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Data-driven expert team formation is a complicated and multifaceted process that requires access to accurate information to identify researchers’ areas and level of expertise and their collaborative prospects. In this respect, bibliometric data represents a valuable and reliable source of information that can be effectively employed in revealing key insights regarding candidates. Due to its complex and complete structure of publication metadata records, IEEE Xplore database may offer the possibility to compute an extensive set of indicators about researchers’ publication production and how they have interacted during time. Considering the case of Politehnica University of Timisoara scholars for the interval 2010–2022, current dataset encapsulates relevant and rich information for assembling multidisciplinary research teams, being also a testing ground for experimenting and calibrating the expert team formation methods and mechanisms.

Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we take a step further by more accurately modeling the research themes as finite sets of key terms. The proposed approach involves two stages: identifying the research themes from paper metadata using LDA topic modeling; and, evaluation of research theme trends by employing a version of the Mann-Kendall test that is able to cope with multivariate time series of term occurrences. The results obtained by applying this general methodology to Information Security domain confirm its viability.

Sentiment analysis of domain-specific short messages (DSSMs) raises challenges due to their peculiar nature, which can often include field-specific terminology, jargon, and abbreviations. In this paper, we investigate the distinctive characteristics of user-generated content across multiple domains, with DSSMs serving as the central point. With cross-domain models on the rise, we examine the capability of the models to accurately interpret hidden meanings embedded in domain-specific terminology. For our investigation, we utilize three different community platform datasets: a Jira dataset for DSSMs as it contains particular vocabulary related to software engineering, a Twitter dataset for domain-independent short messages (DISMs) because it holds everyday speech type of language, and a Reddit dataset as an intermediary case. Through machine learning techniques, we thus explore whether software engineering short messages exhibit notable differences compared to regular messages. For this, we utilized the cross-domain knowledge transfer approach and RoBERTa sentiment analysis technique to prove the existence of efficient models in addressing DSSMs challenges across multiple domains. Our study reveals that DSSMs are semantically different from DISMs due to F1 score differences generated by the models.

Following the numerous attacks that exploited vulnerabilities of Controller Area Networks (CAN), intrusion detection systems have become a topic of prime importance for in-vehicle buses. Newer in-vehicle communication layers, such as CAN-FD, despite the larger payloads which can easily integrate cryptographic elements, need similar attention. But detecting intrusions may call for demanding algorithms that are not computationally cheap while timely detection is necessary in order to process frames in real-time and take the appropriate actions. In this work we evaluate the performance of several binary classifiers on traditional in-vehicle Electronic Control Units (ECUs) and compare them to modern Android devices which have become widespread inside cars with the adoption of Android-capable infotainment systems. Needless to say, these modern devices benefit from higher computational and memory resources while cloud connectivity may alleviate computational costs even further. Contrasting between traditional controllers and Android devices has become necessary and so far there have been little efforts in this direction. To create a realistic testbed, we use collected in-vehicle CAN bus traffic from an SUV as well as more demanding logs from Advanced Driver-assistance Systems (ADAS) implemented on CAN-FD which we augment with adversarial activity.

Identifying possible research gaps is a main step in problem framing, however it is increasingly tedious and expensive considering the continuously growing amount of published material. This situation suggests the critical need for methodologies and tools that can assist researchers in their selection of future research topics. Related work mostly focuses on trend analysis and impact prediction but less on research gap identification. This paper presents our first approach in automated identification of feasible research gaps by using a double-threshold procedure to eliminate the research gaps that are currently difficult to study or offer little novelty. Gaps are then found by extracting subgraphs for the less-frequent co-occurrences and correlations of key terms describing domains. A case study applying the methodology for electronic design automation (EDA) domain is also discussed in the paper.

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.