DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018

Toward an Integrated Competence-based System Supporting Lifelong Learning and Employability: Concepts, Model, and Challenges

Miao, Y., Van der Klink, M., Boon, J., Sloep, P. B., & Koper, R. (2009). Toward an Integrated Competence-based System Supporting Lifelong Learning and Employability: Concepts, Model, and Challenges. In M. Spaniol, Q. Li, R. Klamma & R. W. H. Lau (Eds.), Proceedings of the 8th International Conference Advances in Web Based Learning - ICWL 2009 (pp. 265-276). August, 19-21, 2009, Aachen, Germany. Lecture Notes in Computer Science 5686; Berlin, Heidelberg: Springer-Verlag.Efficient and effective lifelong learning requires that people can make informed decisions about their continuous personal development in the different stages of their lives. In this paper we state that lifelong learners need to be characterized as decision-makers. In order to improve the quality of their decisions we propose the development of an integrated lifelong learning and employment support system, which traces learners’ competence development and provides a decision support environment. An abstract conceptual model has been developed and the main design ideas have been documented using Z notation. Moreover, we analyzed the main technical challenges for the realization of the target system: competence information fusion, decision analysis models, spatial indexing structures and browsing structures and visualization of competence related information objects.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

Automatic Discovery of Complementary Learning Resources

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.Students in a learning experience can be seen as a community working simultaneously (and in some cases collaboratively) in a set of activities. During these working sessions, students carry out numerous actions that affect their learning. But those actions happening outside a class or the Learning Management System cannot be easily observed. This paper presents a technique to widen the observability of these actions. The set of documents browsed by the students in a course was recorded during a period of eight weeks. These documents are then processed and the set with highest similarity with the course notes are selected and recommended back to all the students. The main problem is that this user community visits thousands of documents and only a small percent of them are suitable for recommendation. Using a combination of lexican analysis and information retrieval techniques, a fully automatic procedure to analyze these documents, classify them and select the most relevant ones is presented. The approach has been validated with an empirical study in an undergraduate engineering course with more than one hundred students. The recommended resources were rated as "relevant to the course" by the seven instructors with teaching duties in the course.Work partially funded by the Learn3 project, “Plan Nacional de I+D+I TIN2008-05163/TSI”, the Acción Integrada Ref. DE2009-0051, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650) and TELMA Project (Plan Avanza TSI-020110-2009-85)

New learning opportunities in a networked world: developing a research agenda on innovative uses of ICTS for learning and teaching

IDRC Project Title: Developing a Research Agenda on Expanding New Digital Learning Opportunities in Developing Countries;IDRC Project Number: 107628The report describes outcomes of the activities carried out for the project “New Learning Opportunities in a Networked World: Developing a Research Agenda on Innovative uses of ICTs for Learning and Teaching”. The research consists of three main activities, namely desk research, written expert consultation and group concept mapping study involving a 2-day workshop and a follow-up with experts who could not attend the workshop. These activities are interconnected elements of the consultative approach to establishing a research agenda.International Development Research Centre (IDRC), Canad

Learning Analytics in het onderwijs:Een onderwijskundig perspectief

https://www.surf.nl/kennisbank/2016/rapport-learning-analytics-in-het-onderwijs-een-onderwijskundig-perspectief.htmlLearning analytics in de onderwijspraktijk Meer inzicht in het onderwijsproces, gerichte feedback aan studenten en uiteindelijk verbetering van het onderwijs: dat is de gedachte achter learning analytics. De mogelijkheden van learning analytics zijn groot, maar hoe past een opleiding of instelling ze succesvol toe? Dat valt of staat met de manier waarop learning analytics wordt toegepast in de onderwijspraktijk. Ontwerpen van online onderwijs Learning analytics werkt pas echt als we erin slagen de juiste vragen aan de data te stellen. Dat begint al bij het ontwerpen van online onderwijs. Voor het rapport 'Learning analytics in het onderwijs: een onderwijskundig perspectief' hebben we samen met vertegenwoordigers uit het hoger onderwijs onderzocht hoe je in een onderwijsontwerp effectief gebruik kunt maken van learning analytics. In een aantal cases laten we bovendien zien hoe dat in de onderwijspraktijk kan werken. Ondersteuning en inspiratie voor docenten en onderwijsontwikkelaars Het rapport ondersteunt en inspireert docenten en onderwijsontwikkelaars bij het toepassen van learning analytics in online onderwijs. Zo kunnen ze data verzamelen over hoe studenten door een online omgeving klikken, welke video’s ze bekijken, en welke andere digitale voetsporen ze achterlaten, en wat dat zegt over hun leergedrag.SUR

Not Yet Ready for Everyone: An Experience Report about a Personal Learning Environment for Language Learning

Abstract. A Personal Learning Environment (PLE) is a mash-up of learning services. It enables students and teachers to assemble a work environment that is adapted to a domain and specific individual needs. In this article, we report on our experiences on using a PLE for Lan-guage Learning in five French lectures at the Shanghai Jiao Tong Uni-versity Continuing Education School. We found that while a PLE has the potential to simplify access to and usage of Web sites and services for language learning, students will use it only if properly motivated. Fur-thermore, at the time being, difficulties that result from the user interface and technical implementation make the interactions with PLEs difficult. The problems need to be overcome in order for PLEs to become adopted by the average, not technically highly literate students and teachers. Key words: PLE, mash-up, experience report

The Games Realising Effective & Affective Transformation (GREAT) Project – A pathway to sustainable impact on climate change policy

In this paper the authors introduce the Games Realising Effective and Affective Transformation (GREAT) research project. This EU funded intervention posits the application of digital games, game making and games technologies, as a realisable sustainable solution to actively engage citizens in meaningful dialogue with governments to address the global challenge of climate change. The primary objective of the intervention is to facilitate citizens by using emergent technologies, to provide input into developing national and international policy priorities to address the challenges presented by global climate change, technologies that are both available and accessible. The GREAT project commenced on 01 February 2023 and brings together leading scientists in academia and the games industry in a single programme of research and innovation. The Project aims to establish new forms of social engagement and encourage meaningful dialogue between citizens and senior policy stakeholders (policy makers, policy implementers, political parties, and affected citizens)

Towards a Social Trust-Aware Recommender for Teachers

Fazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2014). Towards a Social Trust-aware Recommender for Teachers. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 177-194): Springer New York.Online communities and networked learning provide teachers with social learning opportunities, allowing them to interact and collaborate with others in order to develop their personal and professional skills. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this . The setting is the Open Discovery Space (ODS) project. Unfortunately, due to the sparsity of the educational datasets most educational recommender systems cannot make accurate recommendations. To overcome this problem, we propose to enhance a trust-based recommender algorithm with social data obtained from monitoring the activities of teachers within the ODS platform. In this article, we outline the re-quirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical ev-idence from a survey study with stakeholders of the ODS project to support the requirements identified from a literature study. Finally, we present an agenda for further research intended to find out which recommender system should ul-timately be deployed in the ODS platform.NELLL, EU 7th framework Open Discovery Spac