004 Datenverarbeitung; Informatik
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As a result of the Bologna reform of educational systems in
Europe the outcome orientation of learning processes, competence-oriented
descriptions of the curricula and competence-oriented assessment
procedures became standard also in Computer Science Education
(CSE). The following keynote addresses important issues of shaping
a CSE competence model especially in the area of informatics system
comprehension and object-oriented modelling. Objectives and research
methodology of the project MoKoM (Modelling and Measurement
of Competences in CSE) are explained. Firstly, the CSE competence
model was derived based on theoretical concepts and then secondly the
model was empirically examined and refined using expert interviews.
Furthermore, the paper depicts the development and examination of
a competence measurement instrument, which was derived from the
competence model. Therefore, the instrument was applied to a large
sample of students at the gymnasium’s upper class level. Subsequently,
efforts to develop a competence level model, based on the retrieved empirical
results and on expert ratings are presented. Finally, further demands
on research on competence modelling in CSE will be outlined.
Regardless of what is intended by government curriculum
specifications and advised by educational experts, the competencies
taught and learned in and out of classrooms can vary considerably.
In this paper, we discuss in particular how we can investigate the
perceptions that individual teachers have of competencies in ICT,
and how these and other factors may influence students’ learning. We
report case study research which identifies contradictions within the
teaching of ICT competencies as an activity system, highlighting issues
concerning the object of the curriculum, the roles of the participants and
the school cultures. In a particular case, contradictions in the learning
objectives between higher order skills and the use of application tools
have been resolved by a change in the teacher’s perceptions which
have not led to changes in other aspects of the activity system. We look
forward to further investigation of the effects of these contradictions in
other case studies and on forthcoming curriculum change.
The paper presents two approaches to the development of
a Computer Science Competence Model for the needs of curriculum
development and evaluation in Higher Education. A normativetheoretical
approach is based on the AKT and ACM/IEEE curriculum
and will be used within the recommendations of the German
Informatics Society (GI) for the design of CS curricula. An empirically
oriented approach refines the categories of the first one with regard to
specific subject areas by conducting content analysis on CS curricula of
important universities from several countries. The refined model will be
used for the needs of students’ e-assessment and subsequent affirmative
action of the CS departments.
The paper discusses the issue of supporting informatics
(computer science) education through competitions for lower and
upper secondary school students (8–19 years old). Competitions play
an important role for learners as a source of inspiration, innovation,
and attraction. Running contests in informatics for school students
for many years, we have noticed that the students consider the contest
experience very engaging and exciting as well as a learning experience.
A contest is an excellent instrument to involve students in problem
solving activities. An overview of infrastructure and development
of an informatics contest from international level to the national one
(the Bebras contest on informatics and computer fluency, originated
in Lithuania) is presented. The performance of Bebras contests in 23
countries during the last 10 years showed an unexpected and unusually
high acceptance by school students and teachers. Many thousands of
students participated and got a valuable input in addition to their regular
informatics lectures at school. In the paper, the main attention is paid
to the developed tasks and analysis of students’ task solving results in
Lithuania.
Social networks are currently at the forefront of tools that
lend to Personal Learning Environments (PLEs). This study aimed to
observe how students perceived PLEs, what they believed were the
integral components of social presence when using Facebook as part
of a PLE, and to describe student’s preferences for types of interactions
when using Facebook as part of their PLE. This study used mixed
methods to analyze the perceptions of graduate and undergraduate
students on the use of social networks, more specifically Facebook as a
learning tool. Fifty surveys were returned representing a 65 % response
rate. Survey questions included both closed and open-ended questions.
Findings suggested that even though students rated themselves relatively
well in having requisite technology skills, and 94 % of students used
Facebook primarily for social use, they were hesitant to migrate these
skills to academic use because of concerns of privacy, believing that
other platforms could fulfil the same purpose, and by not seeing the
validity to use Facebook in establishing social presence. What lies
at odds with these beliefs is that when asked to identify strategies in
Facebook that enabled social presence to occur in academic work, the
majority of students identified strategies in five categories that lead to
social presence establishment on Facebook during their coursework.
Teaching Data Management
(2015)
Data management is a central topic in computer science as
well as in computer science education. Within the last years, this topic is
changing tremendously, as its impact on daily life becomes increasingly
visible. Nowadays, everyone not only needs to manage data of various
kinds, but also continuously generates large amounts of data. In
addition, Big Data and data analysis are intensively discussed in public
dialogue because of their influences on society. For the understanding of
such discussions and for being able to participate in them, fundamental
knowledge on data management is necessary. Especially, being aware
of the threats accompanying the ability to analyze large amounts of
data in nearly real-time becomes increasingly important. This raises the
question, which key competencies are necessary for daily dealings with
data and data management.
In this paper, we will first point out the importance of data management
and of Big Data in daily life. On this basis, we will analyze which are
the key competencies everyone needs concerning data management to
be able to handle data in a proper way in daily life. Afterwards, we will
discuss the impact of these changes in data management on computer
science education and in particular database education.
The Student Learning Ecology
(2015)
Educational research on social media has showed that
students use it for socialisation, personal communication, and informal
learning. Recent studies have argued that students to some degree use
social media to carry out formal schoolwork. This article gives an
explorative account on how a small sample of Norwegian high school
students use social media to self-organise formal schoolwork. This
user pattern can be called a “student learning ecology”, which is a
user perspective on how participating students gain access to learning
resources.
This paper discusses results from a small-scale research
study, together with some recently published research into student
perceptions of ICT for learning in schools, to consider relevant skills
that do not appear to currently being taught. The paper concludes by
raising three issues relating to learning with and through ICT that need
to be addressed in school curricula and classroom teaching.
How Things Work
(2015)
Recognizing and defining functionality is a key competence
adopted in all kinds of programming projects. This study investigates
how far students without specific informatics training are able to identify
and verbalize functions and parameters. It presents observations
from classroom activities on functional modeling in high school chemistry
lessons with altogether 154 students. Finally it discusses the potential
of functional modelling to improve the comprehension of scientific
content.
Computational Thinking
(2015)
Digital technology has radically changed the way people
work in industry, finance, services, media and commerce. Informatics
has contributed to the scientific and technological development of our
society in general and to the digital revolution in particular. Computational
thinking is the term indicating the key ideas of this discipline that
might be included in the key competencies underlying the curriculum
of compulsory education. The educational potential of informatics has
a history dating back to the sixties. In this article, we briefly revisit this
history looking for lessons learned. In particular, we focus on experiences
of teaching and learning programming. However, computational
thinking is more than coding. It is a way of thinking and practicing interactive
dynamic modeling with computers. We advocate that learners
can practice computational thinking in playful contexts where they can
develop personal projects, for example building videogames and/or robots,
share and discuss their construction with others. In our view, this
approach allows an integration of computational thinking in the K-12
curriculum across disciplines.
The Technology Proficiency Self-Assessment (TPSA) questionnaire
has been used for 15 years in the USA and other nations as a
self-efficacy measure for proficiencies fundamental to effective technology
integration in the classroom learning environment. Internal consistency
reliabilities for each of the five-item scales have typically ranged
from .73 to .88 for preservice or inservice technology-using teachers.
Due to changing technologies used in education, researchers sought to
renovate partially obsolete items and extend self-efficacy assessment to
new areas, such as social media and mobile learning. Analysis of 2014
data gathered on a new, 34 item version of the TPSA indicates that the
four established areas of email, World Wide Web (WWW), integrated
applications, and teaching with technology continue to form consistent
scales with reliabilities ranging from .81 to .93, while the 14 new items
gathered to represent emerging technologies and media separate into
two scales, each with internal consistency reliabilities greater than .9.
The renovated TPSA is deemed to be worthy of continued use in the
teaching with technology context.
How does the Implementation of a Literacy Learning Tool Kit influence Literacy Skill Acquisition?
