allows a third party (e.g., a cloud server) to transform an ABE ciphertext into a (short) El Gamal-type ciphertext using a public transformation key provided by a user so that the latter can be decrypted much more efficiently than the former by the user. However, a shortcoming of the original outsourced ABE scheme is that the correctness of the …
Fast Optimal Aggregate Point Search For A Merged Set On Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu, Weimo Liu, Yan Huang
Fast Optimal Aggregate Point Search For A Merged Set On Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu, Weimo Liu, Yan Huang
Research Collection School Of Computing and Information Systems
Aggregate nearest neighbor query, which returns an optimal target point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes two algorithms to process MANN query on road networks when aggregate function is max. Then, we extend the algorithms …
Active Semi-Supervised Approach For Checking App Behavior Against Its Description, Ma Siqi, Shaowei Wang, David Lo, Deng, Robert H., Cong Sun
Active Semi-Supervised Approach For Checking App Behavior Against Its Description, Ma Siqi, Shaowei Wang, David Lo, Deng, Robert H., Cong Sun
Research Collection School Of Computing and Information Systems
Mobile applications are popular in recent years. They are often allowed to access and modify users' sensitive data. However, many mobile applications are malwares that inappropriately use these sensitive data. To detect these malwares, Gorla et al. Propose CHABADA which compares app behaviors against its descriptions. Data about known malwares are not used in their work, which limits its effectiveness. In this work, we extend the work by Gorla et al. By proposing an active and semi-supervised approach for detecting malwares. Different from CHABADA, our approach will make use of both known benign and malicious apps to predict other malicious …
A Convolution Kernel Approach To Identifying Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
A Convolution Kernel Approach To Identifying Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Comparisons in text, such as in online reviews, serve as useful decision aids. In this paper, we focus on the task of identifying whether a comparison exists between a specific pair of entity mentions in a sentence. This formulation is transformative, as previous work only seeks to determine whether a sentence is comparative, which is presumptuous in the event the sentence mentions multiple entities and is comparing only some, not all, of them. Our approach leverages not only lexical features such as salient words, but also structural features expressing the relationships among words and entity mentions. To model these features …
Message Passing For Collective Graphical Models, Tao Sun, Daniel Sheldon, Akshat Kumar
Message Passing For Collective Graphical Models, Tao Sun, Daniel Sheldon, Akshat Kumar
Research Collection School Of Computing and Information Systems
Collective graphical models (CGMs) are a formalism for inference and learning about a population of independent and identically distributed individuals when only noisy aggregate data are available. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in standard graphical models. The connection leads us to derive a novel Belief Propagation (BP) style algorithm for collective graphical models. Mathematically, the algorithm is a strict generalization of BP—it can be viewed as an extension to minimize the Bethe free energy plus additional energy terms that are non-linear functions of the marginals. For CGMs, the algorithm is much …