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Full-Text Articles in Entire DC Network
Towards Opinion Summarization From Online Forums, Ding Ying, Jing Jiang
Towards Opinion Summarization From Online Forums, Ding Ying, Jing Jiang
Research Collection School Of Computing and Information Systems
Summarizing opinions expressed in online forums can potentially benefit many people. However, special characteristics of this problem may require changes to standard text summarization techniques. In this work, we present our initial attempt at extractive summarization of opinionated online forum threads. Given the nature of user generated content in online discussion forums, we hypothesize that besides relevance, text quality and subjectivity also play important roles in deciding which sentences are good summary sentences. We therefore construct an annotated corpus to facilitate our study of extractive summarization of online discussion forums. We define a set of features to capture relevance, text …
A Joint Model Of Product Properties, Aspects And Ratings For Online Reviews, Ding Ying, Jing Jiang
A Joint Model Of Product Properties, Aspects And Ratings For Online Reviews, Ding Ying, Jing Jiang
Research Collection School Of Computing and Information Systems
Product review mining is an important task that can benefit both businesses and consumers. Lately a number of models combining collaborative filtering and content analysis to model reviews have been proposed, among which the Hidden Factors as Topics (HFT) model is a notable one. In this work, we propose a new model on top of HFT to separate product properties and aspects. Product properties are intrinsic to certain products (e.g. types of cuisines of restaurants) whereas aspects are dimensions along which products in the same category can be compared (e.g. service quality of restaurants). Our proposed model explicitly separates the …
How Practitioners Perceive The Relevance Of Software Engineering Research, David Lo, Nachiappan Nagappan, Thomas Zimmermann
How Practitioners Perceive The Relevance Of Software Engineering Research, David Lo, Nachiappan Nagappan, Thomas Zimmermann
Research Collection School Of Computing and Information Systems
The number of software engineering research papers over the last few years has grown significantly. An important question here is: how relevant is software engineering research to practitioners in the field? To address this question, we conducted a survey at Microsoft where we invited 3,000 industry practitioners to rate the relevance of research ideas contained in 571 ICSE, ESEC/FSE and FSE papers that were published over a five year period. We received 17,913 ratings by 512 practitioners who labelled ideas as essential, worthwhile, unimportant, or unwise. The results from the survey suggest that practitioners are positive towards studies done by …
Tweet Sentiment: From Classification To Quantification, Wei Gao, Fabrizio Sebastiani
Tweet Sentiment: From Classification To Quantification, Wei Gao, Fabrizio Sebastiani
Research Collection School Of Computing and Information Systems
Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. "prevalence") …
The Autoproof Verifier: Usability By Non-Experts And On Standard Code, Carlo A. Furia, Christopher M. Poskitt, Julian Tschannen
The Autoproof Verifier: Usability By Non-Experts And On Standard Code, Carlo A. Furia, Christopher M. Poskitt, Julian Tschannen
Research Collection School Of Computing and Information Systems
Formal verification tools are often developed by experts for experts; as a result, their usability by programmers with little formal methods experience may be severely limited. In this paper, we discuss this general phenomenon with reference to AutoProof: a tool that can verify the full functional correctness of object-oriented software. In particular, we present our experiences of using AutoProof in two contrasting contexts representative of non-expert usage. First, we discuss its usability by students in a graduate course on software verification, who were tasked with verifying implementations of various sorting algorithms. Second, we evaluate its usability in verifying code developed …
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 …
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 …
Fast Object Retrieval Using Direct Spatial Matching, Zhiyuan Zhong, Jianke Zhu, Steven C. H. Hoi
Fast Object Retrieval Using Direct Spatial Matching, Zhiyuan Zhong, Jianke Zhu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The conventional bag-of-visual-words (BoW) model is popular for the large-scale object retrieval system but suffers from the critical drawback of ignoring spatial information. RANSAC-based methods attempt to remedy this drawback, but often require traversing all the feature matches for each hypothesis, leading to the heavy computational cost which limits the number of gallery images to be verified for each online query. We propose an efficient direct spatial matching (DSM) approach to directly estimate the scale variation using region sizes, in which all feature matches voted for estimating geometric transformation. DSM is much faster than RANSAC-based methods and exhaustive enumeration approaches. …
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 …
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 …
Gibberish, Assistant, Or Master? Using Tweets Linking To News For Extractive Single-Document Summarization, Zhongyu Wei, Wei Gao
Gibberish, Assistant, Or Master? Using Tweets Linking To News For Extractive Single-Document Summarization, Zhongyu Wei, Wei Gao
Research Collection School Of Computing and Information Systems
Single-document summarization is a challenging task. In this paper, we explore effective ways using the tweets linking to news for generating extractive summary of each document. We reveal the very basic value of tweets that can be utilized by regarding every tweet as a vote for candidate sentences. Base on such finding, we resort to unsupervised summarization models by leveraging the linking tweets to master the ranking of candidate extracts via random walk on a heterogeneous graph. The advantage is that we can use the linking tweets to opportunistically "supervise" the summarization with no need of reference summaries. Furthermore, we …
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 …
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 …
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 …
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.
