Search

Your search keyword '"Trausan-Matu, Stefan"' showing total 63 results
Start Over Author "Trausan-Matu, Stefan" Topic natural language processing Publication Year Range Last 50 years

Refine your results

more »

more »

more »
63 results on '"Trausan-Matu, Stefan"'

1. Multi-Document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links

Author

Dascalu, Marina-Dorinela, Ruseti, Stefan, Dascalu, Mihai, McNamara, Danielle, and Trausan-Matu, Stefan

Abstract

Reading comprehension requires readers to connect ideas within and across texts to produce a coherent mental representation. One important factor in that complex process regards the cohesion of the document(s). Here, we tackle the challenge of providing researchers and practitioners with a tool to visualize text cohesion both within (intra) and between (inter) texts. This tool, Multi-document Cohesion Network Analysis (MD-CNA), expands the structure of a CNA graph with lexical overlap links of multiple types, together with coreference links to highlight dependencies between text fragments of different granularities. We introduce two visualizations of the CNA graph that support the visual exploration of intratextual and intertextual links. First, a "hierarchical view" displays a tree-structure of discourse as a visual illustration of CNA links within a document. Second, a "grid view" available at paragraph or sentence levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization. [This is a chapter in: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (Eds) "Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science," v12164 (p80-85). Cham, Switzerland: Springer Nature Switzerland.]

Published
2020
Full Text
View/download PDF

2. Automated Scoring of Self-Explanations Using Recurrent Neural Networks

Author

Panaite, Marilena, Ruseti, Stefan, Dascalu, Mihai, Balyan, Renu, McNamara, Danielle S., and Trausan-Matu, Stefan

Abstract

Intelligence Tutoring Systems (ITSs) focus on promoting knowledge acquisition, while providing relevant feedback during students' practice. Self-explanation practice is an effective method used to help students understand complex texts by leveraging comprehension. Our aim is to introduce a deep learning neural model for automatically scoring student self-explanations that are targeted at specific sentences. The first stage of the processing pipeline performs an initial text cleaning and applies a set of predefined rules established by human experts in order to identify specific cases (e.g., students who do not understand the text, or students who simply copy and paste their self-explanations from the given input text). The second step uses a Recurrent Neural Network with pre-trained Glove word embeddings to predict self-explanation scores on a scale of 1 to 3. In contrast to previous SVM models trained on the same dataset of 4109 self-explanations, we obtain a significant increase of accuracy from 59% to 73%. Moreover, the new pipeline can be integrated in learning scenarios requiring near real-time responses from the ITS, thus addressing a major limitation in terms of processing speed exhibited by the previous approach. [This paper was published in: M. Scheffel et al. (Eds.), "EC-TEL 2019" (pp. 659-663). Switzerland: Springer.]

Published
2019
Full Text
View/download PDF

3. 'PolyCAFe'--Automatic Support for the Polyphonic Analysis of CSCL Chats

Author

Trausan-Matu, Stefan, Dascalu, Mihai, and Rebedea, Traian

Abstract

Chat conversations and other types of online communication environments are widely used within CSCL educational scenarios. However, there is a lack of theoretical and methodological background for the analysis of collaboration. Manual assessing of non-moderated chat discussions is difficult and time-consuming, having as a consequence that learning scenarios have not been widely adopted, neither in formal education nor in informal learning contexts. An analysis method of collaboration and individual participation is needed. Moreover, computer-support tools for the analysis and assessment of these conversations are required. In this paper, we start from the "polyphonic framework" as a theoretical foundation suitable for the analysis of textual and even gestural interactions within collaborative groups. This framework exploits the notions of dialogism, inter-animation and polyphony for assessing interactions between participants. The basics of the polyphonic framework are discussed and a systematic presentation of the polyphonic analysis method is included. Then, we present the "PolyCAFe" system, which provides tools that support the polyphonic analysis of chat conversations and online discussion forums of small groups of learners. Natural Language Processing (NLP) is used in order to identify topics, semantic similarities and links between utterances. The detected links are then used to build a graph of utterances, which forms the central element for the polyphonic analysis and for providing automatic feedback and support to both tutors and learners. Social Network Analysis is used for computing quantitative measures for the interactions between participants. Two evaluation experiments have been undertaken with "PolyCAFe". Learners find the system useful and efficient. In addition to these advantages, tutors reflecting on the conversation can provide quicker manual feedback.

