Activity diagrams with location context: Experimental comparison of colour and icon annotations

In mobile information systems, the location of the user when performing tasks may be important to take into account during development. Yet, mainstream process models seldom capture this aspect. In previous papers we have evaluated analytically a number of small adaptations for showing location of actions in UML activity diagrams. Two of the most promising adaptations have also been evaluated experimentally. However, another notation alternative that got a quite positive score in the analytical evaluation has not been experimentally evaluated, namely a notation attaching symbolic icons to the activity nodes. This paper reports on an experiment comparing this notation to the most promising one from previous experiments (colour). The results showed no significant difference between the two notations, neither in the quality of answers to the experimental task, the time spent performing the task, nor in opinions about the notation alternatives, as investigated in a post-task questionnaire

Eliciting Security Requirements and Tracing them to Design: An Integration of Common Criteria, Heuristics, and UMLsec

Building secure systems is difficult for many reasons. This paper deals with two of the main challenges: (i) the lack of security expertise in development teams, and (ii) the inadequacy of existing methodologies to support developers who are not security experts. The security standard ISO 14508 (Common Criteria) together with secure design techniques such as UMLsec can provide the security expertise, knowledge, and guidelines that are needed. However, security expertise and guidelines are not stated explicitly in the Common Criteria. They are rather phrased in security domain terminology and difficult to understand for developers. This means that some general security and secure design expertise are required to fully take advantage of the Common Criteria and UMLsec. In addition, there is the problem of tracing security requirements and objectives into solution design,which is needed for proof of requirements fulfilment. This paper describes a security requirements engineering methodology called SecReq. SecReq combines three techniques: the Common Criteria, the heuristic requirements editorHeRA, andUMLsec. SecReqmakes systematic use of the security engineering knowledge contained in the Common Criteria and UMLsec, as well as security-related heuristics in the HeRA tool. The integrated SecReq method supports early detection of security-related issues (HeRA), their systematic refinement guided by the Common Criteria, and the ability to trace security requirements into UML design models. A feedback loop helps reusing experiencewithin SecReq and turns the approach into an iterative process for the secure system life-cycle, also in the presence of system evolution

Natural Labor Pain Management

There is a current trend toward natural pain management in labor, and pregnant women will seek the guidance of childbirth educators to make qualified decisions. The childbirth educator bases practice on the most current evidence; however, natural pain management in labor is not well studied. This paper offers information and current evidence as well as a story that illustrates the use of many natural or complementary and alternative medical therapies used in pain management during labor

Democratizing machine learning

Modelle des maschinellen Lernens sind zunehmend in der Gesellschaft verankert, oft in Form von automatisierten Entscheidungsprozessen. Ein wesentlicher Grund dafür ist die verbesserte Zugänglichkeit von Daten, aber auch von Toolkits für maschinelles Lernen, die den Zugang zu Methoden des maschinellen Lernens für Nicht-Experten ermöglichen. Diese Arbeit umfasst mehrere Beiträge zur Demokratisierung des Zugangs zum maschinellem Lernen, mit dem Ziel, einem breiterem Publikum Zugang zu diesen Technologien zu er- möglichen. Die Beiträge in diesem Manuskript stammen aus mehreren Bereichen innerhalb dieses weiten Gebiets. Ein großer Teil ist dem Bereich des automatisierten maschinellen Lernens (AutoML) und der Hyperparameter-Optimierung gewidmet, mit dem Ziel, die oft mühsame Aufgabe, ein optimales Vorhersagemodell für einen gegebenen Datensatz zu finden, zu vereinfachen. Dieser Prozess besteht meist darin ein für vom Benutzer vorgegebene Leistungsmetrik(en) optimales Modell zu finden. Oft kann dieser Prozess durch Lernen aus vorhergehenden Experimenten verbessert oder beschleunigt werden. In dieser Arbeit werden drei solcher Methoden vorgestellt, die entweder darauf abzielen, eine feste Menge möglicher Hyperparameterkonfigurationen zu erhalten, die wahrscheinlich gute Lösungen für jeden neuen Datensatz enthalten, oder Eigenschaften der Datensätze zu nutzen, um neue Konfigurationen vorzuschlagen. Darüber hinaus wird eine Sammlung solcher erforderlichen Metadaten zu den Experimenten vorgestellt, und es wird gezeigt, wie solche Metadaten für die Entwicklung und als Testumgebung für neue Hyperparameter- Optimierungsmethoden verwendet werden können. Die weite Verbreitung von ML-Modellen in vielen Bereichen der Gesellschaft erfordert gleichzeitig eine genauere Untersuchung der Art und Weise, wie aus Modellen abgeleitete automatisierte Entscheidungen die Gesellschaft formen, und ob sie möglicherweise Individuen oder einzelne Bevölkerungsgruppen benachteiligen. In dieser Arbeit wird daher ein AutoML-Tool vorgestellt, das es ermöglicht, solche Überlegungen in die Suche nach einem optimalen Modell miteinzubeziehen. Diese Forderung nach Fairness wirft gleichzeitig die Frage auf, ob die Fairness eines Modells zuverlässig geschätzt werden kann, was in einem weiteren Beitrag in dieser Arbeit untersucht wird. Da der Zugang zu Methoden des maschinellen Lernens auch stark vom Zugang zu Software und Toolboxen abhängt, sind mehrere Beiträge in Form von Software Teil dieser Arbeit. Das R-Paket mlr3pipelines ermöglicht die Einbettung von Modellen in sogenan- nte Machine Learning Pipelines, die Vor- und Nachverarbeitungsschritte enthalten, die im maschinellen Lernen und AutoML häufig benötigt werden. Das mlr3fairness R-Paket hingegen ermöglicht es dem Benutzer, Modelle auf potentielle Benachteiligung hin zu über- prüfen und diese durch verschiedene Techniken zu reduzieren. Eine dieser Techniken, multi-calibration wurde darüberhinaus als seperate Software veröffentlicht.Machine learning artifacts are increasingly embedded in society, often in the form of automated decision-making processes. One major reason for this, along with methodological improvements, is the increasing accessibility of data but also machine learning toolkits that enable access to machine learning methodology for non-experts. The core focus of this thesis is exactly this – democratizing access to machine learning in order to enable a wider audience to benefit from its potential. Contributions in this manuscript stem from several different areas within this broader area. A major section is dedicated to the field of automated machine learning (AutoML) with the goal to abstract away the tedious task of obtaining an optimal predictive model for a given dataset. This process mostly consists of finding said optimal model, often through hyperparameter optimization, while the user in turn only selects the appropriate performance metric(s) and validates the resulting models. This process can be improved or sped up by learning from previous experiments. Three such methods one with the goal to obtain a fixed set of possible hyperparameter configurations that likely contain good solutions for any new dataset and two using dataset characteristics to propose new configurations are presented in this thesis. It furthermore presents a collection of required experiment metadata and how such meta-data can be used for the development and as a test bed for new hyperparameter optimization methods. The pervasion of models derived from ML in many aspects of society simultaneously calls for increased scrutiny with respect to how such models shape society and the eventual biases they exhibit. Therefore, this thesis presents an AutoML tool that allows incorporating fairness considerations into the search for an optimal model. This requirement for fairness simultaneously poses the question of whether we can reliably estimate a model’s fairness, which is studied in a further contribution in this thesis. Since access to machine learning methods also heavily depends on access to software and toolboxes, several contributions in the form of software are part of this thesis. The mlr3pipelines R package allows for embedding models in so-called machine learning pipelines that include pre- and postprocessing steps often required in machine learning and AutoML. The mlr3fairness R package on the other hand enables users to audit models for potential biases as well as reduce those biases through different debiasing techniques. One such technique, multi-calibration is published as a separate software package, mcboost