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 …
Topological Spatial Verification For Instance Search, Wei Zhang, Chong-Wah Ngo
Topological Spatial Verification For Instance Search, Wei Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper proposes an elastic spatial verification method for Instance Search, particularly for dealing with non-planar and non-rigid queries exhibiting complex spatial transformations. Different from existing models that map keypoints between images based on a linear transformation (e.g., affine, homography), our model exploits the topological arrangement of keypoints to address the non-linear spatial transformations that are extremely common in real life situations. In particular, we propose a novel technique to elastically verify the topological spatial consistency with the triangulated graph through a "sketch-and-match" scheme. The spatial topology configuration, emphasizing relative positioning rather than absolute coordinates, is first sketched by a …
Semi-Supervised Hashing With Semantic Confidence For Large Scale Visual Search, Yingwei Pan, Ting Yao, Houqiang Li, Chong-Wah Ngo, Tao Mei
Semi-Supervised Hashing With Semantic Confidence For Large Scale Visual Search, Yingwei Pan, Ting Yao, Houqiang Li, Chong-Wah Ngo, Tao Mei
Research Collection School Of Computing and Information Systems
Similarity search is one of the fundamental problems for large scale multimedia applications. Hashing techniques, as one popular strategy, have been intensively investigated owing to the speed and memory efficiency. Recent research has shown that leveraging supervised information can lead to high quality hashing. However, most existing supervised methods learn hashing function by treating each training example equally while ignoring the different semantic degree related to the label, i.e. semantic confidence, of different examples. In this paper, we propose a novel semi-supervised hashing framework by leveraging semantic confidence. Specifically, a confidence factor is first assigned to each example by neighbor …
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 …
Massive Open Online Networks For Urban Sensing: Design, Deployment And Challenges, Hwee Xian Tan, Hwee-Pink Tan
Massive Open Online Networks For Urban Sensing: Design, Deployment And Challenges, Hwee Xian Tan, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Recent escalating efforts to realize the vision of smart cities have become a global trend. Within such efforts, the setup of relevant infrastructure is necessary to enable scalable sensing and sense-making in urban environments. In this paper, we discuss the design and implementation of MOON - Massive Open Online Networks - to interconnect large-scale and diverse IoT devices to enable the acquisition of real-time sensing data. We describe the system architecture, and software protocols that form the components of MOON. In addition, we highlight the challenges in the deployment and management of such networks. These are derived through experiences gleaned …
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 …
Memes As Building Blocks: A Case Study On Evolutionary Optimization + Transfer Learning For Routing Problems, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Ivor W. Tsang
Memes As Building Blocks: A Case Study On Evolutionary Optimization + Transfer Learning For Routing Problems, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Ivor W. Tsang
Research Collection School Of Computing and Information Systems
A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Particularly, present evolutionary optimization approaches generally start their search from scratch or the ground-zero state of knowledge, independent of how similar the given new problem of interest is to those optimized previously. There has thus been the apparent lack of automated knowledge transfers and reuse across problems. Taking this cue, this paper presents a Memetic Computational Paradigm based on Evolutionary Optimization + Transfer Learning for search, one that models how human solves problems, and embarks on …
Neural Modeling Of Sequential Inferences And Learning Over Episodic Memory, Budhitama Subagdja, Ah-Hwee Tan
Neural Modeling Of Sequential Inferences And Learning Over Episodic Memory, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Episodic memory is a significant part of cognition for reasoning and decision making. Retrieval in episodic memory depends on the order relationships of memory items which provides flexibility in reasoning and inferences regarding sequential relations for spatio-temporal domain. However, it is still unclear how they are encoded and how they differ from representations in other types of memory like semantic or procedural memory. This paper presents a neural model of sequential representation and inferences on episodic memory. It contrasts with the common views on sequential representation in neural networks that instead of maintaining transitions between events to represent sequences, they …
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 …
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" …
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.
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 …