Published
2014
Full Text
View/download PDF

4. Artifact Analysis

Author

Trausan-Matu, Stefan, Slotta, James D., Hoadley, Christopher, Series Editor, van Aalst, Jan, Associate Editor, Jahnke, Isa, Associate Editor, Cress, Ulrike, editor, Rosé, Carolyn, editor, Wise, Alyssa Friend, editor, and Oshima, Jun, editor

Published
2021
Full Text
View/download PDF

6. Improving Writing for Romanian Language

Author

Florea, Anda-Madalina, Dascalu, Mihai, Sirbu, Maria-Dorinela, Trausan-Matu, Stefan, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Rehm, Matthias, editor, Saldien, Jelle, editor, and Manca, Stefania, editor

Published
2020
Full Text
View/download PDF

7. Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques

Author

Ruseti, Stefan, Sirbu, Maria-Dorinela, Calin, Mihnea Andrei, Dascalu, Mihai, Trausan-Matu, Stefan, Militaru, Gheorghe, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2020
Full Text
View/download PDF

9. ReadME – Your Personal Writing Assistant

Author

Toma, Irina, Cotet, Teodor-Mihai, Dascalu, Mihai, Trausan-Matu, Stefan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Scheffel, Maren, editor, Broisin, Julien, editor, Pammer-Schindler, Viktoria, editor, Ioannou, Andri, editor, and Schneider, Jan, editor

Published
2019
Full Text
View/download PDF

10. Automated Scoring of Self-explanations Using Recurrent Neural Networks

Author

Panaite, Marilena, Ruseti, Stefan, Dascalu, Mihai, Balyan, Renu, McNamara, Danielle S., Trausan-Matu, Stefan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Scheffel, Maren, editor, Broisin, Julien, editor, Pammer-Schindler, Viktoria, editor, Ioannou, Andri, editor, and Schneider, Jan, editor

Published
2019
Full Text
View/download PDF

11. Document Cohesion Flow: Striving towards Coherence

Author

Crossley, Scott A., Dascalu, Mihai, Trausan-Matu, Stefan, Allen, Laura K., and McNamara, Danielle S.

Subjects
Cohesion Flow, Natural Language Processing, Computational Models, Cohesion Network Analysis, Coherence, Writing Quality
Abstract

Text cohesion is an important element of discourseprocessing. This paper presents a new approach to modeling,quantifying, and visualizing text cohesion using automatedcohesion flow indices that capture semantic links amongparagraphs. Cohesion flow is calculated by applyingCohesion Network Analysis, a combination of semanticdistances, Latent Semantic Analysis, and Latent DirichletAllocation, as well as Social Network Analysis. Experimentsperformed on 315 timed essays indicated that cohesion flowindices are significantly correlated with human ratings of textcoherence and essay quality. Visualizations of the globalcohesion indices are also included to support a more facileunderstanding of how cohesion flow impacts coherence interms of semantic dependencies between paragraphs.

Published
2016

13. ReadME – Enhancing Automated Writing Evaluation

Author

Sirbu, Maria-Dorinela, Botarleanu, Robert-Mihai, Dascalu, Mihai, Crossley, Scott A., Trausan-Matu, Stefan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Agre, Gennady, editor, van Genabith, Josef, editor, and Declerck, Thierry, editor

Published
2018
Full Text
View/download PDF

14. Cohesion-Centered Analysis of Sociograms for Online Communities and Courses Using ReaderBench

Author

Dascalu, Mihai, Sirbu, Maria-Dorinela, Gutu-Robu, Gabriel, Ruseti, Stefan, Crossley, Scott A., Trausan-Matu, Stefan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pammer-Schindler, Viktoria, editor, Pérez-Sanagustín, Mar, editor, Drachsler, Hendrik, editor, Elferink, Raymond, editor, and Scheffel, Maren, editor

Published
2018
Full Text
View/download PDF

15. Help Me Understand This Conversation: Methods of Identifying Implicit Links Between CSCL Contributions

Author

Masala, Mihai, Ruseti, Stefan, Gutu-Robu, Gabriel, Rebedea, Traian, Dascalu, Mihai, Trausan-Matu, Stefan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pammer-Schindler, Viktoria, editor, Pérez-Sanagustín, Mar, editor, Drachsler, Hendrik, editor, Elferink, Raymond, editor, and Scheffel, Maren, editor

Published
2018
Full Text
View/download PDF

16. Towards an Automated Model of Comprehension (AMoC)

Author

Dascalu, Mihai, Paraschiv, Ionut Cristian, McNamara, Danielle S., Trausan-Matu, Stefan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pammer-Schindler, Viktoria, editor, Pérez-Sanagustín, Mar, editor, Drachsler, Hendrik, editor, Elferink, Raymond, editor, and Scheffel, Maren, editor

Published
2018
Full Text
View/download PDF

17. Identifying Implicit Links in CSCL Chats Using String Kernels and Neural Networks

Author

Masala, Mihai, Ruseti, Stefan, Gutu-Robu, Gabriel, Rebedea, Traian, Dascalu, Mihai, Trausan-Matu, Stefan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Penstein Rosé, Carolyn, editor, Martínez-Maldonado, Roberto, editor, Hoppe, H. Ulrich, editor, Luckin, Rose, editor, Mavrikis, Manolis, editor, Porayska-Pomsta, Kaska, editor, McLaren, Bruce, editor, and du Boulay, Benedict, editor

Published
2018
Full Text
View/download PDF

18. Bring It on! Challenges Encountered While Building a Comprehensive Tutoring System Using ReaderBench

Author

Panaite, Marilena, Dascalu, Mihai, Johnson, Amy, Balyan, Renu, Dai, Jianmin, McNamara, Danielle S., Trausan-Matu, Stefan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Penstein Rosé, Carolyn, editor, Martínez-Maldonado, Roberto, editor, Hoppe, H. Ulrich, editor, Luckin, Rose, editor, Mavrikis, Manolis, editor, Porayska-Pomsta, Kaska, editor, McLaren, Bruce, editor, and du Boulay, Benedict, editor

Published
2018
Full Text
View/download PDF

19. A Survey on Using Linguistic Markers for Diagnosing Neuropsychiatric Disorders with Artificial Intelligence.

Author

Zaman, Ioana-Raluca and Trausan-Matu, Stefan

Subjects
*NEUROBEHAVIORAL disorders, *ARTIFICIAL intelligence, *SYMPTOMS, *NATURAL language processing, *COMPUTER vision, *DIAGNOSIS
Abstract

Neuropsychiatric disorders affect the lives of individuals from cognitive, emotional, and behavioral aspects, impact the quality of their lives, and even lead to death. Outside the medical area, these diseases have also started to be the subject of investigation in the field of Artificial Intelligence: especially Natural Language Processing (NLP) and Computer Vision. The usage of NLP techniques to understand medical symptoms eases the process of identifying and learning more about language-related aspects of neuropsychiatric conditions, leading to better diagnosis and treatment options. This survey shows the evolution of the detection of linguistic markers specific to a series of neuropsychiatric disorders and symptoms. For each disease or symptom, the article presents a medical description, specific linguistic markers, the results obtained using markers, and datasets. Furthermore, this paper offers a critical analysis of the work undertaken to date and suggests potential directions for future research in the field. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

20. Mass Customization in Continuing Medical Education: Automated Extraction of E-Learning Topics

Author

Nistor, Nicolae, Dascălu, Mihai, Guțu, Gabriel, Trăușan-Matu, Ștefan, Choi, Sunhea, Haberman-Lawson, Ashley, Brands, Brigitte Angela, Körner, Christian, Koletzko, Berthold, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lavoué, Élise, editor, Drachsler, Hendrik, editor, Verbert, Katrien, editor, Broisin, Julien, editor, and Pérez-Sanagustín, Mar, editor

Published
2017
Full Text
View/download PDF

21. ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity

Author

Dascalu, Mihai, Gutu, Gabriel, Ruseti, Stefan, Paraschiv, Ionut Cristian, Dessus, Philippe, McNamara, Danielle S., Crossley, Scott A., Trausan-Matu, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lavoué, Élise, editor, Drachsler, Hendrik, editor, Verbert, Katrien, editor, Broisin, Julien, editor, and Pérez-Sanagustín, Mar, editor

Published
2017
Full Text
View/download PDF

22. Expressing Sentiments in Game Reviews

Author

Secui, Ana, Sirbu, Maria-Dorinela, Dascalu, Mihai, Crossley, Scott, Ruseti, Stefan, Trausan-Matu, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dichev, Christo, editor, and Agre, Gennady, editor

Published
2016
Full Text
View/download PDF

23. Classifying Written Texts Through Rhythmic Features

Author

Balint, Mihaela, Dascalu, Mihai, Trausan-Matu, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dichev, Christo, editor, and Agre, Gennady, editor

Published
2016
Full Text
View/download PDF

24. Voice Control Framework for Form Based Applications

Author

Paraschiv, Ionut Cristian, Dascalu, Mihai, Trausan-Matu, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Agre, Gennady, editor, Hitzler, Pascal, editor, Krisnadhi, Adila A., editor, and Kuznetsov, Sergei O., editor

Published
2014
Full Text
View/download PDF

27. Overview and Preliminary Results of Using PolyCAFe for Collaboration Analysis and Feedback Generation

Author

Rebedea, Traian, Dascalu, Mihai, Trausan-Matu, Stefan, Banica, Dan, Gartner, Alexandru, Chiru, Costin, Mihaila, Dan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Wolpers, Martin, editor, Kirschner, Paul A., editor, Scheffel, Maren, editor, Lindstaedt, Stefanie, editor, and Dimitrova, Vania, editor

Published
2010
Full Text
View/download PDF

28. Automatic Support for the Analysis of Online Collaborative Learning Chat Conversations

Author

Trausan-Matu, Stefan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Tsang, Philip, editor, Cheung, Simon K. S., editor, Lee, Victor S. K., editor, and Huang, Ronghuai, editor

