Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).Comment: published in KDD'1

A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction

Probabilistic approaches for tensor factorization aim to extract meaningful structure from incomplete data by postulating low rank constraints. Recently, variational Bayesian (VB) inference techniques have successfully been applied to large scale models. This paper presents full Bayesian inference via VB on both single and coupled tensor factorization models. Our method can be run even for very large models and is easily implemented. It exhibits better prediction performance than existing approaches based on maximum likelihood on several real-world datasets for missing link prediction problem.Comment: arXiv admin note: substantial text overlap with arXiv:1409.808

ReviewQA: a relational aspect-based opinion reading dataset

Deep reading models for question-answering have demonstrated promising performance over the last couple of years. However current systems tend to learn how to cleverly extract a span of the source document, based on its similarity with the question, instead of seeking for the appropriate answer. Indeed, a reading machine should be able to detect relevant passages in a document regarding a question, but more importantly, it should be able to reason over the important pieces of the document in order to produce an answer when it is required. To motivate this purpose, we present ReviewQA, a question-answering dataset based on hotel reviews. The questions of this dataset are linked to a set of relational understanding competencies that we expect a model to master. Indeed, each question comes with an associated type that characterizes the required competency. With this framework, it is possible to benchmark the main families of models and to get an overview of what are the strengths and the weaknesses of a given model on the set of tasks evaluated in this dataset. Our corpus contains more than 500.000 questions in natural language over 100.000 hotel reviews. Our setup is projective, the answer of a question does not need to be extracted from a document, like in most of the recent datasets, but selected among a set of candidates that contains all the possible answers to the questions of the dataset. Finally, we present several baselines over this dataset.Comment: Accepted at Conf\'erence sur l'apprentissage automatique (CAp 2018

Source Code Properties of Defective Infrastructure as Code Scripts

Context: In continuous deployment, software and services are rapidly deployed to end-users using an automated deployment pipeline. Defects in infrastructure as code (IaC) scripts can hinder the reliability of the automated deployment pipeline. We hypothesize that certain properties of IaC source code such as lines of code and hard-coded strings used as configuration values, show correlation with defective IaC scripts. Objective: The objective of this paper is to help practitioners in increasing the quality of infrastructure as code (IaC) scripts through an empirical study that identifies source code properties of defective IaC scripts. Methodology: We apply qualitative analysis on defect-related commits mined from open source software repositories to identify source code properties that correlate with defective IaC scripts. Next, we survey practitioners to assess the practitioner's agreement level with the identified properties. We also construct defect prediction models using the identified properties for 2,439 scripts collected from four datasets. Results: We identify 10 source code properties that correlate with defective IaC scripts. Of the identified 10 properties we observe lines of code and hard-coded string to show the strongest correlation with defective IaC scripts. Hard-coded string is the property of specifying configuration value as hard-coded string. According to our survey analysis, majority of the practitioners show agreement for two properties: include, the property of executing external modules or scripts, and hard-coded string. Using the identified properties, our constructed defect prediction models show a precision of 0.70~0.78, and a recall of 0.54~0.67.Comment: arXiv admin note: text overlap with arXiv:1809.0793

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

Comment on Clark et al. (2019) "The Physical Nature of Neutral Hydrogen Intensity Structure"

A recent publication by Clark et.al (2019, CX19) uses both GALFA-HI observational data and numerical simulations to address the nature of intensity fluctuations in Position-Position-Velocity (PPV) space. The study questions the validity and applicability of the statistical theory of PPV space fluctuations formulated in Lazarian & Pogosyan (2000, LP00) to HI gas and concludes that $"${\it a significant reassessment of many observational and theoretical studies of turbulence in HI}$"$. This implies that dozens of papers that used LP00 theory to explore interstellar turbulence as well as the ongoing research based on LP00 theory are in error. This situation motivates the urgency of our public response. In our Comment we explain why we believe the criticism in CX19 is based on the incorrect understanding of the LP00 theory. In particular, we illustrate that the correlation between PPV slices and dust emissions in CX19 does not properly reveal the relative importance of velocity and density fluctuations in velocity channel maps. While CX19 provides an explanation of the change of the spectral index with respect to the thickness of PPV slice based on the two-phase nature of H1 gas, we failed to see any observational support for this idea. On the contrary, we show that the observations both in two-phase HI and one phase CO show similar results. Moreover, the observed change is in good agreement with LP00 predictions and spectral indexes of velocity and density spectra that are obtained following LP00 procedures are in good agreement with the numerically confirmed expectations of compressible MHD turbulence theory. In short, we could not find any justification of the criticism of LP00 theory that is provided in CX19. On the contrary, our analysis testifies that both available observational and numerical data agree well with the predictions of LP00 theory.Comment: 10 pages, 4 figure

