Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care

We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random forests) classifiers. Experiments with data available from 2013 to 2017 for every citizen in Copenhagen receiving home care (27,775 citizens) show that prediction can achieve state of the art performance as reported in similar health related domains (AUC=0.715). We further find that competitive results can be obtained by using limited information for training, which is very useful when full records are not accessible or available. Smart city analytics does not necessarily require full city records. To our knowledge this preliminary study is the first to predict large increases in home care for smart city analytics

A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection

Compositionality in language refers to how much the meaning of some phrase can be decomposed into the meaning of its constituents and the way these constituents are combined. Based on the premise that substitution by synonyms is meaning-preserving, compositionality can be approximated as the semantic similarity between a phrase and a version of that phrase where words have been replaced by their synonyms. Different ways of representing such phrases exist (e.g., vectors [1] or language models [2]), and the choice of representation affects the measurement of semantic similarity. We propose a new compositionality detection method that represents phrases as ranked lists of term weights. Our method approximates the semantic similarity between two ranked list representations using a range of well-known distance and correlation metrics. In contrast to most state-of-the-art approaches in compositionality detection, our method is completely unsupervised. Experiments with a publicly available dataset of 1048 human-annotated phrases shows that, compared to strong supervised baselines, our approach provides superior measurement of compositionality using any of the distance and correlation metrics considered

Sequence Modelling For Analysing Student Interaction with Educational Systems

The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses previously unseen log data from Edulab, the largest provider of digital learning for mathematics in Denmark, to analyse the sessions of its users, where 1.08 million student sessions are extracted from a subset of their data. We propose to model students as a distribution of different underlying student behaviours, where the sequence of actions from each session belongs to an underlying student behaviour. We model student behaviour as Markov chains, such that a student is modelled as a distribution of Markov chains, which are estimated using a modified k-means clustering algorithm. The resulting Markov chains are readily interpretable, and in a qualitative analysis around 125,000 student sessions are identified as exhibiting unproductive student behaviour. Based on our results this student representation is promising, especially for educational systems offering many different learning usages, and offers an alternative to common approaches like modelling student behaviour as a single Markov chain often done in the literature.Comment: The 10th International Conference on Educational Data Mining 201

Neural Speed Reading with Structural-Jump-LSTM

Recurrent neural networks (RNNs) can model natural language by sequentially 'reading' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as 'neural speed reading', either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word. A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text.Comment: 10 page

Modelling Sequential Music Track Skips using a Multi-RNN Approach

Modelling sequential music skips provides streaming companies the ability to better understand the needs of the user base, resulting in a better user experience by reducing the need to manually skip certain music tracks. This paper describes the solution of the University of Copenhagen DIKU-IR team in the 'Spotify Sequential Skip Prediction Challenge', where the task was to predict the skip behaviour of the second half in a music listening session conditioned on the first half. We model this task using a Multi-RNN approach consisting of two distinct stacked recurrent neural networks, where one network focuses on encoding the first half of the session and the other network focuses on utilizing the encoding to make sequential skip predictions. The encoder network is initialized by a learned session-wide music encoding, and both of them utilize a learned track embedding. Our final model consists of a majority voted ensemble of individually trained models, and ranked 2nd out of 45 participating teams in the competition with a mean average accuracy of 0.641 and an accuracy on the first skip prediction of 0.807. Our code is released at https://github.com/Varyn/WSDM-challenge-2019-spotify.Comment: 4 page

„... und dann habe ich darüber nachgedacht ...“ Das „Modellcurriculum“ – ein Konzept zur Reflexionsarbeit für Studierende im Rahmen ihrer schulpraktischen Ausbildung

Als wichtige Grundlage, um professionelle Kompetenz aufzubauen und weiterzuentwickeln, gilt die Fähigkeit, die eigene Praxis zu reflektieren. In der Aus- und Weiterbildung von Lehrpersonen stellt sich dabei die Frage, wie in der Reflexion Praxiserfahrungen und Wissenschaftswissen ertragreich aufeinander bezogen werden können. Im vorliegenden Beitrag wird ein Pilotprojekt der Universität Passau vorgestellt, welches im Wintersemester 2012/13 am Lehrstuhl für Grundschulpädagogik implementiert wurde und Reflexionsarbeit zur Professionalisierung pädagogischer Akteure in den Fokus nimmt. Das ′′Modellcurriculum für Professionalisierung im Lehrberuf′′ soll Studierende dabei unterstützen, komplexe professionsspezifische Kompetenzen durch die enge Verknüpfung von spezifischen Lehrerveranstaltungen, Mentoraten und Reflexionsseminaren gezielt aufzubauen. In diesem Beitrag werden allerdings ausschließlich die theoretischen Grundlagen und strukturellen Maßnahmen für die Reflexionsarbeit mit Studierenden im Modellcurriculum vorgestellt, da die empirischen Ergebnisse zur wissenschaftlichen Evaluation des Konzepts im Rahmen einer Dissertationsarbeit in Vorbereitung sind (Fischer, in Druck) und daher nicht im vorliegenden Beitrags vorweg genommen werden können

Evaluation of growth in clinical genetics competency among PCPs participating in the UVMHN Genomic DNA Testing Program

Recently UVM Health Network Family Medicine practices have implemented “The Genomic DNA Test” pilot program to begin a concerted effort toward offering genetically informed primary care to all patients. The program aims to increase the number of participating primary care providers stepwise over time. However, some providers may find integration of genomic testing and discussion of clinical genetics issues with patients to be challenging given the relatively recent change toward emphasis on these topics in medical training curricula. The aim of this project was to develop a pilot survey to gather information from current participating providers about how participation in the genomic testing program has influenced their personal knowledge of and comfort with clinical genetics topics and patient counseling.https://scholarworks.uvm.edu/fmclerk/1777/thumbnail.jp

Perceptions of chief nurse executive competencies

The cost of health care in this country is increasing at an alarming rate. Cost containment for acute care hospitals is being mandated in an effort to deal with this issue. This has led to an increase in the roles and responsibilities of all members of the hospital administrative team as well as a need for increased collaboration among members of this group. The Chief Nurse Executive member of this administrative team is typically responsible for the greatest number of personnel and largest percentage of budgetary expenditures. This requires the CNE to synthesize knowledge from the disciplines of both nursing and business administration;The purpose of this study was to research CNE competencies as perceived by CNEs, Chief Executive Officers (CEO), and Presidents of Boards (POB) of randomly selected acute care hospitals (200 beds or more) stratified by regions in the United States. The revised Goodrich tool for determining nursing administration competencies was used and consisted of 116 competencies organized into 12 categories. The following hypothesized differences in perceptions of competencies needed by the CNE were examined: differences between CNEs and CEOs; and, differences within the CNE group based on age, gender, and years of experience in their present position. A low return rate precluded hypothesis testing of the difference in perceptions of all three groups; however, a preliminary analysis of all three groups\u27 responses was conducted. All respondents were also asked what degree they felt was needed by the CNE, as well as additional competencies needed for this position;Results showed a significant difference in CNE and CEO perceptions in 7 of the 12 competency categories. No significant differences were found in perceptions of the CNE group analyzed by hypothesized demographic differences. The additional analysis of all three respondent groups yielded differences in perceptions in 3 of the 12 competency categories. All three groups overwhelmingly chose a combined nursing and business graduate degree as the degree needed for the CNE position. A variety of additional competencies needed for the CNE position were listed by all three groups