Abstract
Nowadays, the consumption of media content has been growing rapidly and consistently, driven by an easy access to Video on Demand platforms. In this context, licensing is needed to ensure that filmmakers receive rightful payment for their content and ensure that their rights as content owners are respected. The traditional licensing process, which is heavily dependent on third parties (legal entities) to mediate the transaction, is very long, costly, and complex, which is a barrier to smaller independent filmmakers. The solution proposed in this work, to address this problem, is to create a business-to-business marketplace platform supported by a Blockchain licensing module. This module takes advantage of Blockchain technology to ensure the licensing requirements and to provide a secure, practical and straightforward way to license media in a decentralised paradigm. The result of this work was validated though a prototype, and a global assessment of the system's usability was performed using the System Usability Scale, where it got the best possible grade.

Abstract
Background: Preterm birth is a global health concern. Its adverse consequences may persist throughout the life course, exerting a potentially heavy burden on families, health systems, and societies. In high-income countries, the first children who benefited from improved care are now adults entering middle age. However, there is a clear gap in the knowledge regarding the long-term outcomes of individuals born preterm. Objective: This study aimed to assess the feasibility of recruiting and following up an e-cohort of adults born preterm worldwide and provide estimations of participation, characteristics of participants, the acceptability of questions, and the quality of data collected. Methods: We implemented a prospective, open, observational, and international e-cohort pilot study (Health of Adult People Born Preterm-an e-Cohort Pilot Study [HAPP-e]). Inclusion criteria were being an adult (aged =18 years), born preterm (<37 weeks of gestation), having internet access and an email address, and understanding at least 1 of the available languages. A large, multifaceted, and multilingual communication strategy was established. Between December 2019 and June 2021, inclusion and repeated data collection were performed using a secured web platform. We provided descriptive statistics regarding participation in the e-cohort, namely, the number of persons who registered on the platform, signed the consent form, initiated and completed the baseline questionnaire, and initiated and completed the follow-up questionnaire. We also described the main characteristics of the HAPP-e participants and provided an assessment of the quality of the data and the acceptability of sensitive questions. Results: As of December 31, 2020, a total of 1004 persons had registered on the platform, leading to 527 accounts with a confirmed email and 333 signed consent forms. A total of 333 participants initiated the baseline questionnaire. All participants were invited to follow-up, and 35.7% (119/333) consented to participate, of whom 97.5% (116/119) initiated the follow-up questionnaire. Completion rates were very high both at baseline (296/333, 88.9%) and at follow-up (112/116, 96.6%). This sample of adults born preterm in 34 countries covered a wide range of sociodemographic and health characteristics. The gestational age at birth ranged from 23+6 to 36+6 weeks (median 32, IQR 29-35 weeks). Only 2.1% (7/333) of the participants had previously participated in a cohort of individuals born preterm. Women (252/333, 75.7%) and highly educated participants (235/327, 71.9%) were also overrepresented. Good quality data were collected thanks to validation controls implemented on the web platform. The acceptability of potentially sensitive questions was excellent, as very few participants chose the I prefer not to say option when available. Conclusions: Although we identified room for improvement in specific procedures, this pilot study confirmed the great potential for recruiting a large and diverse sample of adults born preterm worldwide, thereby advancing research on adults born preterm.

Abstract
Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.

Abstract

Abstract
Formal verification techniques aim at formally proving the correctness of a computer program with respect to a formal specification, but the expertise and effort required for applying formal specification and verification techniques and scalability issues have limited their practical application. In recent years, the tremendous progress with SAT and SMT solvers enabled the construction of a new generation of tools that promise to make formal verification more accessible for software engineers, by automating most if not all of the verification process. The Dafny system is a prominent example of that trend. However, little evidence exists yet about its accessibility. To help fill this gap, we conducted a set of 10 case studies of developing verified implementations in Dafny of some real-world algorithms and data structures, to determine its accessibility for software engineers. We found that, on average, the amount of code written for specification and verification purposes is of the same order of magnitude as the traditional code written for implementation and testing purposes (ratio of 1.14) – an “overhead” that certainly pays off for high-integrity software. The performance of the Dafny verifier was impressive, with 2.4 proof obligations generated per line of code written, and 24 ms spent per proof obligation generated and verified, on average. However, we also found that the manual work needed in writing auxiliary verification code may be significant and difficult to predict and master. Hence, further automation and systematization of verification tasks are possible directions for future advances in the field. © 2023, IFIP International Federation for Information Processing.

Abstract
Context: Low-code development is a concept whose presence has grown both in academia and the software industry and is discussed alongside others, such as model-driven engineering and domain-specific languages. Usability is an important concept in low-code contexts since users of these tools often lack a background in programming. Grey literature articles have also stated that low-code tools have high usability.Objective: This paper examines the current literature about low-code and no-code to discover more about them and their relationship with usability, particularly its quality, which factors are the most relevant, and how users view these tools. This focus on usability aims to provide a different point of view from other works on low-code.Method: We performed a systematic literature review based on a formal protocol for this study. The search protocol returned a total of 207 peer-review articles across five databases, which was supplemented with a snowballing process. These were filtered using inclusion and exclusion criteria, resulting in 38 relevant articles that were analysed, synthesised and reported.Conclusion: Despite growing interest and a strong enterprise presence in academia, we did not find a formal definition of low-code, although common characteristics have been specified. We found that users have a heightened awareness of usability regarding low-code tools, with some authors performing feasibility studies on their implementations or listing factors that influence the user experience in a given tool. Researchers are considering usability factors unconsciously, and the low-code field would grow if research on usability increased. This paper also suggests a definition for low-code development.