(2015)
This study aimed at following how teachers transfer skills
into results while using ABRA literacy software. This was done in
the second part of the pilot study whose aim was to provide equity to
control group teachers and students by exposing them to the ABRACADABRA
treatment after the end of phase 1. This opportunity was
used to follow the phase 1 teachers to see how the skills learned were
being transformed into results. A standard three-day initial training and
planning session on how to use ABRA to teach literacy was held at the
beginning of each phase for ABRA teachers (phase 1 experimental and
phase 2 delayed ABRA). Teachers were provided with teaching materials
including a tentative ABRA curriculum developed to align with the
Kenyan English Language requirements for year 1 and 3 students. Results
showed that although there was no significant difference between
the groups in vocabulary-related subscales which include word reading
and meaning as well as sentence comprehension, students in ABRACADABRA
classes improved their scores at a significantly higher rate
than students in control classes in comprehension related scores. An
average student in the ABRACADABRA group improved by 12 and
16 percentile points respectively compared to their counterparts in the
control group.
In this paper we describe the recent state of our research
project concerning computer science teachers’ knowledge on students’
cognition. We did a comprehensive analysis of textbooks, curricula
and other resources, which give teachers guidance to formulate assignments.
In comparison to other subjects there are only a few concepts
and strategies taught to prospective computer science teachers in university.
We summarize them and given an overview on our empirical
approach to measure this knowledge.
The study reported in this paper involved the employment
of specific in-class exercises using a Personal Response System (PRS).
These exercises were designed with two goals: to enhance students’
capabilities of tracing a given code and of explaining a given code in
natural language with some abstraction. The paper presents evidence
from the actual use of the PRS along with students’ subjective impressions
regarding both the use of the PRS and the special exercises. The
conclusions from the findings are followed with a short discussion on
benefits of PRS-based mental processing exercises for learning programming
and beyond.
Mentoring in a Digital World
(2015)
This paper focuses on the results of the evaluation of the first
pilot of an e-mentoring unit designed by the Hands-On ICT consortium,
funded by the EU LLL programme. The overall aim of this two-year
activity is to investigate the value for professional learning of Massive
Online Open Courses (MOOCs) and Community Online Open Courses
(COOCs) in the context of a ‘community of practice’. Three units in the
first pilot covered aspects of using digital technologies to develop creative
thinking skills. The findings in this paper relate to the fourth unit
about e-mentoring, a skill that was important to delivering the course
content in the other three units. Findings about the e-mentoring unit
included: the students’ request for detailed profiles so that participants
can get to know each other; and, the need to reconcile the different
interpretations of e-mentoring held by the participants when the course
begins. The evaluators concluded that the major issues were that: not all
professional learners would self-organise and network; and few would
wish to mentor their colleagues voluntarily. Therefore, the e-mentoring
issues will need careful consideration in pilots two and three to identify
how e-mentoring will be organised.
Physical computing covers the design and realization of interactive
objects and installations and allows students to develop concrete,
tangible products of the real world that arise from the learners’
imagination. This way, constructionist learning is raised to a level that
enables students to gain haptic experience and thereby concretizes the
virtual. In this paper the defining characteristics of physical computing
are described. Key competences to be gained with physical computing
will be identified.
A project involving the composition of a number of pieces
of music by public participants revealed levels of engagement with and
mastery of complex music technologies by a number of secondary student
volunteers. This paper reports briefly on some initial findings of
that project and seeks to illuminate an understanding of computational
thinking across the curriculum.
Think logarithmically!
(2015)
We discuss here a number of algorithmic topics which we
use in our teaching and in learning of mathematics and informatics to
illustrate and document the power of logarithm in designing very efficient
algorithms and computations – logarithmic thinking is one of the
most important key competencies for solving real world practical problems.
We demonstrate also how to introduce logarithm independently
of mathematical formalism using a conceptual model for reducing a
problem size by at least half. It is quite surprising that the idea, which
leads to logarithm, is present in Euclid’s algorithm described almost
2000 years before John Napier invented logarithm.
Current curricular trends require teachers in Baden-
Wuerttemberg (Germany) to integrate Computer Science (CS) into
traditional subjects, such as Physical Science. However, concrete guidelines
are missing. To fill this gap, we outline an approach where a
microcontroller is used to perform and evaluate measurements in the
Physical Science classroom.
Using the open-source Arduino platform, we expect students to acquire
and develop both CS and Physical Science competencies by using a
self-programmed microcontroller. In addition to this combined development
of competencies in Physical Science and CS, the subject matter
will be embedded in suitable contexts and learning environments,
such as weather and climate.
The poster and abstract describe the importance of teaching
information security in school. After a short description of information
security and important aspects, I will show, how information security
fits into different guidelines or models for computer science educations
and that it is therefore on of the key competencies. Afterwards I will
present you a rough insight of teaching information security in Austria.
ProtoSense
(2015)
Let’s talk about CS!
(2015)
To communicate about a science is the most important key
competence in education for any science. Without communication we
cannot teach, so teachers should reflect about the language they use in
class properly. But the language students and teachers use to communicate
about their CS courses is very heterogeneous, inconsistent and
deeply influenced by tool names. There is a big lack of research and
discussion in CS education regarding the terminology and the role of
concepts and tools in our science. We don’t have a consistent set of
terminology that we agree on to be helpful for learning our science.
This makes it nearly impossible to do research on CS competencies as
long as we have not agreed on the names we use to describe these. This
workshop intends to provide room to fill with discussion and first ideas
for future research in this field.
Participants of this workshop will be confronted exemplarily
with a considerable inconsistency of global Informatics education at
lower secondary level. More importantly, they are invited to contribute
actively on this issue in form of short case studies of their countries.
Until now, very few countries have been successful in implementing
Informatics or Computing at primary and lower secondary level. The
spectrum from digital literacy to informatics, particularly as a discipline
in its own right, has not really achieved a breakthrough and seems to
be underrepresented for these age groups. The goal of this workshop
is not only to discuss the anamnesis and diagnosis of this fragmented
field, but also to discuss and suggest viable forms of therapy in form of
setting educational standards. Making visible good practices in some
countries and comparing successful approaches are rewarding tasks for
this workshop.
Discussing and defining common educational standards on a transcontinental
level for the age group of 14 to 15 years old students in a readable,
assessable and acceptable form should keep the participants of this
workshop active beyond the limited time at the workshop.
A lot has been published about the competencies needed by
students in the 21st century (Ravenscroft et al., 2012). However, equally
important are the competencies needed by educators in the new era
of digital education. We review the key competencies for educators in
light of the new methods of teaching and learning proposed by Massive
Open Online Courses (MOOCs) and their on-campus counterparts,
Small Private Online Courses (SPOCs).
Die Wahl des richtigen Studienfaches und die daran anschließende
Studieneingangsphase sind oft entscheidend für den erfolgreichen Verlauf eines Studiums. Eine große Herausforderung besteht dabei darin, bereits in den ersten Wochen des Studiums bestehende Defizite in vermeintlich einfachen Schlüsselkompetenzen zu erkennen und diese so bald wie möglich zu beheben. Eine zweite, nicht minder wichtige Herausforderung ist es, möglichst frühzeitig für jeden einzelnen Studierenden zu erkennen, ob er bzw. sie das individuell richtige Studienfach gewählt hat, das den jeweiligen persönlichen Neigungen, Interessen und Fähigkeiten entspricht und zur Verwirklichung der eigenen Lebensziele beiträgt. Denn nur dann sind Studierende ausreichend stark und dauerhaft intrinsisch motiviert, um ein anspruchsvolles, komplexes Studium erfolgreich durchzuziehen. In diesem Beitrag fokussieren wir eine Maßnahme, die die Studierenden an einen Prozess zur systematischen Reflexion des eigenen Lernprozesses und der eigenen Ziele heranführt und beides in Relation setzt.
Ziel einer neuen Studieneingangsphase ist, den Studierenden bis zum Ende des ersten Semesters ein vielfältiges Berufsbild der Informatik und Wirtschaftsinformatik mit dem breiten Aufgabenspektrum aufzublättern und damit die Zusammenhänge zwischen den einzelnen Modulen des Curriculums zu verdeutlichen. Die Studierenden sollen in die Lage versetzt werden, sehr eigenständig die Planung und Gestaltung ihres Studiums in die Hand zu nehmen.
In diesem Papier wird das Konzept eines Lernzentrums für die Informatik (LZI) an der Universität Paderborn vorgestellt. Ausgehend von den fachspezifischen Schwierigkeiten der Informatik Studierenden werden die Angebote des LZIs erläutert, die sich über die vier Bereiche Individuelle Beratung und Betreuung, „Offener Lernraum“, Workshops und Lehrveranstaltungen sowie Forschung erstrecken. Eine erste Evaluation mittels Feedbackbögen zeigt, dass das Angebot bei den Studierenden positiv aufgenommen wird. Zukünftig soll das Angebot des LZIs weiter ausgebaut und verbessert werden. Ausgangsbasis dazu sind weitere Studien.
Die Arbeitswelt im Fokus
(2015)
Für Bachelor-Studierende der Wirtschaftsinformatik im zweiten Semester an der Fachhochschule Kiel werden im Modul Informationsmanagement neben klassischen didaktischen Ansätzen in einer seminaristischen Unterrichtsform so genannte „Aktivbausteine“ eingesetzt: Studierende erhalten zum einen die Gelegenheit, sich im Kontakt mit Fach- und Führungskräften aus der Industrie ein konkretes Bild vom Beruf der Wirtschaftsinformatikerin bzw. des Wirtschaftsinformatikers zu machen; zum anderen erarbeiten sie innovative Ansätze der Prozessverbesserung aus Sicht der IT oder mit Nutzenpotenzial für die IT und präsentieren ihre Ergebnisse öffentlich im Rahmen des Kieler Prozessmanagementforums. Diese Aktivbausteine dienen insbesondere der Berufsfeldorientierung: Durch die Informationen, die die Studierenden über die Anforderungen und Tätigkeiten von im Beruf stehenden Menschen erhalten, werden sie in die Lage versetzt, fundierte Entscheidungen bzgl. ihrer Studiengestaltung und Berufswahl zu treffen. Im Beitrag wird die Konzeption der Bausteine vorgestellt und deren Grad der Zielerreichung durch aktuelle Evaluationsergebnisse erläutert. Zudem wird die motivationale Wirkung der Aktivbausteine anhand der Theorie der Selbstbestimmung von Deci und Ryan [DR1985, DR1993, DR2004] erläutert.
Dieser Beitrag diskutiert die Konzeption eines Software-Projektpraktikums im Bereich E-Learning, welches Lehramts- und Fachstudierenden der Informatik ermöglicht, voneinander zu profitieren und praxisrelevante Ergebnisse generiert. Vorbereitungen, Organisation und Durchführung werden vorgestellt und diskutiert. Den Abschluss bildet ein Ausblick auf die Fortführung des Konzepts und den Ausbau des Forschungsgebietes.
Das „Startprojekt“
(2016)
Absolventinnen und Absolventen unserer Informatik-Bachelorstudiengänge benötigen für kompetentes berufliches Handeln sowohl fachliche als auch überfachliche Kompetenzen. Vielfach verlangen wir von Erstsemestern in Grundlagen-Lehrveranstaltungen fast ausschließlich den Aufbau von Fachkompetenz und vernachlässigen dabei häufig Selbstkompetenz, Methodenkompetenz und Sozialkompetenz. Gerade die drei letztgenannten sind für ein erfolgreiches Studium unabdingbar und sollten von Anfang an entwickelt werden. Wir stellen unser „Startprojekt“ als einen Beitrag vor, im ersten Semester die eigenverantwortliche, überfachliche Kompetenzentwicklung in einem fachlichen Kontext zu fördern.
Aus einer Vergleichsstudie mit starken und schwachen Problemlösern konnten Erkenntnisse über die effizienten Herangehensweisen von Hochleistern an Informatikprobleme gewonnen werden. Diese Erkenntnisse wurden in einem Lehrvideo zum informatischen Problemlösen didaktisch aufgearbeitet, sodass Lernenden der Einsatz von Baumstrukturen und Rekursion im konkreten Kontext gezeigt werden kann. Nun wurde die tatsächliche Lernwirksamkeit des Videos sowie die Definition der Zielgruppe in einer Vergleichsstudie mit 66 Studienanfängern überprüft.
Der Unterricht großer Studierendengruppen im wissenschaftlichen Schreiben birgt vielfältige organisatorische Herausforderungen und eine zeitintensive Betreuung durch die Dozenten. Diese Arbeit stellt ein Lehrkonzept mit Peer-Reviews vor, in dem das Feedback der Peers durch eine automatisierte Analyse ergänzt wird. Die Software Confopy liefert metrik- und strukturbasierte Hinweise für die Verbesserung des wissenschaftlichen Schreibstils. Der Nutzen von Confopy wird an 47 studentischen Arbeiten in Draft- und Final-Version illustriert.
Informatik-Studierende haben in der Mehrzahl Schwierigkeiten, einen Einstieg in die Theoretische
Informatik zu finden und die Leistungsanforderungen in den
Endklausuren der zugehörigen Lehrveranstaltungen zu erfüllen. Wir argumentieren, dass dieser Symptomatik mangelnde Kompetenzen im Umgang mit abstrakten und stark formalisierten Themeninhalten zugrunde liegen und schlagen vor, einen Beweisassistenten als interaktives Lernwerkzeug in der Eingangslehre der Theoretischen Informatik zu nutzen, um entsprechende Kompetenzen zu stärken.
Dieser Beitrag diskutiert den Einsatz von interaktiven und automatischen Theorembeweisern in der universitären Lehre. Moderne Theorembeweiser scheinen geeignet zur Implementierung des dialogischen Lernens und als E-Assessment-Werkzeug in der Logikausbilding. Exemplarisch skizzieren wir ein innovaties Lehrprojekt zum Thema „Komputationale Metaphysik“, in dem die zuvor genannten Werkzeuge eingesetzt werden.
Die Unterrichtsmethode Stationsarbeit kann verwendet werden, um Individualisierung und Differenzierung im Lernprozess zu ermöglichen. Dieser Beitrag schlägt Aufgabenformate vor, die in einer Stationsarbeit über das Klassendiagramm aus der Unified Modeling Language verwendet werden können. Die Aufgabenformate wurden bereits mit Studierenden erprobt.
3D point clouds are a digital representation of our world and used in a variety of applications. They are captured with LiDAR or derived by image-matching approaches to get surface information of objects, e.g., indoor scenes, buildings, infrastructures, cities, and landscapes. We present novel interaction and visualization techniques for heterogeneous, time variant, and semantically rich 3D point clouds. Interactive and view-dependent see-through lenses are introduced as exploration tools to enhance recognition of objects, semantics, and temporal changes within 3D point cloud depictions. We also develop filtering and highlighting techniques that are used to dissolve occlusion to give context-specific insights. All techniques can be combined with an out-of-core real-time rendering system for massive 3D point clouds. We have evaluated the presented approach with 3D point clouds from different application domains. The results show the usability and how different visualization and exploration tasks can be improved for a variety of domain-specific applications.
Solving problems combining task and motion planning requires searching across a symbolic search space and a geometric search space. Because of the semantic gap between symbolic and geometric representations, symbolic sequences of actions are not guaranteed to be geometrically feasible. This compels us to search in the combined search space, in which frequent backtracks between symbolic and geometric levels make the search inefficient.We address this problem by guiding symbolic search with rich information extracted from the geometric level through culprit detection mechanisms.
