Workflow models for heterogeneous distributed systems

The role of data in modern scientific workflows becomes more and more crucial. The unprecedented amount of data available in the digital era, combined with the recent advancements in Machine Learning and High-Performance Computing (HPC), let computers surpass human performances in a wide range of fields, such as Computer Vision, Natural Language Processing and Bioinformatics. However, a solid data management strategy becomes crucial for key aspects like performance optimisation, privacy preservation and security. Most modern programming paradigms for Big Data analysis adhere to the principle of data locality: moving computation closer to the data to remove transfer-related overheads and risks. Still, there are scenarios in which it is worth, or even unavoidable, to transfer data between different steps of a complex workflow. The contribution of this dissertation is twofold. First, it defines a novel methodology for distributed modular applications, allowing topology-aware scheduling and data management while separating business logic, data dependencies, parallel patterns and execution environments. In addition, it introduces computational notebooks as a high-level and user-friendly interface to this new kind of workflow, aiming to flatten the learning curve and improve the adoption of such methodology. Each of these contributions is accompanied by a full-fledged, Open Source implementation, which has been used for evaluation purposes and allows the interested reader to experience the related methodology first-hand. The validity of the proposed approaches has been demonstrated on a total of five real scientific applications in the domains of Deep Learning, Bioinformatics and Molecular Dynamics Simulation, executing them on large-scale mixed cloud-High-Performance Computing (HPC) infrastructures

The Constructivistly-Organised Dimensional-Appraisal (CODA) Model and Evidence for the Role of Goal-directed Processes in Emotional Episodes Induced by Music

The study of affective responses to music is a flourishing field. Advancements in the study of this phenomena have been complemented by the introduction of several music-specific models of emotion, with two of the most well-cited ones being the BRECVEMA and the Multifactorial Process Model. These two models have undoubtedly contributed to the field. However, contemporary developments in the wider affective sciences (broadly described as the ‘rise of affectivism’) have yet to be incorporated into the music emotion literature. These developments in the affective sciences may aid in addressing remaining gaps in the music literature, in particular for acknowledging individual and contextual differences. The first aim of this thesis was to outline contemporary theories from the wider affective sciences and subsequently critique current popular models of musical emotions through the lens of these advancements. The second aim was to propose a new model based on this critique: the Constructivistly-Organised Dimensional-Appraisal (CODA) model. This CODA model draws together multiple competing models into a single framework centralised around goal-directed appraisal mechanisms which are key to the wider affective sciences but are a less commonly acknowledged component of musical affect. The third aim was to empirically test some of the core hypotheses of the CODA model. In particular, examining goal-directed mechanisms, their validity in a musical context, and their ability to address individual and contextual differences in musically induced affect. Across four experiments which include exploratory and lab-based designs through to real- world applications, the results are supportive of the role of goal-directed mechanisms in musically induced emotional episodes. Experiment one presents a first test battery of multiple appraisal dimensions developed for music. The results show that several of the hypothesised appraisal dimensions are valid dimensions is a musical context. Moreover, these mechanisms cluster into goal-directed latent variables. Experiment two develops a new set of stimuli annotations relating to musical goals, showing that music can be more or less appropriate for different musical goals (functions). Experiment three, using the new stimuli set from experiment two, tests the effects of different goals with more or less appropriate music on musically induced affect. These results show that goal-directed mechanisms can change induced core-affect (valence and arousal) and intensity, even for the same piece of music. Experiment four extends the study of goal-directed mechanisms into a real-world context through an interdisciplinary and cross-cultural design. The final experiment demonstrates how goal-directed mechanisms can be manipulated through different algorithms to induce negative affect in a Colombian population. The main conclusions of this thesis are that the CODA model, more specifically goal-directed mechanisms, provide a valuable, non-reductive, and more efficient approach to addressing individual and contextual differences for musically induced emotional episodes in the new era of affectivism

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Decision-making in obstetric emergencies. Individual differences and professional boundaries.

In affluent nations, variations in obstetric care, particularly during emergencies, perplexingly manifest in differing intervention and outcome rates. Although these variations mirror systemic disparities, they are also suggested to reflect the interplay of social and professional interactions between obstetricians/gynecologists and midwives, stemming from adherence to distinct professional paradigms and the influence of personal factors on decision-making and collaboration. This thesis sought to unpack these complexities by exploring individual differences and professional perspectives in decision-making during obstetric emergencies through a blend of interpretive and statistical approaches in a series of studies.Utilizing a narrative methodology with in-depth interviews and subsequent thematic analysis, Papers I and IV investigated the experiences of obstetricians/gynecologists (N=17) and midwives (N=27) during obstetric emergencies. Paper I used images of artwork as associative triggers in interviews, helping to illuminate decisionmakingprocesses, while Paper IV critically evaluated its thematic findings through the sociological lens of “boundary work”. Concurrently, Papers II and III employed psychometric instruments, including online questionnaires and the Five Factor Model personality test, to collect and analyze data from obstetricians/gynecologists and midwives (N = 472 for Paper II and N = 447 for Paper III). This involved investigating variables, such as Decision-Making styles, Negative Impact of Inductions, Healthcare Crisis Experience, and Job Satisfaction, alongside personality dimensions and complementary variables through various statistical tests.The studies revealed a diversity of findings: Paper I highlights that obstetricians/gynecologists navigate flexible decision-making environments, crystallizing into one of three distinct styles intertwining with their identities and practice narratives. Paper II unveils a specific personality profile among obstetricians/gynecologists and demonstrates correlations between personality traits, particularly Neuroticism, and distinct decision-making styles, while spotlightinggender and experience as significant influential factors. Paper III identifies divergent perspectives between the professions regarding labor inductions and job satisfaction, and highlights correlations among job satisfaction, views on labor inductions, and Neuroticism. Lastly, Paper IV underscores the multifaceted roles of midwives, who navigate, and sometimes resist, medical hierarchies to advocate for women’s physical and emotional well-being during childbirth, in a manner reshaping healthcare norms yet potentially sustaining historical tensions with obstetricians/gynecologists.This research highlights the intricate ways in which the personal and professional identities of obstetricians/gynecologists and midwives impact decision-making during obstetric emergencies. These insights invite a thoughtful reevaluation: How can training, support systems, and collaboration be recalibrated to encompass theseinfluential dynamics comprehensively? How can we as practitioners create work environments that not only acknowledge but also actively integrate varied personal perspectives and professional values and goals

