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Dialogism provides the grounds for building a comprehensivemodel of discourse and it is focused on the multiplicity ofperspectives (i.e., voices). Dialogism can be present in anytype of text, while voices become themes or recurrent topicsemerging from the discourse. In this study, we examine theextent that differences between self-explanations and think-alouds can be detected using computational textual indicesderived from dialogism. Students (n = 68) read a text aboutnatural selection and were instructed to generate self-explanations or think-alouds. The linguistic features of thesetext responses were analyzed using ReaderBench, anautomated text analysis tool. A discriminant function analysisusing these features correctly classified 80.9% of the students’assigned experimental conditions (self-explanation vs. thinkaloud). Our results indicate that self-explanation promotestext processing that focuses on connected ideas, rather thanseparate voices or points of view covering multiple topics.

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.

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]

The exponential growth of scientific publications increases the effort required to identify relevant articles. Moreover, the scale of studies is a frequent barrier to research as the majority of studies are low or medium-scaled and do not generalize well while lacking statistical power. As such, we introduce an automated method that supports the identification of large-scale studies in terms of population. First, we introduce a training corpus of 1229 manually annotated paragraphs extracted from 20 articles with different structures and considered populations. Our method considers prompting a FLAN-T5 language model with targeted questions and paragraphs from the previous corpus so that the model returns the number of participants from the study. We adopt a dialogic extensible approach in which the model is asked a sequence of questions that are gradual in terms of focus. Second, we use a validation corpus with 200 articles labeled for having N larger than 1000 to assess the performance of our language model. Our model, without any preliminary filtering with heuristics, achieves an F1 score of 0.52, surpassing previous analyses performed that obtained an F1 score of 0.51. Moreover, we achieved an F1 score of 0.69 when combined with previous extraction heuristics, thus arguing for the robustness and extensibility of our approach. Finally, we apply our model to a newly introduced dataset of ERIC publications to observe trends across the years in the Education domain. A spike was observed in 2019, followed by a decrease in 2020 and, afterward, a positive trend; nevertheless, the overall percentage is lower than 3%, suggesting a major problem in terms of scale and the need for a change in perspective. [ABSTRACT FROM AUTHOR]

As Computer-Supported Collaborative Learning (CSCL) gains a broader usage, the need for automated tools capable of supporting tutors in the time-consuming process of analyzing conversations becomes more pressing. Moreover, collaboration, which presumes the intertwining of ideas or points of view among participants, is a central element of dialogue performed in CSCL environments. Therefore, starting from dialogism and a cohesion-based model of discourse, we propose and validate two computational models for assessing collaboration. The first model is based on a cohesion graph and can be perceived as a longitudinal analysis of the ongoing conversation, thus accounting for collaboration from a social knowledge-building perspective. In the second approach, collaboration is regarded from a dialogical perspective as the intertwining or synergy of voices pertaining to different speakers, therefore enabling a transversal analysis of subsequent discussion slices.

In this paper, a new method that computes the aesthetics of a melody fragment is proposed, starting from dissonances. While music generated with artificial intelligence applications may be produced considerably more quickly than human-composed music, it has the drawback of not being appreciated like a human composition, being many times perceived by humans as artificial. For achieving supervised machine learning objectives of improving the quality of the great number of generated melodies, it is a challenge to ask humans to grade them. Therefore, it would be preferable if the aesthetics of artificial-intelligence-generated music is calculated by an algorithm. The proposed method in this paper is based on a neural network and a mathematical formula, which has been developed with the help of a study in which 108 students evaluated the aesthetics of several melodies. For evaluation, numerical values generated by this method were compared with ratings provided by human listeners from a second study in which 30 students participated and scores were generated by an existing different method developed by psychologists and three other methods developed by musicians. Our method achieved a Pearson correlation of 0.49 with human aesthetic scores, which is a much better result than other methods obtained. Additionally, our method made a distinction between human-composed melodies and artificial-intelligence-generated scores in the same way that human listeners did. [ABSTRACT FROM AUTHOR]

The research presented in the paper aims at increasing the capacity to identify security weaknesses in programming languages that are less supported by specialized security analysis tools, based on the knowledge gathered from securing the popular ones, for which security experts, scanners, and labeled datasets are, in general, available. This goal is vital in reducing the overall exposure of software applications. We propose a solution to expand the capabilities of security gaps detection to downstream languages, influenced by their more popular "ancestors" from the programming languages' evolutionary tree, using language keyword tokenization and clustering based on word embedding techniques. We show that after training a machine learning algorithm on C, C++, and Java applications developed by a community of programmers with similar behavior of writing code, we can detect, with acceptable accuracy, similar vulnerabilities in C# source code written by the same community. To achieve this, we propose a core cross-language representation of source code, optimized for security weaknesses classifiers, named CLaSCoRe. Using this method, we can achieve zero-shot vulnerability detection—in our case, without using any training data with C# source code. [ABSTRACT FROM AUTHOR]

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.

Software developers represent the bastion of application security against the overwhelming cyber-attacks which target all organizations and affect their resilience. As security weaknesses which may be introduced during the process of code writing are complex and matching different and variate skills, most applications are launched intrinsically vulnerable. We have advanced our research for a security scanner able to use automated learning techniques based on machine learning algorithms to recognize patterns of security weaknesses in source code. To make the scanner independent on the programming language, the source code is converted to a vectorial representation using natural language processing methods, which are able to retain semantical traits of the original code and at the same time to reduce the dependency on the lexical structure of the program. The security flaws detection performance is in the ranges accepted by software security professionals (recall > 0.94) even when vulnerable samples are very low represented in the dataset (e.g., less than 4% vulnerable code for a specific CWE in the dataset). No significant change or adaptation is needed to change the source code language under scrutiny. We apply this approach on detecting Common Weaknesses Enumeration (CWE) vulnerabilities in datasets provided by NIST (Test suites–NIST Software Assurance Reference Dataset). [ABSTRACT FROM AUTHOR]

This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Naïve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges. [ABSTRACT FROM AUTHOR]

The broad use of computer-supported collaborative-learning (CSCL) environments (e.g., instant messenger-chats, forums, blogs in online communities, and massive open online courses) calls for automated tools to support tutors in the time-consuming process of analyzing collaborative conversations. In this article, the authors propose and validate the cohesion network analysis (CNA) model, housed within the ReaderBench platform. CNA, grounded in theories of cohesion, dialogism, and polyphony, is similar to social network analysis (SNA), but it also considers text content and discourse structure and, uniquely, uses automated cohesion indices to generate the underlying discourse representation. Thus, CNA enhances the power of SNA by explicitly considering semantic cohesion while modeling interactions between participants. The primary purpose of this article is to describe CNA analysis and to provide a proof of concept, by using ten chat conversations in which multiple participants debated the advantages of CSCL technologies. Each participant’s contributions were human-scored on the basis of their relevance in terms of covering the central concepts of the conversation. SNA metrics, applied to the CNA sociogram, were then used to assess the quality of each member’s degree of participation. The results revealed that the CNA indices were strongly correlated to the human evaluations of the conversations. Furthermore, a stepwise regression analysis indicated that the CNA indices collectively predicted 54% of the variance in the human ratings of participation. The results provide promising support for the use of automated computational assessments of collaborative participation and of individuals’ degrees of active involvement in CSCL environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]

Vocabulary acquisition is considered one of the most important aspects of a language because it defines and constraints communication. In order to overcome this, students and teachers use various techniques meant to improve the learning process and facilitate word acquisition. Using conceptual networks that establish links to prior knowledge is an approach with proven good results. The game presented in this paper, Semantic Taboo, is a serious game that helps users enlarge their conceptual network. The game has two modes, one for teachers and one for students. In the teacher module, users have the option of adding playing cards. The theme of the card (the seed) is taken as input, while the list of taboo words is selected from the list of semantically related words provided by the ReaderBench framework. The student model has the same game principle as the traditional game, but the list of taboo words and the description of the seed help the learner acquire new words. The preliminary validation consisted of 15 users who were impressed by the game concept and its educational value. [ABSTRACT FROM AUTHOR]

Serious games based on reading strategies are an efficient alternative for improving students' capabilities of text understanding. In today's industry and academic environments, there are various games that target reading strategies, but unfortunately most keep focus only on the educational component, leaving the user experience behind. These games are not appealing to young students and thus, may not fully achieve the desired outcomes. The serious game described in this paper puts emphasis on the user experience, tightly coupled with keeping a high standard of its educational focus. Preliminary validation experiments denote a high user acceptance of the proposed educational scenario as an alternative to the traditional scenario that consists of a lecture followed by a self-explanation of the input narrative text. [ABSTRACT FROM AUTHOR]

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]

This paper presents a research on using rhetorical structures for assessing collaborative processes in Computer-Supported Collaborative Learning (CSCL) chats. For this purpose, the ideas of Bakhtin's dialogism theory and Trausan-Matu's polyphonic model are used, starting from the identification of the threads of repeated words from chats. Cue phrases and their usage in linking the identified threads are also considered. The results are presented in statistical tables and graphics that ease the understanding of the collaborative process, helping teachers to analyze and assess students' collaborative chats. It also allows students to know and understand the interactions and how it contributes to the conversation. [ABSTRACT FROM AUTHOR]

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The paper presents a generic framework for an intelligent information system of competence management based on ontologies for information technology companies. In a first step it will be applied in an information technology (IT) small enterprise and then its applicability will be verified for other organizations of the same type. The work presented in the paper is performed under the project "CONTO - Ontology-based Competencies Management in Information Technology" funded by the Romanian Ministry of Education and Research, involving two universities, a research institute and an IT private company. A competence management system (CMS), in our vision has to achieve three functions: (a) to support the complete and systematic acquisition of knowledge about the competence of the members of an enterprise; (b) to provide the knowledge about competences and their owners; (c) to apply the available knowledge to serve a purpose. The core of the competence management information system is an ontology that plays the role of the declarative knowledge repository containing the basic concepts (such as: company-job, competence, domain, group, person etc.) and their relationships with other concepts, instances and properties. The Protégé environment was used for the development of this ontology. The structure of the ontology is conceived so that description logics can be used to represent the concept definitions of the application domain in a structured and formally well-understood way. Knowledge acquisition is performed in our approach by enriching the ontology, according to the requirements of the IT company. An advantage of using an ontology-based system is the possibility of the identification of new relations among concepts based on inferences starting from the existing knowledge. The user can choose to query instances of one type of concept. The paper also presents some use-cases. [ABSTRACT FROM AUTHOR]

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]

The paper presents the details and the history of building a corpus of instant messenger (chat) conversations with multiple participants used for Computer-Supported Collaborative Learning. Two XML annotation schemas are presented. The first is the basic one in the corpus. It is automatically generated starting from various formats in which chats are exported. The second schema extends the first one. It is generated by an editor for manually annotating the chats. Details of the editor are included. The corpus may be used in different purposes: machine learning, analysis and statistics computation. [ABSTRACT FROM AUTHOR]