Using natural shape statistics of urban form to model social capital

The social aspect is an important but often overlooked part of sustainable development philosophy. In hoping to popularise and show the importance of social sustainable development, this study tries to find a relation between the social environment and urban form. Research in the social capital field provided the methodology to acquire social computational data. The relation between human actions and the environment is noted in many theories, and used in some practices. Human cognition is computationally predictable with natural shape analysis and machine learning methods. In the analysis of shape, a topological skeleton is a proven method to acquire statistical data that correlates with data collected from human experiments. In this study, the analysis of urban form with respect to human cognition was used to acquire computational data for a machine learning model of social capital in counties in the USA Article in English. Socialinio kapitalo modeliavimas taikant pastatų formos statistinę analizę Santrauka Tvarios plėtros teorijoje socialinė aplinka yra pripažinta kaip svarbus veiksnys, tačiau trūksta praktinės metodikos. Ryšio tarp urbanistinės formos ir socialinės aplinkos radimas aktualizuotų ir padėtų populiarinti socialinę tvarią plėtrą. Aplinkos įtaka žmonių tarpusavio elgesiui yra ne kartą aptartas reiškinys, tačiau praktikoje retai taikomas. Ankstesniuose socialinio kapitalo tyrimuose pateikiamos metodologijos ir statistiniai duomenys esamos situacijos analizei atlikti. Kaip žmonės suvokia formas, yra nuspėjama taikant statistinę formos analizę ir dirbtinio intelekto metodologiją – sistemos mokymąsi. Klasifikuojant formas topologinio skeleto metodologija gaunami rezultatai koreliuoja su duomenimis, surinktais per eksperimentą, kuriame žmonės klasifikuoja formas. Taikant žinomas formos analizės metodologijas, atspindinčias suvokimą, buvo surinkti duomenys modeliuoti socialinį kapitalą su sisteminio mokymosi modeliu. Sisteminis mokymasis yra dirbtinio intelekto sritis, kurioje remiantis pateiktais duomenimis automatiškai sukalibruojama kompleksinė matematinė formulė. Modeliuojant socialinį kapitalą su formos skeleto statistiniais duomenimis, geriausi rezultatai pasiekti taikant neuroniniais tinklais pagristą sisteminį mokymąsi. Reikšminiai žodžiai: urbanistinė forma,  formos analizė statis­tiniais metodais, socialinis kapitalas, sisteminis mokymasis, daugiasluoksnis perceptronas

Do people communicate about their whereabouts? Investigating the relation between user-generated text messages and Foursquare check-in places

The social functionality of places (e.g. school, restaurant) partly determines human behaviors and reflects a region’s functional configuration. Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces. Assuming their potential impacts on human verbalization behaviors, one possibility is to link the functions of places to verbal representations such as users’ postings in location-based social networks (LBSNs). In this study, we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places. We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places, which enables a reliable verification of the semantic concepts identified from user-generated text snippets. A latent semantic analysis is conducted on a large Foursquare check-in dataset. The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization. To further elaborate the representativeness of text messages, this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts (i.e., representativeness), and another investigation on semantic concepts to quantify how well they can be represented by text messages (i.e., representability). The results shed light on featured terms with strong locational characteristics, as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations

Location-Specific Tweet Detection and Topic Summarization in Twitter

Abstract-Automatic detection of tweets that provide Location-specific information will be extremely useful in conveying geo-location based knowledge to the users. However, there is a significant challenge in retrieving such tweets due to the sparsity of geo-tag information, the short textual nature of tweets, and the lack of pre-defined set of topics. In this paper, we develop a novel framework to identify and summarize tweets that are specific to a location. First, we propose a weighting scheme called Location Centric Word Co-occurrence (LCWC) that uses the content of the tweets and the network information of the twitterers to identify tweets that are location-specific. We evaluate the proposed model using a set of annotated tweets and compare the performance with other weighting schemes studied in the literature. This paper reports three key findings: (a) top trending tweets from a location are poor descriptors of location-specific tweets, (b) ranking tweets purely based on users' geo-location cannot ascertain the location specificity of tweets, and (c) users' network information plays an important role in determining the location-specific characteristics of the tweets. Finally, we train a topic model based on Latent Dirichlet Allocation (LDA) using a large collection of local news database and tweet-based Urls to predict the topics from the location-specific tweets and present them using an interactive web-based interface

Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums

This article belongs to the Special Issue E-learning, Digital Learning, and Digital Communication Used for Education Sustainability.Discussion forums are a valuable source of information in educational platforms such as Massive Open Online Courses (MOOCs), as users can exchange opinions or even help other students in an asynchronous way, contributing to the sustainability of MOOCs even with low interaction from the instructor. Therefore, the use of the forum messages to get insights about students’ performance in a course is interesting. This article presents an automatic grading approach that can be used to assess learners through their interactions in the forum. The approach is based on the combination of three dimensions: (1) the quality of the content of the interactions, (2) the impact of the interactions, and (3) the user’s activity in the forum. The evaluation of the approach compares the assessment by experts with the automatic assessment obtaining a high accuracy of 0.8068 and Normalized Root Mean Square Error (NRMSE) of 0.1799, which outperforms previous existing approaches. Future research work can try to improve the automatic grading by the training of the indicators of the approach depending on the MOOCs or the combination with text mining techniques.This research was funded by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learn Projects under Grants TIN2017-85179-C3-1-R and PID2020-112584RB-C31, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307 and under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation), a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects, more specifically through projects InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP)

A study of neighbour selection strategies for POI recommendation in LBSNs

Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.Fil: Rios, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin