004 Datenverarbeitung; Informatik
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In der letzten Jahren ist die Zahl der erfolgreichen Prüfungen von Studierenden im Informatikkurs des ersten Studienjahres für verschiedene Studiengänge an der Universität Óbuda stark gesunken. Dies betrifft Prüfungen in den Teilgebieten Rechnerarchitektur, Betrieb von Peripheriegeräten, Binäre Codierung und logische Operationen, Computerviren, Computernetze und das Internet, Steganographie und Kryptographie, Betriebsysteme. Mehr als der Hälfte der Studenten konnte die Prüfungen der ersten Semester nicht erfolgreich absolvieren. Die hier vorgelegte Analyse der Studienleistungen zielt darauf ab, Gründe für diese Entwicklung zu identifizieren, die Zahl der Abbrecher zu reduzieren und die Leistungen der Studenten zu verbessern. Die Analyse zeigt, dass die Studenten die erforderlichen Lehrmaterialen erst ein bis zwei Tage vor oder sogar erst am Tag der Klausuren vom Server downloaden, so dass sie nicht mehr hinreichend Zeit zum Lernen haben. Diese Tendenz zeigt sich bei allen Teilgebieten des Studiengangs. Ein Mangel an kontinuierlicher Mitarbeit scheint einer der Gründe für ein frühes Scheitern zu sein. Ferner zeigt sich die Notwendigkeit, dass bei den Lehrangeboten in Informatik auf eine kontinuierliche Kommunikation mit den Studierenden und Rückmeldung zu aktuellen Unterrichtsinhalten zu achten ist. Dies kann durch motivierende Maßnahmen zur Teilnahme an den Übungen oder durch kleine wöchentliche schriftliche Tests geschehen.
Helping overcome distance, the use of videoconferencing tools has surged during the pandemic. To shed light on the consequences of videoconferencing at work, this study takes a granular look at the implications of the self-view feature for meeting outcomes. Building on self-awareness research and self-regulation theory, we argue that by heightening the state of self-awareness, self-view engagement depletes participants’ mental resources and thereby can undermine online meeting outcomes. Evaluation of our theoretical model on a sample of 179 employees reveals a nuanced picture. Self-view engagement while speaking and while listening is positively associated with self-awareness, which, in turn, is negatively associated with satisfaction with meeting process, perceived productivity, and meeting enjoyment. The criticality of the communication role is put forward: looking at self while listening to other attendees has a negative direct and indirect effect on meeting outcomes; however, looking at self while speaking produces equivocal effects.
The noble way to substantiate decisions that affect many people is to ask these people for their opinions. For governments that run whole countries, this means asking all citizens for their views to consider their situations and needs.
Organizations such as Africa's Voices Foundation, who want to facilitate communication between decision-makers and citizens of a country, have difficulty mediating between these groups. To enable understanding, statements need to be summarized and visualized. Accomplishing these goals in a way that does justice to the citizens' voices and situations proves challenging. Standard charts do not help this cause as they fail to create empathy for the people behind their graphical abstractions. Furthermore, these charts do not create trust in the data they are representing as there is no way to see or navigate back to the underlying code and the original data. To fulfill these functions, visualizations would highly benefit from interactions to explore the displayed data, which standard charts often only limitedly provide.
To help improve the understanding of people's voices, we developed and categorized 80 ideas for new visualizations, new interactions, and better connections between different charts, which we present in this report. From those ideas, we implemented 10 prototypes and two systems that integrate different visualizations. We show that this integration allows consistent appearance and behavior of visualizations. The visualizations all share the same main concept: representing each individual with a single dot. To realize this idea, we discuss technologies that efficiently allow the rendering of a large number of these dots. With these visualizations, direct interactions with representations of individuals are achievable by clicking on them or by dragging a selection around them. This direct interaction is only possible with a bidirectional connection from the visualization to the data it displays. We discuss different strategies for bidirectional mappings and the trade-offs involved. Having unified behavior across visualizations enhances exploration. For our prototypes, that includes grouping, filtering, highlighting, and coloring of dots. Our prototyping work was enabled by the development environment Lively4. We explain which parts of Lively4 facilitated our prototyping process. Finally, we evaluate our approach to domain problems and our developed visualization concepts.