We contribute to the theoretical understanding of randomized search heuristics by investigating their optimization behavior on satisfiable random k-satisfiability instances both in the planted solution model and the uniform model conditional on satisfiability. Denoting the number of variables by n, our main technical result is that the simple () evolutionary algorithm with high probability finds a satisfying assignment in time when the clause-variable density is at least logarithmic. For low density instances, evolutionary algorithms seem to be less effective, and all we can show is a subexponential upper bound on the runtime for densities below . We complement these mathematical results with numerical experiments on a broader density spectrum. They indicate that, indeed, the () EA is less efficient on lower densities. Our experiments also suggest that the implicit constants hidden in our main runtime guarantee are low. Our main result extends and considerably improves the result obtained by Sutton and Neumann (Lect Notes Comput Sci 8672:942-951, 2014) in terms of runtime, minimum density, and clause length. These improvements are made possible by establishing a close fitness-distance correlation in certain parts of the search space. This approach might be of independent interest and could be useful for other average-case analyses of randomized search heuristics. While the notion of a fitness-distance correlation has been around for a long time, to the best of our knowledge, this is the first time that fitness-distance correlation is explicitly used to rigorously prove a performance statement for an evolutionary algorithm.
Die Vermittlung von Modellierungsfähigkeiten in der Softwaretechnik-Ausbildung konzentriert sich meist auf Modellierungskonzepte, Notationen und Entwicklungswerkzeuge. Die Betrachtung der Modellierungsaktivitäten, etwa die Entwicklung und Gegenüberstellung alternativer Modellvorschläge, steht weniger im Vordergrund. Die vorliegende Studie untersucht zwei Formen des kollaborativen Modellierens am Tabletop in Bezug auf ihren Einfluss auf die Modellierungsaktivitäten in kleinen Gruppen. Die Ergebnisse zeigen, dass sowohl selbstorganisierte als auch moderierte Modellierungssitzungen das Entwickeln eines gemeinsamen Modellverständnisses fördern. In moderierten Sitzungen wurden zudem mehr alternative Lösungsideen entwickelt und in stärkerem Maße diskutiert.
Recombination of free charge is a key process limiting the performance of solar cells. For low mobility materials, such as organic semiconductors, the kinetics of non-geminate recombination (NGR) is strongly linked to the motion of charges. As these materials possess significant disorder, thermalization of photogenerated carriers in the inhomogeneously broadened density of state distribution is an unavoidable process. Despite its general importance, knowledge about the kinetics of NGR in complete organic solar cells is rather limited. We employ time delayed collection field (TDCF) experiments to study the recombination of photogenerated charge in the high-performance polymer:fullerene blend PCDTBT:PCBM. NGR in the bulk of this amorphous blend is shown to be highly dispersive, with a continuous reduction of the recombination coefficient throughout the entire time scale, until all charge carriers have either been extracted or recombined. Rapid, contact-mediated recombination is identified as an additional loss channel, which, if not properly taken into account, would erroneously suggest a pronounced field dependence of charge generation. These findings are in stark contrast to the results of TDCF experiments on photovoltaic devices made from ordered blends, such as P3HT:PCBM, where non-dispersive recombination was proven to dominate the charge carrier dynamics under application relevant conditions.
Compared to their inorganic counterparts, organic semiconductors suffer from relatively low charge carrier mobilities. Therefore, expressions derived for inorganic solar cells to correlate characteristic performance parameters to material properties are prone to fail when applied to organic devices. This is especially true for the classical Shockley-equation commonly used to describe current-voltage (JV)-curves, as it assumes a high electrical conductivity of the charge transporting material. Here, an analytical expression for the JV-curves of organic solar cells is derived based on a previously published analytical model. This expression, bearing a similar functional dependence as the Shockley-equation, delivers a new figure of merit α to express the balance between free charge recombination and extraction in low mobility photoactive materials. This figure of merit is shown to determine critical device parameters such as the apparent series resistance and the fill factor.
In this paper, using an algorithm based on the retrospective rejection sampling scheme introduced in [A. Beskos, O. Papaspiliopoulos, and G. O. Roberts,Methodol. Comput. Appl. Probab., 10 (2008), pp. 85-104] and [P. Etore and M. Martinez, ESAIM Probab.Stat., 18 (2014), pp. 686-702], we propose an exact simulation of a Brownian di ff usion whose drift admits several jumps. We treat explicitly and extensively the case of two jumps, providing numerical simulations. Our main contribution is to manage the technical di ffi culty due to the presence of t w o jumps thanks to a new explicit expression of the transition density of the skew Brownian motion with two semipermeable barriers and a constant drift.
Creation, collection and retention of knowledge in digital communities is an activity that currently requires being explicitly targeted as a secure method of keeping intellectual capital growing in the digital era. In particular, we consider it relevant to analyze and evaluate the empathetic cognitive personalities and behaviors that individuals now have with the change from face-to-face communication (F2F) to computer-mediated communication (CMC) online. This document proposes a cyber-humanistic approach to enhance the traditional SECI knowledge management model. A cognitive perception is added to its cyclical process following design thinking interaction, exemplary for improvement of the method in which knowledge is continuously created, converted and shared. In building a cognitive-centered model, we specifically focus on the effective identification and response to cognitive stimulation of individuals, as they are the intellectual generators and multiplicators of knowledge in the online environment. Our target is to identify how geographically distributed-digital-organizations should align the individual's cognitive abilities to promote iteration and improve interaction as a reliable stimulant of collective intelligence. The new model focuses on analyzing the four different stages of knowledge processing, where individuals with sympathetic cognitive personalities can significantly boost knowledge creation in a virtual social system. For organizations, this means that multidisciplinary individuals can maximize their extensive potential, by externalizing their knowledge in the correct stage of the knowledge creation process, and by collaborating with their appropriate sympathetically cognitive remote peers.
Applications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk and resources of the data center without affecting the service quality of applications.
Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)]
The Potsdam answer set solving collection, or Potassco for short, bundles various tools implementing and/or applying answer set programming. The article at hand succeeds an earlier description of the Potassco project published in Gebser et al. (AI Commun 24(2):107-124, 2011). Hence, we concentrate in what follows on the major features of the most recent, fifth generation of the ASP system clingo and highlight some recent resulting application systems.
The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.
We introduce a type and effect system, for an imperative object calculus, which infers sharing possibly introduced by the evaluation of an expression, represented as an equivalence relation among its free variables. This direct representation of sharing effects at the syntactic level allows us to express in a natural way, and to generalize, widely-used notions in literature, notably uniqueness and borrowing. Moreover, the calculus is pure in the sense that reduction is defined on language terms only, since they directly encode store. The advantage of this non-standard execution model with respect to a behaviorally equivalent standard model using a global auxiliary structure is that reachability relations among references are partly encoded by scoping. (C) 2018 Elsevier B.V. All rights reserved.
Die Veröffentlichung eines global frei verfügbaren Onlinekurses abseits der großen MOOC Plattformen bringt spezielle Herausforderungen mit sich. Neben technischen Herausforderungen sind eine effiziente Wissensvermittlung und die Erhaltung der Lernmotivation zentral. Der folgende Beitrag stellt Techniken zur Steigerung der Lerneffizienz und -motivation anhand des ARCS Modells vor. Er zeigt auf, wie die verschiedenen Techniken in der Entwicklung des Onlinekurses „Designing Sustainable Food Systems“ umgesetzt wurden und inwieweit sie erfolgreich waren.
Das Training sozioemotionaler Kompetenzen ist gerade für Menschen mit Autismus nützlich. Ein solches Training kann mithilfe einer spielbasierten Anwendung effektiv gestaltet werden. Zwei Minispiele, Mimikry und Emo-Mahjong, wurden realisiert und hinsichtlich User Experience evaluiert. Die jeweiligen Konzepte und die Evaluationsergebnisse sollen hier vorgestellt werden.
Einsatz einer mobilen Lern-App - Ein Werkzeug zur Verbesserung von klinisch-praktischem Unterricht
(2018)
Der Unterricht am Krankenbett ist im Medizinstudium eine wertvolle Möglichkeit klinisch-praktische Fertigkeiten zu erlernen. Eine optimale Vorbereitung der Studierenden ist dabei Voraussetzung. Eine mobile Lern-App wurde entwickelt, die den Studierenden, neben Lernzielen, Kursinhalte und Anleitungen zu Untersuchungstechniken bietet, um die Vorbereitung auf einen klinisch-praktischen Kurs zu fördern und Kurzinformationen auch während des Kurses zur Verfügung zu stellen. 175 Studierende hatten die Möglichkeit die App parallel zu einem klinischen Untersuchungs-Kurs im Semester zu nutzen. Im Anschluss beantworteten die Studierenden einen Fragebogen zur Nützlichkeit und Vielseitigkeit der App und zur Zufriedenheit mit der App unter Verwendung eine 5-Punkt-Likert-Skala und zwei offenen Fragen. In diesem Beitrag wird das Kurskonzept zusammen mit der Lern-App, die Ergebnisse aus dem Fragebogen und unsere Schlussfolgerungen daraus vorgestellt. Studierende bewerteten die App grundsätzlich als hilfreich. Sie sollte dabei gründlich eingeführt werden. Patienten sollten über die Nutzung von Smartphones im Studentenunterricht zu Lernzwecken informiert werden.
Der vorliegende Beitrag berichtet auf der Grundlage von Erfahrungen mit dem Audience Response System (ARS) „Auditorium Mobile Classroom Service“ von Erfolgsfaktoren für den Einsatz in der universitären Lehre. Dabei werden sowohl die technischen Rahmenbedingungen und Herausforderungen der Anwendungen berücksichtigt, als auch die unterschiedlichen didaktischen Konzepte und Ziele der beteiligten Akteure (Studierende, Lehrende und Institution). Ziel ist es, Einflussfaktoren für den erfolgreichen Einsatz sowohl für die Praxis als auch die wissenschaftliche Untersuchung und Weiterentwicklung der Systeme zu benennen und ein heuristisches Framework für Chancen und Herausforderungen beim Einsatz von ARS anzubieten.
Der Beitrag skizziert ein Modell, das die Entwicklung digitaler Kompetenzen im Lehramtsstudium fördern soll. Zwar wird das Kompetenzmodell aus der Deutschdidaktik heraus entwickelt, nimmt aber auch fachübergreifende Anforderungen in den Bereichen Informationskompetenz, medientechnischer Kompetenzen, Fähigkeiten der Medienanalyse und -reflexion sowie Sprachhandlungskompetenz in den Blick. Damit wird das Ziel verfolgt, die besonderen Anforderungen angehender Lehrkräfte als Mediator*innen digitaler Kompetenzen darzustellen. Das beschriebene Modell dieser Vermittlungskompetenz dient der Verankerung digitaler Lehr-Lernkonzepte als wesentlicher Bestandteil der modernen Lehrer*innenbildung.
Um beim Berufseinstieg erfolgreich als Informatiker wirken zu können, reicht es oft nicht aus nur separierte Kenntnisse über technische und theoretische Grundlagen, Programmiersprachen, Werkzeuge und Selbst- und Zeitmanagement zu besitzen. Vielmehr sollten Absolventen diese Kenntnisse praktisch miteinander verzahnt einsetzen können. An der Universität wird Studierenden leider selten die Möglichkeit geboten, diese verschiedenen Bereiche der Informatik miteinander integriert auszuüben. Dafür entwickeln wir seit über zwei Dekaden ein Lehr- und Lernkonzept zur Unterstützung praktischer Softwareentwicklungsveranstaltungen und setzen dieses um. Dadurch bieten wir angehenden SoftwareentwicklerInnen und ProjektmanagerInnen eine Umgebung, in der sie neues, praktisch relevantes Wissen erwerben können, sich selbst praktisch erproben und ihr Wissen konkret einsetzen können. Hier legen wir einen Schwerpunkt auf das Arbeiten im Team. Das hier vorgestellte Konzept kann auf ähnliche Lehrveranstaltungen übertragen und aufgrund seiner Modularisierung verändert und erweitert werden.
Im Rahmen eines Informatikstudiums wird neben theoretischen Grundlagen und Programmierfähigkeiten auch gezielt vermittelt, wie moderne Software in der Praxis entwickelt wird. Dabei wird oftmals eine Form der Projektarbeit gewählt, um Studierenden möglichst realitätsnahe Erfahrungen zu ermöglichen. Die Studierenden entwickeln einzeln oder in kleineren Teams Softwareprodukte für ausgewählte Problemstellungen. Neben fachlichen Inhalte stehen durch gruppendynamische Prozesse auch überfachliche Kompetenzen im Fokus. Dieser Beitrag präsentiert eine Interviewstudie mit Dozierenden von Softwareprojektpraktika an der RWTH Aachen und konzentriert sich auf die Ausgestaltung der Veranstaltungen sowie Förderung von überfachlichen Kompetenzen nach einem Kompetenzprofil für Softwareingenieure.
Empirische Untersuchungen von Lückentext-Items zur Beherrschung der Syntax einer Programmiersprache
(2018)
Lückentext-Items auf der Basis von Programmcode können eingesetzt werden, um Kenntnisse in der Syntax einer Programmiersprache zu prüfen, ohne dazu komplexe Programmieraufgaben zu stellen, deren Bearbeitung weitere Kompetenzen erfordert. Der vorliegende Beitrag dokumentiert den Einsatz von insgesamt zehn derartigen Items in einer universitären Erstsemestervorlesung zur Programmierung mit Java. Es werden sowohl Erfahrungen mit der Konstruktion der Items als auch empirische Daten aus dem Einsatz diskutiert. Der Beitrag zeigt dadurch insbesondere die Herausforderungen bei der Konstruktion valider Instrumente zur Kompetenzmessung in der Programmierausbildung auf. Die begrenzten und teilweise vorläufigen Ergebnisse zur Qualität der erzeugten Items legen trotzdem nahe, dass Erstellung und Einsatz entsprechender Items möglich ist und einen Beitrag zur Kompetenzmessung leisten kann.
Was ist Data Science?
(2018)
In Zusammenhang mit den Entwicklungen der vergangenen Jahre, insbesondere in den Bereichen Big Data, Datenmanagement und Maschinenlernen, hat sich der Umgang mit Daten und deren Analyse wesentlich weiterentwickelt. Mittlerweile wird die Datenwissenschaft als eigene Disziplin angesehen, die auch immer stärker durch entsprechende Studiengänge an Hochschulen repräsentiert wird. Trotz dieser zunehmenden Bedeutung ist jedoch oft unklar, welche konkreten Inhalte mit ihr in Verbindung stehen, da sie in verschiedensten Ausprägungen auftritt. In diesem Beitrag werden daher die hinter der Data Science stehenden informatischen Inhalte durch eine qualitative Analyse der Modulhandbücher etablierter Studiengänge aus diesem Bereich ermittelt und so ein Beitrag zur Charakterisierung dieser Disziplin geleistet. Am Beispiel der Entwicklung eines Data-Literacy-Kompetenzmodells, die als Ausblick skizziert wird, wird die Bedeutung dieser Charakterisierung für die weitere Forschung expliziert.
In diesem Artikel werden die Ergebnisse einer explorativen Datenanalyse über die Studierendenperformance in Klausur- und Hausaufgaben eines Einführungskurses der Theoretischen Informatik vorgestellt. Da bisher empirisch wenig untersucht ist, welche Probleme Studierenden in den Einführungskursen haben und die Durchfallquoten in diesen Kursen sehr hoch sind, soll auf diesem Weg ein Überblick gegeben werden. Die Ergebnisse zeigen, dass alle Studierenden unabhängig von ihrer Klausurnote die niedrigste Performance in den Klausur- und Hausaufgaben aufweisen, in denen formale Beweise gefordert sind. Dieses Ergebnis stärkt die Vermutung, dass didaktische
Ansätze und Maßnahmen sich insbesondere auf das Erlernen formaler Beweismethoden fokussieren sollten, um Informatik-Studierende nachhaltiger dabei zu unterstützen, in Theoretischer Informatik erfolgreich zu sein.