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Diversified Collaboration, Strategic Plan Design, and Strategic Planning Outcomes in Public Sector Aging Services

Effective strategic planning in aging services is needed to help public sector agencies and their networks successfully meet the needs of a growing and aging U.S. population of older adults. The purpose of this mixed-method study was to explore public sector state strategic plans and the effect of diversified collaboration, strategic plan design, and regional and state characteristics on related outcomes and organizational performance. This study reviewed State Plans on Aging and the effect of the diversity of stakeholders contributing to developing the plans, the comprehensiveness of the plans’ design, and the states’ performance in areas related to long-term services and supports. This study sought to provide insights that would add to the existing body of knowledge on public sector strategic planning, and help to enhance strategic planning activities aimed at improving services and supports for older adults. This study found that diversified collaboration and strategic plan design could have a positive effect on strategic planning outcomes. The study also employed a framework for studying strategic planning that answered previous calls for more research linking process/micro and practice/macro approaches to strategic planning research. Process-based research focuses on the microlevels of planning. Practice-based research focuses on the macro levels. When integrated together, these two types of strategic planning perspectives allow researchers to understand better how, why, and when strategic planning works. This study offers some insights into future research, provides implications for practice, and serves as a call to further action in addressing a broad social challenge

Identifying risk patterns for suicide attempts in individuals with diabetes : a data-driven approach using LASSO regression

Diabetes is a major health concern in the United States, with 34.2 million Americans affected in 2020. Unfortunately, the risk of suicide is also elevated in individuals with diabetes, with around 90,000 people with diabetes committing suicide each year. People with type 1 diabetes are three to four times more likely to attempt suicide, and those with newly diagnosed type 2 diabetes are twice as likely to attempt suicide compared to the general population. However, poor mental health comorbidity is still neglected, and more recommendations are needed to support for people with diabetes. It is widely acknowledged that the comorbidity of depression with diabetes is considered a higher risk factor for suicide attempts Previous studies have used logistic regression to identify risk factors for suicide attempts in individuals with diabetes. However, this technique can be prone to overfitting when the number of variables is high. To address this issue, we used the LASSO (Least Absolute Shrinkage and Selection Operator), a regularization technique, to reduce overfitting in a logistic regression model. It works by adding a penalty term ([lambda]) to the log-likelihood function, which shrinks the estimates of the coefficients. This process allows LASSO to act as a feature selection method, effectively setting coefficients that contribute most to the error to zero. Because few studies have focused on un derstanding the relationship between suicide attempts and diabetes, we used association rule mining ARM an explainable rule based machine learning technique, for knowledge discovery to reveal previously unknown relationships between suicide attempts and diabetes. This approach has already proved useful in the medical field, where it has been applied to electronic health record (EHR) data to discover associations such as disease co-occurrences, drug-disease associations, and symptomatic patterns of disease. However, no previous studies have used ARM to determine risk factors and predict suicide attempts in people with diabetes. The aim of this dissertation is to identify patterns of risk factors for suicide attempts in individuals with diabetes, with the long term goal of developing a clinical decision support system that can be integrated into EHRs. This system would allow healthcare providers to identify patients with diabetes at high risk of suicide attempts and provide appropriate preventive measures during outpatient clinic visits. To achieve this goal, we have three specific aims: (1) to identify potential risk factors for suicide attempts in individuals with diabetes through a literature review; (2) to investigate risk factors for suicide attempts in individuals with diabetes using LASSO regression; (3) to identify risk patterns for suicide attempts in individuals with diabetes using association rule mining. In this dissertation, we have reviewed the literature and compiled a list of data elements for suicide attempts in people with diabetes. We then retrieved data on patients with diabetes from Cerner Real-World Data [trade mark]. LASSO regression was used for feature selection, and ARM was used for investigating the risk patterns. We discovered risk patterns that are understandable and practical for healthcare providers. The findings of this research can inform suicide prevention efforts for people with diabetes and contribute to improved mental health outcomes.Includes bibliographical references

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation