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Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics & Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.

The use of computational systems for enhancing collaborative learning is widely spread. While interest in the application of Computer-Supported Collaborative Learning (CSCL) systems in cybersecurity exists, endeavors in this sense are difficult to compare due to different choices of methods and tools. In this sense, we advance a typology of CSCL systems in cybersecurity and introduce an automated system, Yggdrasil, which identifies emerging vulnerability threats from tweets containing references to explicit news articles. Such a system can be used to enhance collaborative learning processes of experts and non-experts in cybersecurity regarding the early detection of vulnerabilities, by automatically extracting information from online communities. For this purpose, we built a corpus of 650 annotated tweets that contain links to cybersecurity related articles, posted by key profiles in the cybersecurity landscape. Using insights from state-of-the-art approaches in early detection of vulnerabilities from tweets and sentiment analysis from Twitter, we conducted two experiments for the development of Yggdrasil. Our results argue that the use of the BERT language model is an adequate approach for identifying relevant posts or news articles, having an accuracy over 91% in various configurations. Furthermore, we also illustrate the use of transfer learning as a viable alternative for the early detection of cybernetic vulnerabilities.

The drawbacks of traditional methods of cybernetic vulnerability detection relate to the required time to identify new threats, to register them in the Common Vulnerabilities and Exposures (CVE) records, and to score them with the Common Vulnerabilities Scoring System (CVSS). These problems can be mitigated by early vulnerability detection systems relying on social media and open-source data. This paper presents a model that aims to identify emerging cybernetic vulnerabilities in cybersecurity news articles, as part of a system for automatic detection of early cybernetic threats using Open Source Intelligence (OSINT). Three machine learning models were trained on a novel dataset of 1000 labeled news articles to create a strong baseline for classifying cybersecurity articles as relevant (i.e., introducing new security threats), or irrelevant: Support Vector Machines, a Multinomial Naive Bayes classifier, and a finetuned BERT model. The BERT model obtained the best performance with a mean accuracy of 88.45% on the test dataset. Our experiments support the conclusion that Natural Language Processing (NLP) models are an appropriate choice for early vulnerability detection systems in order to extract relevant information from cybersecurity news articles.

Execution of complex tasks can be optimized by using parallel computing integrated within specialized frameworks. Thus, the AKKA framework was employed to speed up the processing time of the HUB-TECH platform to recommend semantically relevant articles, starting from a project description. Both parallel and distributed approaches are presented and a speed-up analysis between the two is performed. Distributed architectures enhance the system's performance by computing an increased number of semantic distances in less time. This usually requires more physical resources than parallel processing which is also capable of efficiently exploiting single nodes. The hardware specifications of the machine are relevant when trying to determine the optimal number of actors that can run on a machine in order to produce the maximal throughput. Our results show a 22% decrease in execution time while comparing serial and parallel approaches, a drastic 86% decrease when distributed methods are employed in contrast to serial executions, as well as an 82% improvement when comparing distributed and parallel experiments.

Nowadays blogging has become one of the most common and frequently employed methods for creating communities around specific topics of interest. This phenomenon is due to the existence of numerous online platforms that enable the creation with ease of blogs even for non-technical users. However, finding relevant blogs is a difficult and time-consuming process. This paper focuses on predicting whether a post has an impact or not based only on its writing style and automatically inferred textual complexity indices. We define the impact of a post as the traction it receives from other users in terms of number of received comments. Thus, several blogs were crawled on which a set of textual complexity indices available from the ReaderBench framework were computed. By splitting the posts in two categories, we were able to predict with an accuracy of 69.7% whether a post will receive comments or not.