Die Lehre von wissenschaftlichem Arbeiten stellt einen zentralen Aspekt in forschungsorientierten Studiengängen wie der Informatik dar. Trotz diverser Angebote werden mittel- und langfristig Mängel in der
Arbeitsqualität von Studierenden sichtbar. Dieses Paper analysiert daher das Profil der Studierenden, deren Anwendung des wissenschaftlichen Arbeitens, und das Angebot von Proseminaren zum Thema „Einführung in das wissenschaftliche Arbeiten“ einer deutschen Universität. Die Ergebnisse mehrerer Erhebungen zeigen dabei diverse Probleme bei Studierenden auf, u. a. bei dem Prozessverständnis, dem Zeitmanagement und der Kommunikation.
Um für ein Leben in der digitalen Gesellschaft vorbereitet zu sein, braucht jeder heute in verschiedenen Situationen umfangreiche informatische Grundlagen. Die Bedeutung von Informatik nimmt nicht nur in immer mehr
Bereichen unseres täglichen Lebens zu, sondern auch in immer mehr Ausbildungsrichtungen. Um junge Menschen auf ihr zukünftiges Leben und/oder ihre zukünftige berufliche Tätigkeit vorzubereiten, bieten verschiedene Hochschulen Informatikmodule für Studierende anderer Fachrichtungen an. Die Materialien jener Kurse bilden einen umfangreichen Datenpool, um die für Studierende anderer Fächer bedeutenden Aspekte der Informatik mithilfe eines empirischen Ansatzes zu identifizieren. Im Folgenden werden 70 Module zu informatischer Bildung für Studierende anderer Fachrichtungen analysiert. Die Materialien – Publikationen, Syllabi und Stundentafeln – werden zunächst mit einer qualitativen Inhaltsanalyse nach Mayring untersucht und anschließend quantitativ ausgewertet. Basierend auf der Analyse werden Ziele, zentrale Themen und Typen eingesetzter Werkzeuge identifiziert.
Students of computer science studies enter university education with very different competencies, experience and knowledge. 145 datasets collected of freshmen computer science students by learning management systems in relation to exam outcomes and learning dispositions data (e. g. student dispositions, previous experiences and attitudes measured through self-reported surveys) has been exploited to identify indicators as predictors of academic success and hence make effective interventions to deal with an extremely heterogeneous group of students.
Vorlesungs-Pflege
(2018)
Ähnlich zu Alterungsprozessen bei Software degenerieren auch Vorlesungen, wenn sie nicht hinreichend gepflegt werden. Die Gründe hierfür werden ebenso beleuchtet wie mögliche Indikatoren und Maßnahmen – der Blick ist dabei immer der eines Informatikers. An drei Vorlesungen wird erläutert, wie der Degeneration von Lehrveranstaltungen
gegengewirkt werden kann. Mangels hinreichend großer empirischer Daten liefert das Paper keine unumstößlichen Wahrheiten. Ein Ziel ist es vielmehr Kollegen, die ähnliche Phänomene beobachten, einen ersten Anker für einen
inneren Diskurs zu bieten. Ein langfristiges Ziel ist die Sammlung eines Katalogs an Maßnahmen zur Pflege von Informatikvorlesungen.
Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing, and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM (Long Short-Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long-term visual-language interactions by making use of history and future context information at high-level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different ways to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale, and vertical mirror are proposed to prevent over-fitting in training deep models. To understand how our models "translate" image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO, and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model). Our experiments also prove that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate the performance of transfer learning of the Bi-LSTM model significantly outperforms previous methods on the Pascal1K dataset.
In der dualen IT-Ausbildung als Verbindung von beruflicher und akademischer Qualifikation werden die berufstypischen Werkzeuge, wie z. B. Laptops, ebenso in den Lehr-Lern-Prozessen der akademischen Unterrichtseinheiten eingesetzt. Im Prüfungswesen wird oft auf klassische Papierklausuren zurückgegriffen. Unterrichtseinheiten mit hohem Blended-Learning-Anteil ohne E-Prüfung werden dabei als „nicht konsistent“ wahrgenommen. In diesem Artikel wird eine empirische Studie dargelegt, die untersucht, welche Einflüsse aus der persönlichen Lernbiografie bei den Lehrenden in einer dualen IT-Ausbildung dazu führen können, die Möglichkeiten eines E-Assessments als summative Modulprüfung anzunehmen oder abzulehnen. Beispielhaft wurden in der dargelegten Studie Interviews mit Dozenten geführt und diese hinsichtlich der Verbindung zwischen Lernbiografie, Gestaltung der Didaktik der Lehr-Lern-Prozesse, Zufriedenheit und Veränderungsbereitschaft untersucht.
Berufsbegleitende Studiengänge stehen vor besonderen Schwierigkeiten, für die der Einsatz von Blended Learning-Szenarien sinnvoll sein kann. Welche speziellen Herausforderungen sich dabei ergeben und welche Lösungsansätze dagegen steuern, betrachtet der folgende Artikel anhand eines Praxisberichts aus dem Studiengang M. P. A. Wissenschaftsmanagement an der Universität Speyer.
While the role of and consequences of being a bystander to face-to-face bullying has received some attention in the literature, to date, little is known about the effects of being a bystander to cyberbullying. It is also unknown how empathy might impact the negative consequences associated with being a bystander of cyberbullying. The present study focused on examining the longitudinal association between bystander of cyberbullying depression, and anxiety, and the moderating role of empathy in the relationship between bystander of cyberbullying and subsequent depression and anxiety. There were 1,090 adolescents (M-age = 12.19; 50% female) from the United States included at Time 1, and they completed questionnaires on empathy, cyberbullying roles (bystander, perpetrator, victim), depression, and anxiety. One year later, at Time 2, 1,067 adolescents (M-age = 13.76; 51% female) completed questionnaires on depression and anxiety. Results revealed a positive association between bystander of cyberbullying and depression and anxiety. Further, empathy moderated the positive relationship between bystander of cyberbullying and depression, but not for anxiety. Implications for intervention and prevention programs are discussed.
plasp 3
(2019)
We describe the new version of the Planning Domain Definition Language (PDDL)-to-Answer Set Programming (ASP) translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by Satisfiability Testing (SAT) planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.
Multi-sided platforms (MSP) strongly affect markets and play a crucial part within the digital and networked economy. Although empirical evidence indicates their occurrence in many industries, research has not investigated the game-changing impact of MSP on traditional markets to a sufficient extent. More specifically, we have little knowledge of how MSP affect value creation and customer interaction in entire markets, exploiting the potential of digital technologies to offer new value propositions. Our paper addresses this research gap and provides an initial systematic approach to analyze the impact of MSP on the insurance industry. For this purpose, we analyze the state of the art in research and practice in order to develop a reference model of the value network for the insurance industry. On this basis, we conduct a case-study analysis to discover and analyze roles which are occupied or even newly created by MSP. As a final step, we categorize MSP with regard to their relation to traditional insurance companies, resulting in a classification scheme with four MSP standard types: Competition, Coordination, Cooperation, Collaboration.
The development of new and better optimization and approximation methods for Job Shop Scheduling Problems (JSP) uses simulations to compare their performance. The test data required for this has an uncertain influence on the simulation results, because the feasable search space can be changed drastically by small variations of the initial problem model. Methods could benefit from this to varying degrees. This speaks in favor of defining standardized and reusable test data for JSP problem classes, which in turn requires a systematic describability of the test data in order to be able to compile problem adequate data sets. This article looks at the test data used for comparing methods by literature review. It also shows how and why the differences in test data have to be taken into account. From this, corresponding challenges are derived which the management of test data must face in the context of JSP research.
Indexes are essential for the efficient processing of database workloads. Proposed solutions for the relevant and challenging index selection problem range from metadata-based simple heuristics, over sophisticated multi-step algorithms, to approaches that yield optimal results. The main challenges are (i) to accurately determine the effect of an index on the workload cost while considering the interaction of indexes and (ii) a large number of possible combinations resulting from workloads containing many queries and massive schemata with possibly thousands of attributes. <br /> In this work, we describe and analyze eight index selection algorithms that are based on different concepts and compare them along different dimensions, such as solution quality, runtime, multi-column support, solution granularity, and complexity. In particular, we analyze the solutions of the algorithms for the challenging analytical Join Order, TPC-H, and TPC-DS benchmarks. Afterward, we assess strengths and weaknesses, infer insights for index selection in general and each approach individually, before we give recommendations on when to use which approach.
