Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201

Recommending Learning Videos for MOOCs and Flipped Classrooms

[EN] New teaching approaches are emerging in higher education, such as flipped classrooms. In addition, academic institutions are offering new types of training like Massive Online Open Courses. Both of these new ways of education require high-quality learning objects for their success, with learning videos being the most common to provide theoretical concepts. This paper describes a hybrid learning recommender system based on content-based techniques, which is able to recommend useful videos to learners and teachers from a learning video repository. This hybrid technique has been successfully applied to a real scenario such as the central video repository of the Universitat Politècnica de València.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and TIN2017-89156-R projects of the Spanish government, and PROMETEO/2018/002 project of Generalitat Valenciana. J. Jordán and V. Botti are funded by UPV PAID-06-18 project. J. Jordán is also funded by grant APOSTD/2018/010 of Generalitat Valenciana - Fondo Social Europeo.Jordán, J.; Valero Cubas, S.; Turró, C.; Botti Navarro, VJ. (2020). Recommending Learning Videos for MOOCs and Flipped Classrooms. Springer. 146-157. https://doi.org/10.1007/978-3-030-49778-1_12S146157Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2012). https://doi.org/10.1007/s11280-012-0187-zvan Dijck, J., Poell, T.: Higher education in a networked world: European responses to U.S. MOOCs. Int. J. Commun.: IJoC 9, 2674–2692 (2015)Dwivedi, P., Bharadwaj, K.K.: e-learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst. 32(2), 264–276 (2015)Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)Institute and Committee of Electrical and Electronics Engineers: Learning Technology Standards: IEEE Standard for Learning Object Metadata. IEEE Standard 1484.12.1 (2002)Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44(4), 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-zMaassen, P., Nerland, M., Yates, L. (eds.): Reconfiguring Knowledge in Higher Education. Higher Education Dynamics, vol. 50. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-72832-2MLLP research group, Universitat Politècnica de València: Tlp: The translectures-upv platform. http://www.mllp.upv.es/tlpO’Flaherty, J., Phillips, C.: The use of flipped classrooms in higher education: a scoping review. Internet High. Educ. 25, 85–95 (2015)Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on World Wide Web, pp. 521–530 (2007)Rodríguez, P., Heras, S., Palanca, J., Duque, N., Julián, V.: Argumentation-based hybrid recommender system for recommending learning objects. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS/AT -2015. LNCS (LNAI), vol. 9571, pp. 234–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33509-4_19Roehl, A., Reddy, S.L., Shannon, G.J.: The flipped classroom: an opportunity to engage millennial students through active learning strategies. J. Fam. Consum. Sci. 105, 44–49 (2013)Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)Stoica, A.S., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: A semi-supervised method to classify educational videos. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 218–228. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_19Tarus, J.K., Niu, Z., Yousif, A.: A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gener. Comput. Syst. 72, 37–48 (2017)Tucker, B.: The flipped classroom. Online instruction at home frees class time for learning. Educ. Next Winter 2012, 82–83 (2012)Turcu, G., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: Towards a custom designed mechanism for indexing and retrieving video transcripts. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 299–309. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_26Turró, C., Morales, J.C., Busquets-Mataix, J.: A study on assessment results in a large scale flipped teaching experience. In: 4th International Conference on Higher Education Advances (HEAD 2018), pp. 1039–1048 (2018)Turró, C., Despujol, I., Busquets, J.: Networked teaching, the story of a success on creating e-learning content at Universitat Politècnica de València. EUNIS J. High. Educ. (2014)Zajda, J., Rust, V. (eds.): Globalisation and Higher Education Reforms. GCEPR, vol. 15. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28191-

Using a Hybrid Recommending System for Learning Videos in Flipped Classrooms and MOOCs

[EN] New challenges in education require new ways of education. Higher education has adapted to these new challenges by means of offering new types of training like massive online open courses and by updating their teaching methodology using novel approaches as flipped classrooms. These types of training have enabled universities to better adapt to the challenges posed by the pandemic. In addition, high quality learning objects are necessary for these new forms of education to be successful, with learning videos being the most common learning objects to provide theoretical concepts. This paper describes a new approach of a previously presented hybrid learning recommender system based on content-based techniques, which was capable of recommend useful videos to learners and lecturers from a learning video repository. In this new approach, the content-based techniques are also combined with a collaborative filtering module, which increases the probability of recommending relevant videos. This hybrid technique has been successfully applied to a real scenario in the central video repository of the Universitat Politècnica de València.This research was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and TIN2017-89156-R projects of the Spanish government, and PROMETEO/2018/002 project of Generalitat Valenciana.Jordán, J.; Valero Cubas, S.; Turró, C.; Botti, V. (2021). Using a Hybrid Recommending System for Learning Videos in Flipped Classrooms and MOOCs. Electronics. 10(11):1-19. https://doi.org/10.3390/electronics10111226S119101