Our work provides inspiration and a starting point for visualization development in this domain. Our visualizations can improve communication between citizens and their government and motivate empathetic decisions. Our approach, combining low-level entities to create visualizations, provides value to an explorative and empathetic workflow. We show that the design space for visualizing this kind of data has a lot of potential and that it is possible to combine qualitative and quantitative approaches to data analysis.
We describe a framework to support the implementation of web-based systems to manipulate data stored in relational databases. Since the conceptual model of a relational database is often specified as an entity-relationship (ER) model, we propose to use the ER model to generate a complete implementation in the declarative programming language Curry. This implementation contains operations to create and manipulate entities of the data model, supports authentication, authorization, session handling, and the composition of individual operations to user processes. Furthermore and most important, the implementation ensures the consistency of the database w.r.t. the data dependencies specified in the ER model, i.e., updates initiated by the user cannot lead to an inconsistent state of the database. In order to generate a high-level declarative implementation that can be easily adapted to individual customer requirements, the framework exploits previous works on declarative database programming and web user interface construction in Curry.
We launched an original large-scale experiment concerning informatics learning in French high schools. We are using the France-IOI platform to federate resources and share observation for research. The first step is the implementation of an adaptive hypermedia based on very fine grain epistemic modules for Python programming learning. We define the necessary traces to be built in order to study the trajectories of navigation the pupils will draw across this hypermedia. It may be browsed by pupils either as a course support, or an extra help to solve the list of exercises (mainly for algorithmics discovery). By leaving the locus of control to the learner, we want to observe the different trajectories they finally draw through our system. These trajectories may be abstracted and interpreted as strategies and then compared for their relative efficiency. Our hypothesis is that learners have different profiles and may use the appropriate strategy accordingly. This paper presents the research questions, the method and the expected results.
Although educational content in electronic form is increasing dramatically, its usage in an educational environment is poor, mainly due to the fact that there is too much of (unreliable) redundant, and not relevant information. Finding appropriate answers is a rather difficult task being reliant on the user filtering of the pertinent information from the noise. Turning knowledge bases like the online tele-TASK archive into useful educational resources requires identifying correct, reliable, and "machine-understandable" information, as well as developing simple but efficient search tools with the ability to reason over this information. Our vision is to create an E-Librarian Service, which is able to retrieve multimedia resources from a knowledge base in a more efficient way than by browsing through an index, or by using a simple keyword search. In our E-Librarian Service, the user can enter his question in a very simple and human way; in natural language (NL). Our premise is that more pertinent results would be retrieved if the search engine understood the sense of the user's query. The returned results are then logical consequences of an inference rather than of keyword matchings. Our E-Librarian Service does not return the answer to the user's question, but it retrieves the most pertinent document(s), in which the user finds the answer to his/her question. Among all the documents that have some common information with the user query, our E-Librarian Service identifies the most pertinent match(es), keeping in mind that the user expects an exhaustive answer while preferring a concise answer with only little or no information overhead. Also, our E-Librarian Service always proposes a solution to the user, even if the system concludes that there is no exhaustive answer. Our E-Librarian Service was implemented prototypically in three different educational tools. A first prototype is CHESt (Computer History Expert System); it has a knowledge base with 300 multimedia clips that cover the main events in computer history. A second prototype is MatES (Mathematics Expert System); it has a knowledge base with 115 clips that cover the topic of fractions in mathematics for secondary school w.r.t. the official school programme. All clips were recorded mainly by pupils. The third and most advanced prototype is the "Lecture Butler's E-Librarain Service"; it has a Web service interface to respect a service oriented architecture (SOA), and was developed in the context of the Web-University project at the Hasso-Plattner-Institute (HPI). Two major experiments in an educational environment - at the Lycée Technique Esch/Alzette in Luxembourg - were made to test the pertinence and reliability of our E-Librarian Service as a complement to traditional courses. The first experiment (in 2005) was made with CHESt in different classes, and covered a single lesson. The second experiment (in 2006) covered a period of 6 weeks of intensive use of MatES in one class. There was no classical mathematics lesson where the teacher gave explanations, but the students had to learn in an autonomous and exploratory way. They had to ask questions to the E-Librarian Service just the way they would if there was a human teacher.
1 Introduction 1.1 Project formulation 1.2 Our contribution 2 Pedagogical Aspect 4 2.1 Modern teaching 2.2 Our Contribution 2.2.1 Autonomous and exploratory learning 2.2.2 Human machine interaction 2.2.3 Short multimedia clips 3 Ontology Aspect 3.1 Ontology driven expert systems 3.2 Our contribution 3.2.1 Ontology language 3.2.2 Concept Taxonomy 3.2.3 Knowledge base annotation 3.2.4 Description Logics 4 Natural language approach 4.1 Natural language processing in computer science 4.2 Our contribution 4.2.1 Explored strategies 4.2.2 Word equivalence 4.2.3 Semantic interpretation 4.2.4 Various problems 5 Information Retrieval Aspect 5.1 Modern information retrieval 5.2 Our contribution 5.2.1 Semantic query generation 5.2.2 Semantic relatedness 6 Implementation 6.1 Prototypes 6.2 Semantic layer architecture 6.3 Development 7 Experiments 7.1 Description of the experiments 7.2 General characteristics of the three sessions, instructions and procedure 7.3 First Session 7.4 Second Session 7.5 Third Session 7.6 Discussion and conclusion 8 Conclusion and future work 8.1 Conclusion 8.2 Open questions A Description Logics B Probabilistic context-free grammars
Version Control Systems (VCS) allow developers to manage changes to software artifacts. Developers interact with VCSs through a variety of client programs, such as graphical front-ends or command line tools. It is desirable to use the same version control client program against different VCSs. Unfortunately, no established abstraction over VCS concepts exists. Instead, VCS client programs implement ad-hoc solutions to support interaction with multiple VCSs. This thesis presents Pur, an abstraction over version control concepts that allows building rich client programs that can interact with multiple VCSs. We provide an implementation of this abstraction and validate it by implementing a client application.
Algorithmic management
(2022)
Algorithmic management
(2022)
Boolean constraint solving technology has made tremendous progress over the last decade, leading to industrial-strength solvers, for example, in the areas of answer set programming (ASP), the constraint satisfaction problem (CSP), propositional satisfiability (SAT) and satisfiability of quantified Boolean formulas (QBF). However, in all these areas, there exist multiple solving strategies that work well on different applications; no strategy dominates all other strategies. Therefore, no individual solver shows robust state-of-the-art performance in all kinds of applications. Additionally, the question arises how to choose a well-performing solving strategy for a given application; this is a challenging question even for solver and domain experts. One way to address this issue is the use of portfolio solvers, that is, a set of different solvers or solver configurations. We present three new automatic portfolio methods: (i) automatic construction of parallel portfolio solvers (ACPP) via algorithm configuration,(ii) solving the $NP$-hard problem of finding effective algorithm schedules with Answer Set Programming (aspeed), and (iii) a flexible algorithm selection framework (claspfolio2) allowing for fair comparison of different selection approaches. All three methods show improved performance and robustness in comparison to individual solvers on heterogeneous instance sets from many different applications. Since parallel solvers are important to effectively solve hard problems on parallel computation systems (e.g., multi-core processors), we extend all three approaches to be effectively applicable in parallel settings. We conducted extensive experimental studies different instance sets from ASP, CSP, MAXSAT, Operation Research (OR), SAT and QBF that indicate an improvement in the state-of-the-art solving heterogeneous instance sets. Last but not least, from our experimental studies, we deduce practical advice regarding the question when to apply which of our methods.