Exploring Racial Disparity in St. Louis City Fetal-Infant Death

The perinatal periods of risk (PPOR) methodology was used to analyze resident fetal and infant deaths in St. Louis City, Missouri, for the years 1999 - 2008. The PPOR approach is mapped into four periods: Maternal Health/Prematurity (MHP), Maternal Care (MC), Newborn Care (NC), and Infant Health (IF). Both Blacks and Whites experienced excess fetal-infant death within the MHP periods. Recognizing specific periods of increased risk provides key information to transform data into action. Findings allow childbirth educators, community members, and policy-makers to further explore barriers limiting maternal care

The Role of Ultrasound in the Lebanese Outreach Setting

A cross-sectional study was carried out on 669 patients to assess the role of introducing ultrasound into obstetrical outreach in Lebanon. Data were collected, and descriptive statistics were performed. Sonographic findings were compared using Chi-square tests between underserved Lebanese and Syrian refugee mothers. Ultrasound plays a significant role in properly dating pregnancies in addition to identifying at-risk fetuses and detecting placental abnormalities. Medical providers need to make sonographic evaluation in the Lebanese outreach obstetrical setting more available and more systematic in order to secure a safe outcome for underserved Lebanese and Syrian refugee mothers and offspring

Mindfulness: Being Present in the Moment

This article serves to enlighten childbirth educators’ knowledge about mindfulness and the mother-baby benefits associated with incorporating mindfulness- based interventions into practice. Jon Kabat-Zinn, who developed the Mindfulness Based Stress Reduction program, brought the concept of mindfulness into the world of healthcare and mainstream society. Mindfulness is the practice of bringing awareness to the here and now using a variety of methods. Nancy Bardacke has taken the practice of mindfulness further and developed a program for expecting mothers, known as Mindfulness Based Childbirth and Parenting. This program has been shown to reduce stress responses that may be harmful to a pregnant woman’s well-being and that of her unborn child. Maternal stress is linked to preterm birth, low birth weight, miscarriages, lower Apgar scores, smaller infant head circumference, and postpartum depression. Integrating mindfulness-based interventions throughout pregnancy can help manage pain, reduce stress, anxiety, the risk of developing postpartum depression, and increase a woman’s overall mood

A Review of Fatherhood Related Issues in the Country of Lebanon

Fatherhood issues in the country of Lebanon remain largely unexplored and undocumented. This review serves as a basis for fatherhood issues and presents a snapshot of the current situation with a background of some of the most related challenges affecting the issue of parenting in Lebanon. In addition, this review lays the background of how these challenges affect women of childbearing age who often end up raising their families on their own. Cultural and religious beliefs as well as factors relating to political influences in the Middle East region are discussed. The author concludes with a set of lessons learned

The Role of Ultrasound in the Lebanese Outreach Setting

A cross-sectional study was carried out on 669 patients to assess the role of introducing ultrasound into obstetrical outreach in Lebanon. Data were collected, and descriptive statistics were performed. Sonographic findings were compared using Chi-square tests between underserved Lebanese and Syrian refugee mothers. Ultrasound plays a significant role in properly dating pregnancies in addition to identifying at-risk fetuses and detecting placental abnormalities. Medical providers need to make sonographic evaluation in the Lebanese outreach obstetrical setting more available and more systematic in order to secure a safe outcome for underserved Lebanese and Syrian refugee mothers and offspring