Published
2010
Full Text
View/download PDF

30. Extraction of Socio-semantic Data from Chat Conversations in Collaborative Learning Communities

Author

Rebedea, Traian, Trausan-Matu, Stefan, Chiru, Costin-Gabriel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Dillenbourg, Pierre, editor, and Specht, Marcus, editor

Published
2008
Full Text
View/download PDF

32. Flexible Querying of an Intelligent Information System for EU Joint Project Proposals in a Specific Topic

Author

Trausan-Matu, Stefan, Bantea, Ruxandra, Posea, Vlad, Petrescu, Diana, Gartner, Alexandru, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Esposito, Floriana, editor, Raś, Zbigniew W., editor, Malerba, Donato, editor, and Semeraro, Giovanni, editor

Published
2006
Full Text
View/download PDF

34. 'Please ReaderBench this text': A multi-dimensional textual complexity assessment framework

Author

Dascalu, Mihai, Crossley, Scott, Mcnamara, Danielle, Dessus, Philippe, Trausan-Matu, Stefan, 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, University of Georgia [USA], Arizona State University [Tempe] (ASU), Laboratoire de Recherche sur les Apprentissages en Contexte (LaRAC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Education, Institute of Education Sciences - Grant R305A130124, USA, Department of Defense, Office of Naval Research - Grants N00014140343 and N000141712300, USA, S. D. Craig, European Project: 644187,H2020,H2020-ICT-2014-1,RAGE(2015), Dessus, Philippe, and Realising an Applied Gaming Eco-system - RAGE - - H20202015-02-01 - 2019-01-31 - 644187 - VALID

Subjects
learning analytics, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, [SHS.EDU]Humanities and Social Sciences/Education, [SHS.EDU] Humanities and Social Sciences/Education, comprehension modeling, automated essay scoring, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, data analytics, Natural Language Processing
Abstract

International audience; A critical task for tutors is to provide learners with suitable reading materials in terms of difficulty. The challenge of this endeavor is increased by students' individual variability and the multiple levels in which complexity can vary, thus arguing for the necessity of automated systems to support teachers. This chapter describes ReaderBench, an open-source multi-dimensional and multilingual system that uses advanced Natural Language Processing techniques to assess textual complexity at multiple levels including surface-based, syntax, semantics and discourse structure. In contrast to other existing approaches, ReaderBench is centered on cohesion and makes extensive usage of two complementary models, i.e., Cohesion Network Analysis and the polyphonic model inspired from dialogism. The first model provides an in-depth view of discourse in terms of cohesive links, whereas the second one highlights interactions between points of view spanning throughout the discourse. In order to argue for its wide applicability and extensibility, two studies are introduced. The first study investigates the degree to which ReaderBench textual complexity indices differentiate between high and low cohesion texts. The ReaderBench indices led to a higher classification accuracy than those included in prior studies using Coh-Metrix and TAACO. In the second study, ReaderBench indices are used to predict the difficulty of a set of various texts. Although the high number of predictive indices (50 plus) accounted for less variance than previous studies, they make valuable contributions to our understanding of text due to their wide coverage.

Published
2018

35. Liftoff – ReaderBench introduces new online functionalities

Author

Gutu-Robu, Gabriel, Sirbu, Maria-Dorinela, Cristian Paraschiv, Ionut, Dascălu, Mihai, Dessus, Philippe, Trausan-Matu, Stefan, 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, University Politehnica of Bucarest, Laboratoire de Recherche sur les Apprentissages en Contexte (LaRAC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), README project 'Interactive and Innovative application for evaluating the readability of texts in Romanian Language and for improving users' writing styles', contract no. 114/15.09.2017, MySMIS 2014 code 119286., and Dessus, Philippe

Subjects
[INFO.EIAH] Computer Science [cs]/Technology for Human Learning, CSCL, [SHS.EDU]Humanities and Social Sciences/Education, [SHS.EDU] Humanities and Social Sciences/Education, Cohesion Network Analysis, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Semantic Models, Natural Language Processing
Abstract

International audience; Natural Language Processing (NLP) became a trending domain within recent years for many researches and companies due to its wide applicability and the new advances in technology. The aim of this paper is to introduce an updated version or our open-source NLP framework, ReaderBench (http://readerbench.com/), designed to support both students and tutors in multiple learning scenarios that encompass one or more of the following dimensions: Cohesion Network Analysis of discourse, textual complexity assessment, keywords’ extraction using Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and word2vec semantic models, as well as the analysis of online communities and discussions. The latest version of our ReaderBench framework (v4.1) includes: a) new features, Application Programing Interfaces (APIs) and visualizations (e.g., sociograms, analysis of interaction between participants inside a community), b) a new web interface written in Angular 6, and c) the integration of new technologies to increase performance (i.e., spaCy and AKKA), as well as modularity and ease of deployment (i.e., Artifactory and Maven modules). ReaderBench is a fully functional framework capable to enhance the quality of learning processes conducted in multiple languages (English, French, Romanian, Dutch, Spanish, and Italian), and covering both individual and collaborative assessments.

Published
2018

36. Identifying reading strategies employed by learners within their oral french self-explanations

Author

Panaite, Marilena, Dascalu, Mihai, Dessus, Philippe, Bianco, Maryse, Trausan-Matu, Stefan, 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, Laboratoire de Recherche sur les Apprentissages en Contexte (LaRAC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), I. Roceanu, ANR-10-BLAN-1907,DEVCOMP,Le développement de la compréhension des textes: analyse des habiletés sous-jacentes et consruction d'un outil d'évaluation(2010), European Project: 644187,H2020,H2020-ICT-2014-1,RAGE(2015), Dessus, Philippe, BLANC - Le développement de la compréhension des textes: analyse des habiletés sous-jacentes et consruction d'un outil d'évaluation - - DEVCOMP2010 - ANR-10-BLAN-1907 - BLANC - VALID, and Realising an Applied Gaming Eco-system - RAGE - - H20202015-02-01 - 2019-01-31 - 644187 - VALID

Subjects
Science and knowledge, Librarianship, [SHS.EDU]Humanities and Social Sciences/Education, Institution, Publications, [SHS.EDU] Humanities and Social Sciences/Education, Documentation, Speech recognition, Computer science, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, Information, Automated identification of reading strategies, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, ReaderBench framework, Organization, Natural Language Processing
Abstract

International audience; Natural Language Processing has massively evolved during the last years and many up-to-date applications integrate different speech tools in order to create an enhanced user experiences. For obtaining a seamless integration of existing speech recognition systems, there is a trending interest for developing and improving existing speech-to-text algorithms. The aim of this paper is to improve user interaction with the ReaderBench platform, by developing and integrating a speech recognition module designed so that young pupils can dictate their self-explanations to a given text. Afterwards, the ReaderBench framework is used to automatically evaluate the employed reading strategies based on the resulted speech transcriptions. A dataset containing 160 self-explanations from students ranging from 9 to 11 years old was analysed using both original transcripts, and the ones automatically generated by our custom speech recognition system. Multiple methods designed to perform speech recognition are also compared, while a new dedicated model was trained in order to improve the quality of the existing French model for speech recognition from CMUSphinx speech recognition system. Our revised model includes a pronunciation dictionary obtained after training a Long Short-Term Memory (LSTM) Grapheme-to-Phoneme neural network. The accuracy of our system is benchmarked in relation to the automated process of identifying reading strategies implemented in our ReaderBench framework, which is applied on both manual transcriptions and automated speech-to-text inputs. The obtained results argue for the adequacy of our method as the slight decrease in terms of identification accuracy is justifiable in contrast to the effort of manually transcribing each self-explanation.

Published
2018

37. ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language

Author

Dascalu, Mihai, Westera, W., Ruseti, Stefan, Trausan-Matu, Stefan, Kurvers, H.J., André, Elisabeth, Baker, Ryan, Hu, Xiangen, T. Rodrigo, Ma. Mercedes, du Boulay, Benedict, RS-Theme Applied Gaming and Simulation, Rage project, Department FEEEL, André , Elisabeth, Baker, Ryan, Hu, Xiangen, T. Rodrigo , Ma. Mercedes, and du Boulay , Benedict

Subjects
Computer science, Readerbench framework, Automated segmentation, Dutch semantic models, 02 engineering and technology, Automated essay scoring, computer.software_genre, 050105 experimental psychology, Dutch language, Complexity index, Discriminant function analysis, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Learning, 0501 psychology and cognitive sciences, Structure (mathematical logic), business.industry, 4. Education, 05 social sciences, academic performance, Variance (accounting), Range (mathematics), Artificial intelligence, business, Games, computer, Natural language processing, textual complexity assessment
Abstract

Automated Essay Scoring has gained a wider applicability and usage with the integration of advanced Natural Language Processing techniques which enabled in-depth analyses of discourse in order capture the specificities of written texts. In this paper, we introduce a novel Automatic Essay Scoring method for Dutch language, built within the Readerbench framework, which encompasses a wide range of textual complexity indices, as well as an automated segmentation approach. Our method was evaluated on a corpus of 173 technical reports automatically split into sections and subsections, thus forming a hierarchical structure on which textual complexity indices were subsequently applied. The stepwise regression model explained 30.5% of the variance in students’ scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1%) whether they are high or low performance students.

Published
2017

38. Cohesion-Centered Analysis of CSCL Environments using ReaderBench

Author

Dascalu, Mihai, Gutu, Gabriel, Cristian Paraschiv, Ionut, Ruseti, Stefan, Dessus, Philippe, Mcnamara, Danielle, Crossley, Scott, Trausan-Matu, Stefan, 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, Laboratoire de Recherche sur les Apprentissages en Contexte (LaRAC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Arizona State University [Tempe] (ASU), University of Georgia [USA], and Dessus, Philippe

Subjects
[INFO.EIAH] Computer Science [cs]/Technology for Human Learning, CSCL, [SHS.EDU]Humanities and Social Sciences/Education, [SHS.EDU] Humanities and Social Sciences/Education, Cohesion Network Analysis, Virtual Communities, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Textual Cohesion, Massive Open Online Course, Natural Language Processing
Abstract

International audience; Computer Supported Collaborative Learning (CSCL) environments have become a viable learning alternative from which valuable data can be extracted and used for advanced analyses centered on evaluating participants' involvement and their interactions. Such automated assessments are implemented within our ReaderBench framework, a Natural Language Processing platform that contains multiple advanced text analysis functionalities. The ReaderBench framework is based on Cohesion Network Analysis from which different sociograms, relying on semantic similarity, are generated in order to reflect interactions between participants. In this paper, we briefly describe the enforced mechanisms used to analyze three scenarios: individual chat conversations, virtual communities of practice and MOOCs.

Published
2017

39. ReaderBench, the learning companion

Author

Dascalu, Mihai, Dessus, Philippe, Trausan-Matu, Stefan, 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, Laboratoire de Recherche sur les Apprentissages en Contexte (LaRAC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Dessus, Philippe

Subjects
[INFO.EIAH] Computer Science [cs]/Technology for Human Learning, [SHS.EDU]Humanities and Social Sciences/Education, [SHS.EDU] Humanities and Social Sciences/Education, Intelligent tutoring system, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Natural Language Processing
Abstract

International audience; ReaderBench is a fully functional automated software framework, designed to be an educational helper for students, teachers, and tutors. The system makes use of text-mining techniques based on advanced natural language processing and machine learning algorithms to design and deliver summative and formative assessments using multiple data sets (e.g., textual materials, behavior tracks, self-explanations).

Published
2016

40. Curricula Customization with the Readerbench Framework.

Author

GUTU-ROBU, Gabriel, DASCALU, Mihai, CARABAS, Mihai, ELISEI, Alexandru, TRAUSAN-MATU, Stefan, NISTOR, Nicolae, CHOI, Sunhea, SPOERL, Rebecca, and KOLETZKO, Berthold

Subjects
CURRICULUM change, MASSIVE open online courses, MOBILE learning, COHESION (Linguistics), NATURAL language processing
Abstract

Providing customized curricula tailored to learner's needs became a stringent problem while relating to the increasing number of people attending Massive Open Online Courses (MOOCs) and eLearning platforms because the same content is provided to all students. This study presents a Moodle plugin created on top of an eLearning course that enables curricula customization based on the learning needs of a high number of participants. With the help of the Mass Customization approach, two categories of attendees were identified in a previous research and imposed multiple filtering criteria, out of which the first one refers to participants' profession. The second criterion, topics of interest, allows learners to select keywords of interest from a predefined two-level word list, but also to enumerate their own terms using natural language. With the support of ReaderBench, an advanced Natural Language Processing framework, the most relevant lessons are retrieved in descending order of semantic relatedness. Third, an additional specific parameter allows participants to establish what kind of learning materials they require - i.e., theoretical and background oriented, practice and counseling documents, or guidelines. Our collection of documents is composed of lessons with a short description and their title, together with lists of pre- and post-requisite lessons. Our tool provides a comprehensive list of recommended lessons that best match the input criteria, corroborated with the list of related pre- and post-requisite lessons. Moreover, we provide information in terms of the duration of each lesson, as well as potential Continuous Medical Education points gained after finishing all selected lessons. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

41. ReaderBench, an Environment for Analyzing Text Complexity and Reading Strategies

Author

Dascalu, Mihai, Dessus, Philippe, Trausan-Matu, Stefan, Bianco, Maryse, Nardy, Aurélie, Dascălu, Mihai, Trăușan-Matu, Ștefan, Computer Science Department [Bucarest], University Politehnica of Bucharest [Romania] (UPB), Laboratoire des Sciences de l'Éducation (Grenoble) (LSE), Université Pierre Mendès France - Grenoble 2 (UPMF), MeTAH, Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Laboratoire des Sciences de l'Éducation (Grenoble) (LSE), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Pierre Mendès France - Grenoble 2 (UPMF), 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, H. Chad Lane, Kalina Yacef, Jack Mostow, and Philip Pavlik

