Making Legacy LMS adaptable using Policy and Policy templates

Koesling, A., Herder, E., De Coi, J., & Abel, F. (2008). Making Legacy LMS adaptable using Policy and Policy templates. In J. Baumeister & M. Atzmüller, Proceedings of the 16th Workshop on Adaptivity and User Modeling in Interactive System, ABIS 2008 (pp. 35-40). October, 6-8, 2008, Würzburg, Germany: University of Würzburg. Website with link to proceedings: http://lwa08.informatik.uni-wuerzburg.de/Wiki.jsp?page=FGABIS08In this paper, we discuss how users and designers of existing learning management systems (LMSs) can make use of policies to enhance adaptivity and adaptability. Many widespread LMSs currently only use limited and proprietary rule systems defining the system behaviour. Personalization of those systems is done based on those rule systems allowing only for fairly restricted adaptation rules. Policies allow for more sophisticated and flexible adaptation rules, provided by multiple stakeholders and they can be integrated into legacy systems. We present the benefits and feasibility of our ongoing approach of extending an existing LMS with policies. We will use the LMS ILIAS as a hands-on example to allow users to make use of system personalization.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Preference Search Service - Specification and Implementation

While the growing number of learning resources increases the   choice for learners, it also makes it more and more difficult to   find suitable courses. Thus, improved search capabilities on   learning resource repositories are required.  In   this   document,   we   describe   the   implementation   of   our   approach for learning resource search based on preference   queries. The implementation comprises a Web Service as well  as a java package supporting the client development for the   service.   This   Web   Service   acts   as   one   part   of   the   TENCompetence Personalization Services developed in WP7.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

An algorithm to estimate the risk of child labor

In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic

Extracting personal information from conversations

Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications. Background facts about users can allow chatbot assistants to produce more topical and empathic replies. In the context of recommendation and retrieval models, personal facts can be used to customize the ranking results for individual users. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge. Such knowledge bases are easily interpretable and can provide users with full control over their own personal knowledge, including revising stored facts and managing access by downstream services for personalization purposes. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Mainstream extraction methods specialize on well-structured data, such as biographical texts or encyclopedic articles, which are rare for most people. In turn, conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts. In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers’ personal attributes: • Demographic attributes, age, gender, profession and family status, are inferred by HAMs - hierarchical neural classifiers with attention mechanism. Trained HAMs can be transferred between different types of conversational data and provide interpretable predictions. • Long-tailed personal attributes, hobby and profession, are predicted with CHARM - a zero-shot learning model, overcoming the lack of labeled training samples for rare attribute values. By linking conversational utterances to external sources, CHARM is able to predict attribute values which it never saw during training. • Interpersonal relationships are inferred with PRIDE - a hierarchical transformer-based model. To accurately predict fine-grained relationships, PRIDE leverages personal traits of the speakers and the style of conversational utterances. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.Personengebundene Fakten sind eine vielseitig nutzbare Quelle für die verschiedensten Anwendungen. Hintergrundfakten über Nutzer können es Chatbot-Assistenten ermöglichen, relevantere und persönlichere Antworten zu geben. Im Kontext von Empfehlungs- und Retrievalmodellen können personengebundene Fakten dazu verwendet werden, die Ranking-Ergebnisse für Nutzer individuell anzupassen. Eine Personengebundene Wissensdatenbank, gefüllt mit persönlichen Daten wie demografischen Angaben, Interessen und Beziehungen, kann eine universelle Schnittstelle für die Speicherung und Abfrage solcher Fakten sein. Wissensdatenbanken sind leicht zu interpretieren und bieten dem Nutzer die vollständige Kontrolle über seine personenbezogenen Fakten, einschließlich der Überarbeitung und der Verwaltung des Zugriffs durch nachgelagerte Dienste, etwa für Personalisierungszwecke. Um den Nutzern den aufwändigen manuellen Aufbau einer solchen persönlichen Wissensdatenbank zu ersparen, können automatisierte Extraktionsmethoden auf den textuellen Inhalten der Nutzer – wie z.B. Konversationen oder Beiträge in sozialen Medien – angewendet werden. Die üblichen Extraktionsmethoden sind auf strukturierte Daten wie biografische Texte oder enzyklopädische Artikel spezialisiert, die bei den meisten Menschen keine Rolle spielen. In dieser Dissertation beschäftigen wir uns mit der Gewinnung von persönlichem Wissen aus Dialogdaten und schlagen mehrere neuartige Deep-Learning-Modelle zur Ableitung persönlicher Attribute von Sprechern vor: • Demographische Attribute wie Alter, Geschlecht, Beruf und Familienstand werden durch HAMs - Hierarchische Neuronale Klassifikatoren mit Attention-Mechanismus - abgeleitet. Trainierte HAMs können zwischen verschiedenen Arten von Gesprächsdaten übertragen werden und liefern interpretierbare Vorhersagen • Vielseitige persönliche Attribute wie Hobbys oder Beruf werden mit CHARM ermittelt - einem Zero-Shot-Lernmodell, das den Mangel an markierten Trainingsbeispielen für seltene Attributwerte überwindet. Durch die Verknüpfung von Gesprächsäußerungen mit externen Quellen ist CHARM in der Lage, Attributwerte zu ermitteln, die es beim Training nie gesehen hat • Zwischenmenschliche Beziehungen werden mit PRIDE, einem hierarchischen transformerbasierten Modell, abgeleitet. Um präzise Beziehungen vorhersagen zu können, nutzt PRIDE persönliche Eigenschaften der Sprecher und den Stil von Konversationsäußerungen Experimente mit verschiedenen Konversationstexten, inklusive Reddit-Diskussionen und Filmskripten, demonstrieren die Praxistauglichkeit unserer Methoden und ihre hervorragende Leistung im Vergleich zum aktuellen Stand der Technik

Detection of phosphates originating from Enceladus’s ocean

Saturn’s moon Enceladus harbours a global1 ice-covered water ocean2,3. The Cassini spacecraft investigated the composition of the ocean by analysis of material ejected into space by the moon’s cryovolcanic plume4,5,6,7,8,9. The analysis of salt-rich ice grains by Cassini’s Cosmic Dust Analyzer10 enabled inference of major solutes in the ocean water (Na+, K+, Cl–, HCO3–, CO32–) and its alkaline pH3,11. Phosphorus, the least abundant of the bio-essential elements12,13,14, has not yet been detected in an ocean beyond Earth. Earlier geochemical modelling studies suggest that phosphate might be scarce in the ocean of Enceladus and other icy ocean worlds15,16. However, more recent modelling of mineral solubilities in Enceladus’s ocean indicates that phosphate could be relatively abundant17. Here we present Cassini’s Cosmic Dust Analyzer mass spectra of ice grains emitted by Enceladus that show the presence of sodium phosphates. Our observational results, together with laboratory analogue experiments, suggest that phosphorus is readily available in Enceladus’s ocean in the form of orthophosphates, with phosphorus concentrations at least 100-fold higher in the moon’s plume-forming ocean waters than in Earth’s oceans. Furthermore, geochemical experiments and modelling demonstrate that such high phosphate abundances could be achieved in Enceladus and possibly in other icy ocean worlds beyond the primordial CO2 snowline, either at the cold seafloor or in hydrothermal environments with moderate temperatures. In both cases the main driver is probably the higher solubility of calcium phosphate minerals compared with calcium carbonate in moderately alkaline solutions rich in carbonate or bicarbonate ions

Mining User Interests from Social Media

Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work