Asterias: a parallelized web-based suite for the analysis of expression and aCGH data

Asterias (\url{http://www.asterias.info}) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI). Most of our applications allow the user to obtain additional information for user-selected genes by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data; converting between different types of gene/clone and protein identifiers; filtering and imputation; finding differentially expressed genes related to patient class and survival data; searching for models of class prediction; using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity; searching for molecular signatures and predictive genes with survival data; detecting regions of genomic DNA gain or loss. The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.Comment: web based application; 3 figure

Noise and vibration from building-mounted micro wind turbines Part 1: Review and proposed methodology

Description To research the quantification of vibration from a micro turbine, and to develop a method of prediction of vibration and structure borne noise in a wide variety of installations in the UK. Objective The objectives of the study are as follows: 1) Develop a methodology to quantify the amount of source vibration from a building mounted micro wind turbine installation, and to predict the level of vibration and structure-borne noise impact within such buildings in the UK. 2) Test and validate the hypothesis on a statically robust sample size 3) Report the developed methodology in a form suitable for widespread adoption by industry and regulators, and report back on the suitability of the method on which to base policy decisions for a future inclusion for building mounted turbines in the GPDO

Towards using social media to identify individuals at risk for preventable chronic illness

We describe a strategy for the acquisition of training data necessary to build a social-media-driven early detection system for individuals at risk for (preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter. The questions are designed to inspire participant engagement and collect relevant data to train a public-health model applied to individuals. Prior systems designed to use social media such as Twitter to predict obesity (a risk factor for T2DM) operate on entire communities such as states, counties, or cities, based on statistics gathered by government agencies. Because there is considerable variation among individuals within these groups, training data on the individual level would be more effective, but this data is difficult to acquire. The approach proposed here aims to address this issue. Our strategy has two steps. First, we trained a random forest classifier on data gathered from (public) Twitter statuses and state-level statistics with state-of-the-art accuracy. We then converted this classifier into a 20-questions-style quiz and made it available online. In doing so, we achieved high engagement with individuals that took the quiz, while also building a training set of voluntarily supplied individual-level data for future classification.Comment: This paper will appear in LREC 201

What is the Nature of Chinese MicroBlogging: Unveiling the Unique Features of Tencent Weibo

China has the largest number of online users in the world and about 20% internet users are from China. This is a huge, as well as a mysterious, market for IT industry due to various reasons such as culture difference. Twitter is the largest microblogging service in the world and Tencent Weibo is one of the largest microblogging services in China. Employ the two data sets as a source in our study, we try to unveil the unique behaviors of Chinese users. We have collected the entire Tencent Weibo from 10th, Oct, 2011 to 5th, Jan, 2012 and obtained 320 million user profiles, 5.15 billion user actions. We study Tencent Weibo from both macro and micro levels. From the macro level, Tencent users are more active on forwarding messages, but with less reciprocal relationships than Twitter users, their topic preferences are very different from Twitter users from both content and time consuming; besides, information can be diffused more efficient in Tencent Weibo. From the micro level, we mainly evaluate users' social influence from two indexes: "Forward" and \Follower", we study how users' actions will contribute to their social influences, and further identify unique features of Tencent users. According to our studies, Tencent users' actions are more personalized and diversity, and the influential users play a more important part in the whole networks. Based on the above analysis, we design a graphical model for predicting users' forwarding behaviors. Our experimental results on the large Tencent Weibo data validate the correctness of the discoveries and the effectiveness of the proposed model. To the best of our knowledge, this work is the first quantitative study on the entire Tencentsphere and information diffusion on it.Comment: WWW2013(submitted