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Asia Minor Greek: Towards a Computational Processing

AbstractIn this paper, we discuss issues concerning the computational aspect of an on-going research project which aims at providing a systematic study of three Greek dialects of Asia Minor (“Pontus, Cappadocia, Aivali: In search of Asia Minor Greek”- AmiGre) In fact, the project constitutes the first attempt to describe dialectal phenomena at a phonological, morphological, and structural level. Furthermore, it also constitutes the first attempt in Greece to combine Informatics and Theoretical Lin- guistics in order to facilitate the above-mentioned task. The aim here is to provide the design principles of the computational component of the project namely, an electronic dictionary and a multimedia database which would provide an innovative mechanism of storing, processing and retrieving oral and written dialectal data

Deliverable D2.6 LinkedTV Framework for Generating Video Enrichments with Annotations

This deliverable describes the final LinkedTV framework that provides a set of possible enrichment resources for seed video content using techniques such as text and web mining, information extraction and information retrieval technologies. The enrichment content is obtained from four type of sources: a) by crawling and indexing web sites described in a white list specified by the content partners, b) by querying the API or SPARQL endpoint of the Europeana digital library network which is publicly exposed, c) by querying multiple social networking APIs, d) by hyperlinking to other parts of TV programs within the same collection using a Solr index. This deliverable also describes an additional content annotation functionality, namely labelling enrichment (as well as seed) content with thematic topics, as well as the process of exposing content annotations to this module and to the filtering services of LinkedTV’s personalization workflow. We illustrate the enrichment workflow for the two main scenarios of LinkedTV which have lead to the development of the LinkedCulture and LinkedNews applications, which respectively use the TVEnricher and TVNewsEnricher enrichment services. The original title of this deliverable from the DoW was Advanced concept labelling by complementary Web mining

Deliverable D2.3 Specification of Web mining process for hypervideo concept identification

This deliverable presents a state-of-art and requirements analysis report for the web mining process as part of the WP2 of the LinkedTV project. The deliverable is divided into two subject areas: a) Named Entity Recognition (NER) and b) retrieval of additional content. The introduction gives an outline of the workflow of the work package, with a subsection devoted to relations with other work packages. The state-of-art review is focused on prospective techniques for LinkedTV. In the NER domain, the main focus is on knowledge-based approaches, which facilitate disambiguation of identified entities using linked open data. As part of the NER requirement analysis, the first tools developed are described and evaluated (NERD, SemiTags and THD). The area of linked additional content is broader and requires a more thorough analysis. A balanced overview of techniques for dealing with the various knowledge sources (semantic web resources, web APIs and completely unstructured resources from a white list of web sites) is presented. The requirements analysis comes out of the RBB and Sound and Vision LinkedTV scenarios

Designing and evaluating a user interface for continous embedded lifelogging based on physical context

PhD ThesisAn increase in both personal information and storage capacity has encouraged people to store and archive their life experience in multimedia formats. The usefulness of such large amounts of data will remain inadequate without the development of both retrieval techniques and interfaces that help people access and navigate their personal collections. The research described in this thesis investigates lifelogging technology from the perspective of the psychology of memory and human-computer interaction. The research described seeks to increase my understanding of what data can trigger memories and how I might use this insight to retrieve past life experiences in interfaces to lifelogging technology. The review of memory and previous research on lifelogging technology allows and support me to establish a clear understanding of how memory works and design novel and effective memory cues; whilst at the same time I critiqued existing lifelogging systems and approaches to retrieving memories of past actions and activities. In the initial experiments I evaluated the design and implementation of a prototype which exposed numerous problems both in the visualisation of data and usability. These findings informed the design of novel lifelogging prototype to facilitate retrieval. I assessed the second prototype and determined how an improved system supported access and retrieval of users’ past life experiences, in particular, how users group their data into events, how they interact with their data, and the classes of memories that it supported. In this doctoral thesis I found that visualizing the movements of users’ hands and bodies facilitated grouping activities into events when combined with the photos and other data captured at the same time. In addition, the movements of the user's hand and body and the movements of some objects can promote an activity recognition or support user detection and grouping of them into events. Furthermore, the ability to search for specific movements significantly reduced the amount of time that it took to retrieve data related to specific events. I revealed three major strategies that users followed to understand the combined data: skimming sequences, cross sensor jumping and continued scanning