Data errors represent a major issue in most application workflows. Before any important task can take place, a certain data quality has to be guaranteed by eliminating a number of different errors that may appear in data. Typically, most of these errors are fixed with data preparation methods, such as whitespace removal. However, the particular error of duplicate records, where multiple records refer to the same entity, is usually eliminated independently with specialized techniques. Our work is the first to bring these two areas together by applying data preparation operations under a systematic approach prior to performing duplicate detection. <br /> Our process workflow can be summarized as follows: It begins with the user providing as input a sample of the gold standard, the actual dataset, and optionally some constraints to domain-specific data preparations, such as address normalization. The preparation selection operates in two consecutive phases. First, to vastly reduce the search space of ineffective data preparations, decisions are made based on the improvement or worsening of pair similarities. Second, using the remaining data preparations an iterative leave-one-out classification process removes preparations one by one and determines the redundant preparations based on the achieved area under the precision-recall curve (AUC-PR). Using this workflow, we manage to improve the results of duplicate detection up to 19% in AUC-PR.
Challenges for self-driving database systems, which tune their physical design and configuration autonomously, are manifold: Such systems have to anticipate future workloads, find robust configurations efficiently, and incorporate knowledge gained by previous actions into later decisions. We present a component-based framework for self-driving database systems that enables database integration and development of self-managing functionality with low overhead by relying on separation of concerns. By keeping the components of the framework reusable and exchangeable, experiments are simplified, which promotes further research in that area. Moreover, to optimize multiple mutually dependent features, e.g., index selection and compression configurations, we propose a linear programming (LP) based algorithm to derive an efficient tuning order automatically. Afterwards, we demonstrate the applicability and scalability of our approach with reproducible examples.
Author summary <br /> The use of orally inhaled drugs for treating lung diseases is appealing since they have the potential for lung selectivity, i.e. high exposure at the site of action -the lung- without excessive side effects. However, the degree of lung selectivity depends on a large number of factors, including physiochemical properties of drug molecules, patient disease state, and inhalation devices. To predict the impact of these factors on drug exposure and thereby to understand the characteristics of an optimal drug for inhalation, we develop a predictive mathematical framework (a "pharmacokinetic model"). In contrast to previous approaches, our model allows combining knowledge from different sources appropriately and its predictions were able to adequately predict different sets of clinical data. Finally, we compare the impact of different factors and find that the most important factors are the size of the inhaled particles, the affinity of the drug to the lung tissue, as well as the rate of drug dissolution in the lung. In contrast to the common belief, the solubility of a drug in the lining fluids is not found to be relevant. These findings are important to understand how inhaled drugs should be designed to achieve best treatment results in patients. <br /> The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics.
Reading traces
(2020)
Through a design study, we develop an approach to data exploration that utilizes elastic visualizations designed to support varying degrees of detail and abstraction. Examining the notions of scalability and elasticity in interactive visualizations, we introduce a visualization of personal reading traces such as marginalia or markings inside the reference library of German realist author Theodor Fontane. To explore such a rich and extensive collection, meaningful visual forms of abstraction and detail are as important as the transitions between those states. Following a growing research interest in the role of fluid interactivity and animations between views, we are particularly interested in the potential of carefully designed transitions and consistent representations across scales. The resulting prototype addresses humanistic research questions about the interplay of distant and close reading with visualization research on continuous navigation along several granularity levels, using scrolling as one of the main interaction mechanisms. In addition to presenting the design process and resulting prototype, we present findings from a qualitative evaluation of the tool, which suggest that bridging between distant and close views can enhance exploration, but that transitions between views need to be crafted very carefully to facilitate comprehension.
Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.
Large real-world networks typically follow a power-law degree distribution. To study such networks, numerous random graph models have been proposed. However, real-world networks are not drawn at random. Therefore, Brach et al. (27th symposium on discrete algorithms (SODA), pp 1306-1325, 2016) introduced two natural deterministic conditions: (1) a power-law upper bound on the degree distribution (PLB-U) and (2) power-law neighborhoods, that is, the degree distribution of neighbors of each vertex is also upper bounded by a power law (PLB-N). They showed that many real-world networks satisfy both properties and exploit them to design faster algorithms for a number of classical graph problems. We complement their work by showing that some well-studied random graph models exhibit both of the mentioned PLB properties. PLB-U and PLB-N hold with high probability for Chung-Lu Random Graphs and Geometric Inhomogeneous Random Graphs and almost surely for Hyperbolic Random Graphs. As a consequence, all results of Brach et al. also hold with high probability or almost surely for those random graph classes. In the second part we study three classical NP-hard optimization problems on PLB networks. It is known that on general graphs with maximum degree Delta, a greedy algorithm, which chooses nodes in the order of their degree, only achieves a Omega (ln Delta)-approximation forMinimum Vertex Cover and Minimum Dominating Set, and a Omega(Delta)-approximation forMaximum Independent Set. We prove that the PLB-U property with beta>2 suffices for the greedy approach to achieve a constant-factor approximation for all three problems. We also show that these problems are APX-hard even if PLB-U, PLB-N, and an additional power-law lower bound on the degree distribution hold. Hence, a PTAS cannot be expected unless P = NP. Furthermore, we prove that all three problems are in MAX SNP if the PLB-U property holds.
In recent years, the increased interest in application areas such as social networks has resulted in a rising popularity of graph-based approaches for storing and processing large amounts of interconnected data. To extract useful information from the growing network structures, efficient querying techniques are required.
In this paper, we propose an approach for graph pattern matching that allows a uniform handling of arbitrary constraints over the query vertices. Our technique builds on a previously introduced matching algorithm, which takes concrete host graph information into account to dynamically adapt the employed search plan during query execution. The dynamic algorithm is combined with an existing static approach for search plan generation, resulting in a hybrid technique which we further extend by a more sophisticated handling of filtering effects caused by constraint checks. We evaluate the presented concepts empirically based on an implementation for our graph pattern matching tool, the Story Diagram Interpreter, with queries and data provided by the LDBC Social Network Benchmark. Our results suggest that the hybrid technique may improve search efficiency in several cases, and rarely reduces efficiency.
This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the (legally required) trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification.
M-rate 0L systems are interactionless Lindenmayer systems together with a function assigning to every string a set of multisets of productions that may be applied simultaneously to the string. Some questions that have been left open in the forerunner papers are examined, and the computational power of deterministic M-rate 0L systems is investigated, where also tabled and extended variants are taken into consideration.
This special issue contains extended versions of four selected papers from the 11th International Conference on Graph Transformation (ICGT 2018). The articles cover a tool for computing core graphs via SAT/SMT solvers (graph language definition), graph transformation through graph surfing in reaction systems (a new graph transformation formalism), the essence and initiality of conflicts in M-adhesive transformation systems, and a calculus of concurrent graph-rewriting processes (theory on conflicts and parallel independence).
Die Digitalisierung von Produktionsprozessen schreitet mit einer hohen Intensität voran. Weiterbildung hat eine hohe Relevanz für betriebliche Transformationsprozesse. Die betriebliche Weiterbildungspraxis ist den aktuellen Herausforderungen der Digitalisierung jedoch nicht gewachsen. Herausforderungen sind Kompetenzlücken der Mitarbeiter, ungewisse Anforderungsprofile und Tätigkeitstypen, demographischer Wandel sowie veraltete didaktische Ansätze. Zudem wird bestehender inhaltlicher und pädagogischer Freiraum bei der Gestaltung von Weiterbildung oftmals nur unzureichend ausgenutzt. Die skizzierte Situation führt dazu, dass der Mehrwert gegenwärtiger Qualifizierungsangebote sowohl für Unternehmen als auch Beschäftigte nicht ausgeschöpft wird. Ausgehend von Veränderungen durch Digitalisierung in der Produktion und deren Auswirkungen auf die Kompetenzentwicklung diskutiert dieser Beitrag Herausforderungen gegenwärtiger betrieblicher Weiterbildung. Er leitet Handlungsempfehlungen ab, die mithilfe von Beispielen gewerkschaftlich unterstützter Weiterbildungspraxis illustriert werden. Im Ergebnis erhalten Interessierte einen Überblick über gegenwärtige Herausforderungen und Handlungsempfehlungen für die Gestaltung und Durchführung von Weiterbildung in Zeiten der Digitalisierung.
User Experience (UX) describes the holistic experience of a user before, during, and after interaction with a platform, product, or service. UX adds value and attraction to their sole functionality and is therefore highly relevant for firms. The increased interest in UX has produced a vast amount of scholarly research since 1983. The research field is, therefore, complex and scattered. Conducting a bibliometric analysis, we aim at structuring the field quantitatively and rather abstractly. We employed citation analyses, co-citation analyses, and content analyses to evaluate productivity and impact of extant research. We suggest that future research should focus more on business and management related topics.
Multiplicative Up-Drift
(2020)
Drift analysis aims at translating the expected progress of an evolutionary algorithm (or more generally, a random process) into a probabilistic guarantee on its run time (hitting time). So far, drift arguments have been successfully employed in the rigorous analysis of evolutionary algorithms, however, only for the situation that the progress is constant or becomes weaker when approaching the target. Motivated by questions like how fast fit individuals take over a population, we analyze random processes exhibiting a (1+delta)-multiplicative growth in expectation. We prove a drift theorem translating this expected progress into a hitting time. This drift theorem gives a simple and insightful proof of the level-based theorem first proposed by Lehre (2011). Our version of this theorem has, for the first time, the best-possible near-linear dependence on 1/delta} (the previous results had an at least near-quadratic dependence), and it only requires a population size near-linear in delta (this was super-quadratic in previous results). These improvements immediately lead to stronger run time guarantees for a number of applications. We also discuss the case of large delta and show stronger results for this setting.
Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions.
Several numerical tools designed to overcome the challenges of smoothing in a non-linear and non-Gaussian setting are investigated for a class of particle smoothers. The considered family of smoothers is induced by the class of linear ensemble transform filters which contains classical filters such as the stochastic ensemble Kalman filter, the ensemble square root filter, and the recently introduced nonlinear ensemble transform filter. Further the ensemble transform particle smoother is introduced and particularly highlighted as it is consistent in the particle limit and does not require assumptions with respect to the family of the posterior distribution. The linear update pattern of the considered class of linear ensemble transform smoothers allows one to implement important supplementary techniques such as adaptive spread corrections, hybrid formulations, and localization in order to facilitate their application to complex estimation problems. These additional features are derived and numerically investigated for a sequence of increasingly challenging test problems.
Social Media, Quo Vadis?
(2020)
Over the past two decades, social media have become a crucial and omnipresent cultural and economic phenomenon, which has seen platforms come and go and advance technologically. In this study, we explore the further development of social media regarding interactive technologies, platform development, relationships to news media, the activities of institutional and organizational users, and effects of social media on the individual and the society over the next five to ten years by conducting an international, two-stage Delphi study. Our results show that enhanced interaction on platforms, including virtual and augmented reality, somatosensory sense, and touch- and movement-based navigation are expected. AIs will interact with other social media users. Inactive user profiles will outnumber active ones. Platform providers will diversify into the WWW, e-commerce, edu-tech, fintechs, the automobile industry, and HR. They will change to a freemium business model and put more effort into combating cybercrime. Social media will become the predominant news distributor, but fake news will still be problematic. Firms will spend greater amounts of their budgets on social media advertising, and schools, politicians, and the medical sector will increase their social media engagement. Social media use will increasingly lead to individuals’ psychic issues. Society will benefit from economic growth and new jobs, increased political interest, democratic progress, and education due to social media. However, censorship and the energy consumption of platform operators might rise.
Homeoffice und mobiles Arbeiten haben sich infolge der Covid-19-Pandemie bei vielen Unternehmen bekanntlich etabliert. Die Anweisung bzw. „Duldung“ des Homeoffice beruhte allerdings meist mehr auf tatsächlicher als auf rechtlicher Grundlage. Letztere könnte aber aus betrieblicher Übung erwachsen. Dieser Beitrag geht dem rechtlichen Rahmen dafür nach.
Arbeitsschutz bei Corona
(2020)
CloudStrike
(2020)
Most cyber-attacks and data breaches in cloud infrastructure are due to human errors and misconfiguration vulnerabilities. Cloud customer-centric tools are imperative for mitigating these issues, however existing cloud security models are largely unable to tackle these security challenges. Therefore, novel security mechanisms are imperative, we propose Risk-driven Fault Injection (RDFI) techniques to address these challenges. RDFI applies the principles of chaos engineering to cloud security and leverages feedback loops to execute, monitor, analyze and plan security fault injection campaigns, based on a knowledge-base. The knowledge-base consists of fault models designed from secure baselines, cloud security best practices and observations derived during iterative fault injection campaigns. These observations are helpful for identifying vulnerabilities while verifying the correctness of security attributes (integrity, confidentiality and availability). Furthermore, RDFI proactively supports risk analysis and security hardening efforts by sharing security information with security mechanisms. We have designed and implemented the RDFI strategies including various chaos engineering algorithms as a software tool: CloudStrike. Several evaluations have been conducted with CloudStrike against infrastructure deployed on two major public cloud infrastructure: Amazon Web Services and Google Cloud Platform. The time performance linearly increases, proportional to increasing attack rates. Also, the analysis of vulnerabilities detected via security fault injection has been used to harden the security of cloud resources to demonstrate the effectiveness of the security information provided by CloudStrike. Therefore, we opine that our approaches are suitable for overcoming contemporary cloud security issues.
Social networking sites (SNS) are a rich source of latent information about individual characteristics. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, commercial brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. Predictive evaluation on brands' accounts reveals that Facebook platform provides a slight advantage over Twitter platform in offering more self-disclosure for users' to express their emotions especially their demographic and psychological traits. Results also confirm the wider perspective that the same social media account carry a quite similar and comparable personality scores over different social media platforms. For evaluating our prediction results on actual brands' accounts, we crawled the Facebook API and Twitter API respectively for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary.
Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines' instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 % for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.
TPC-H continues to be the most widely used benchmark for relational OLAP systems. It poses a number of challenges, also known as "choke points", which database systems have to solve in order to achieve good benchmark results. Examples include joins across multiple tables, correlated subqueries, and correlations within the TPC-H data set. Knowing the impact of such optimizations helps in developing optimizers as well as in interpreting TPC-H results across database systems.
This paper provides a systematic analysis of choke points and their optimizations. It complements previous work on TPC-H choke points by providing a quantitative discussion of their relevance. It focuses on eleven choke points where the optimizations are beneficial independently of the database system. Of these, the flattening of subqueries and the placement of predicates have the biggest impact. Three queries (Q2, Q17, and Q21) are strongly ifluenced by the choice of an efficient query plan; three others (Q1, Q13, and Q18) are less influenced by plan optimizations and more dependent on an efficient execution engine.
Argument mining on twitter
(2021)
In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.
We systematically explore the effect of calibration data length on the performance of a conceptual hydrological model, GR4H, in comparison to two Artificial Neural Network (ANN) architectures: Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU), which have just recently been introduced to the field of hydrology. We implemented a case study for six river basins across the contiguous United States, with 25 years of meteorological and discharge data. Nine years were reserved for independent validation; two years were used as a warm-up period, one year for each of the calibration and validation periods, respectively; from the remaining 14 years, we sampled increasing amounts of data for model calibration, and found pronounced differences in model performance. While GR4H required less data to converge, LSTM and GRU caught up at a remarkable rate, considering their number of parameters. Also, LSTM and GRU exhibited the higher calibration instability in comparison to GR4H. These findings confirm the potential of modern deep-learning architectures in rainfall runoff modelling, but also highlight the noticeable differences between them in regard to the effect of calibration data length.