Subjects
Computer science, [SHS.EDU]Humanities and Social Sciences/Education, 02 engineering and technology, computer.software_genre, Latent Dirichlet allocation, symbols.namesake, Text Cohesion, Support Vector Machines, 0202 electrical engineering, electronic engineering, information engineering, Latent Dirichlet Allocation, Reading Strategies, business.industry, Latent semantic analysis, 4. Education, 05 social sciences, 050301 education, Comprehension, Cohesion (linguistics), Textual Complexity, Latent Semantic Analysis, symbols, 020201 artificial intelligence & image processing, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Artificial intelligence, business, 0503 education, computer, Natural language processing
Abstract

Session: Educational Data Mining; International audience; ReaderBench is a multi-purpose, multi-lingual and flexible environment that enables the assessment of a wide range of learners' productions and their manipulation by the teacher. ReaderBench allows the assessment of three main textual features: cohesion-based assessment, reading strategies identification and textual complexity evaluation, which have been subject to empirical validations. ReaderBench covers a complete cycle, from the initial complexity assessment of reading materials, the assignment of texts to learners, the capture of metacognitions reflected in one's textual verbalizations and comprehension evaluation, therefore fostering learner's self-regulation process.

Published
2013

42. A Program For Analyzing Classical Arabic Poetry For Teaching Purposes.

Author

Ahmed, Munef Abdullah and Trausan-Matu, Stefan

Subjects
ARABIC language education, ARABIC poetry, NATURAL language processing, ONLINE education, PYTHON programming language
Abstract

A difficult but important aspect of e-learning for English language speakers is teaching the Arabic language. The major task for students in this context is analyzing Arabic poetry, which differs in several aspects from the English poetry. Natural Language Processing (NLP) for poetry analysis, and especially, being excellent in finding Arabic poetic characteristics is extremely difficult. Despite the rapid progress in this area in some international languages, the analysis of classical Arabic poetry has not received a sufficient attention due to the difficulty of the Arabic language, and the difficulties of analyzing its poetic theories. In this paper, a new way of finding the characteristics of the classical Arabic poem was introduced. One of the most important characteristics of Arabic poetry is the rhythm which is considered as the crown of this type of poem. The rhyme, which is the end of the musical tone of each verse in a poem, and the types of rhyme are also important features of any poetic art. In this article, we focused on the short vowels ("dammah", "kasra", "fatha", "tanween", and "shade"), mobile and constant letters which depend on the theory that analyzes the characteristics of the classical Arabic poetry introduced by Al-Khalil bin Ahmed Al Farahidi. An analysis program was written in a Python platform, which analyzes poems and provides the aforementioned features and some statistical measures. The program will be developed for an e-learning platform, and for use in the classification and identification of classical Arabic poems. [ABSTRACT FROM AUTHOR]

Published
2017

43. 2nd Workshop 'Natural language processing in support of learning: Metrics, feedback and connectivity (NLPsL 2010)'

Author

Trausan-Matu, Stefan, Dessus, Philippe, 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, Laboratoire des Sciences de l'Éducation (Grenoble) (LSE), Université Pierre Mendès France - Grenoble 2 (UPMF), LTfLL, and European Project: 212578,EC:FP7:ICT,FP7-ICT-2007-1,LTFLL(2008)

Subjects
TAL, [SHS.EDU]Humanities and Social Sciences/Education, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Natural Language Processing
Abstract

The purpose of this workshop was to focus on using language technologies in support of learning and teaching - by trying to identify what questions and problems are solved, but also to raise and discuss how well the metrics and algorithms developed assist in the provision of support and the construction of feedback for learning. What are the most efficient ways? To what extent do they match distances inferred by a teacher's assessments? The workshop addresses the problem of how support can be provided and feedback be generated in order to help students learn and teachers to assess their progress.

Published
2010

44. Implementing Bakhtin's Dialogism Theory with NLP Techniques in Distance Learning Environments

Author

Dessus, Philippe, Trausan-Matu, Stefan, Laboratoire des Sciences de l'Éducation (Grenoble) (LSE), Université Pierre Mendès France - Grenoble 2 (UPMF), 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, Stefan Trausan-Matu, Philippe Dessus, European Project: 212578,EC:FP7:ICT,FP7-ICT-2007-1,LTFLL(2008), Dessus, Philippe, and Language Technology for Lifelong Learning - LTFLL - - EC:FP7:ICT2008-03-01 - 2011-02-28 - 212578 - VALID

Subjects
Dialogism, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, CSCL, Latent Semantic Analysis, [SHS.EDU]Humanities and Social Sciences/Education, [SHS.EDU] Humanities and Social Sciences/Education, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Natural Language Processing
Abstract

International audience; The purpose of this paper is to present and discuss a Natural Language Processing-based operationalization of Bakhtin's dialogism ideas, and to insert it in the context of distance learning. Three core concepts from Bakhin are introduced and their application in the domain of distance learning discussed. Then, two computer-based systems based on these ideas, PolyCAFe and Pensum are presented.

Published
2010

45. A SEMANTIC APPROACH TO ANALYZE SCIENTIFIC PAPER ABSTRACTS.

Author

PARASCHIV, Ionut Cristian, DASCALU, Mihai, TRAUSAN-MATU, Stefan, and DESSUS, Philippe

Subjects
SEMANTICS, CITATION analysis, NATURAL language processing, DIRICHLET problem, LEARNING curve
Abstract

Each domain and its underlying communities evolve in time and each period is centered on specific topics that emerge from textual sources that characterize the domain. Our analysis represents an extension of other researches performed on the same corpora that were focusing more on evaluating co-citations between the articles in order to compute their importance score (Grauwin and Jensen [1]). Our approach presents a general perspective of the domain by performing semantic comparisons between article abstracts using natural language processing techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or semantic distances in lexicalized ontologies, i.e. WordNet. Moreover, graph visual representations are generated using Gephi in order to highlight the keywords of each paper and of the domain, the document similarity view or the table of keyword-abstract overlap score. The purpose of the views is to minimize the learning curve of the domain and to facilitate the research process for someone interested in a particular subject. Also, in order to further argue the benefits of our approach, some potential refinements of the methods for classification that can be performed as future improvements are presented. [ABSTRACT FROM AUTHOR]

Published
2015
Full Text
View/download PDF

46. A ventriloquism perspective on Natural Language Processing.

Author

Trausan-Matu, Stefan

Subjects
VENTRILOQUISM, NATURAL language processing, PUBLIC administration, NATION building, STATE power
Abstract

Natural language interfaces and, in general, Natural Language Processing (NLP) state of the art applications cannot handle satisfactory (if they can at all) conversational implicatures, illocutionary force, stylization, parody, inner disguised polemic, disguised dialogue and other similar phenomena. Considering the ventriloquism perspective in the context of the dialogic-polyphonic theories of Mikhael Bakhtin may provide a basis on trying to cope with such phenomena. The paper introduces some basic ideas of Bakhtin related to ventriloquism in an extended sense, discusses the approaches based on ventriloquism in other domains than NLP, then it analyzes several NLP applications from the perspective of this concept. Several ideas on how the analysis of ventriloquism might be done in NLP are introduced. [ABSTRACT FROM AUTHOR]

Published
2016

47. A System for the Automatic Analysis of Computer-Supported Collaborative Learning Chats.

Author

Trausan-Matu, Stefan, Dascalu, Mihai, and Rebedea, Traian

Abstract

The paper presents a system for helping the analysis of Computer-Supported Collaborative Learning chat (instant messenger) sessions, starting from a polyphonic model of the discourse inspired by the dialogistics of Bakhtin. Some theoretical basics of the model are presented, followed by implementation details and validation results. [ABSTRACT FROM PUBLISHER]

Published
2012
Full Text
View/download PDF

48. A Tool for Discourse Analysis and Visualization.

Author

Chiru, Costin-Gabriel and Trausan-Matu, Stefan

Abstract

In this paper we present a system that combines the cognitive and socio-cultural paradigms existent in the field of discourse analysis in order to analyze both narrations and conversations. The novelty of our approach is that existing applications are oriented on analyzing only one of these two types, an adaptation being necessary for the analysis of the other type. [ABSTRACT FROM PUBLISHER]

Published
2012
Full Text
View/download PDF

49. Identifying the Structure of CSCL Conversations Using String Kernels.

Author

Masala, Mihai, Ruseti, Stefan, Rebedea, Traian, Dascalu, Mihai, Gutu-Robu, Gabriel, and Trausan-Matu, Stefan

Subjects
NATURAL language processing, COLLABORATIVE learning
Abstract

Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow by teachers due to the mixture of multiple and many times concurrent discussion threads, with different interaction patterns between participants. Automated guidance can be provided with the help of Natural Language Processing techniques that target the identification of topic mixtures and of semantic links between utterances in order to adequately observe the debate and continuation of ideas. This paper introduces a method for discovering such semantic links embedded within chat conversations using string kernels, word embeddings, and neural networks. Our approach was validated on two datasets and obtained state-of-the-art results on both. Trained on a relatively small set of conversations, our models relying on string kernels are very effective for detecting such semantic links with a matching accuracy larger than 50% and represent a better alternative to complex deep neural networks, frequently employed in various Natural Language Processing tasks where large datasets are available. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

50. Toward Automatic Inference of Causal Structure in Student Essays

Author

Hastings, Peter, Hughes, Simon, Britt, Anne, Blaum, Dylan, Wallace, Patty, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Trausan-Matu, Stefan, editor, Boyer, Kristy Elizabeth, editor, Crosby, Martha, editor, and Panourgia, Kitty, editor

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results