Für die vorliegende Studie »Qualitative Untersuchung zur Akzeptanz des neuen Personalausweises und Erarbeitung von Vorschlägen zur Verbesserung der Usability der Software AusweisApp« arbeitete ein Innovationsteam mit Hilfe der Design Thinking Methode an der Aufgabenstellung »Wie können wir die AusweisApp für Nutzer intuitiv und verständlich gestalten?« Zunächst wurde die Akzeptanz des neuen Personalausweises getestet. Bürger wurden zu ihrem Wissensstand und ihren Erwartungen hinsichtlich des neuen Personalausweises befragt, darüber hinaus zur generellen Nutzung des neuen Personalausweises, der Nutzung der Online-Ausweisfunktion sowie der Usability der AusweisApp. Weiterhin wurden Nutzer bei der Verwendung der aktuellen AusweisApp beobachtet und anschließend befragt. Dies erlaubte einen tiefen Einblick in ihre Bedürfnisse. Die Ergebnisse aus der qualitativen Untersuchung wurden verwendet, um Verbesserungsvorschläge für die AusweisApp zu entwickeln, die den Bedürfnissen der Bürger entsprechen. Die Vorschläge zur Optimierung der AusweisApp wurden prototypisch umgesetzt und mit potentiellen Nutzern getestet. Die Tests haben gezeigt, dass die entwickelten Neuerungen den Bürgern den Zugang zur Nutzung der Online-Ausweisfunktion deutlich vereinfachen. Im Ergebnis konnte festgestellt werden, dass der Akzeptanzgrad des neuen Personalausweises stark divergiert. Die Einstellung der Befragten reichte von Skepsis bis hin zu Befürwortung. Der neue Personalausweis ist ein Thema, das den Bürger polarisiert. Im Rahmen der Nutzertests konnten zahlreiche Verbesserungspotenziale des bestehenden Service Designs sowohl rund um den neuen Personalausweis, als auch im Zusammenhang mit der verwendeten Software aufgedeckt werden. Während der Nutzertests, die sich an die Ideen- und Prototypenphase anschlossen, konnte das Innovtionsteam seine Vorschläge iterieren und auch verifizieren. Die ausgearbeiteten Vorschläge beziehen sich auf die AusweisApp. Die neuen Funktionen umfassen im Wesentlichen: · den direkten Zugang zu den Diensteanbietern, · umfangreiche Hilfestellungen (Tooltips, FAQ, Wizard, Video), · eine Verlaufsfunktion, · einen Beispieldienst, der die Online-Ausweisfunktion erfahrbar macht. Insbesondere gilt es, den Nutzern mit der neuen Version der AusweisApp Anwendungsfelder für ihren neuen Personalausweis und einen Mehrwert zu bieten. Die Ausarbeitung von weiteren Funktionen der AusweisApp kann dazu beitragen, dass der neue Personalausweis sein volles Potenzial entfalten kann.
Unique column combinations of a relational database table are sets of columns that contain only unique values. Discovering such combinations is a fundamental research problem and has many different data management and knowledge discovery applications. Existing discovery algorithms are either brute force or have a high memory load and can thus be applied only to small datasets or samples. In this paper, the wellknown GORDIAN algorithm and "Apriori-based" algorithms are compared and analyzed for further optimization. We greatly improve the Apriori algorithms through efficient candidate generation and statistics-based pruning methods. A hybrid solution HCAGORDIAN combines the advantages of GORDIAN and our new algorithm HCA, and it significantly outperforms all previous work in many situations.
In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver’s interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth.
In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that
allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth.
Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.
Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.
In recent years, the ever-growing amount of documents on the Web as well as in closed systems for private or business contexts led to a considerable increase of valuable textual information about topics, events, and entities. It is a truism that the majority of information (i.e., business-relevant data) is only available in unstructured textual form. The text mining research field comprises various practice areas that have the common goal of harvesting high-quality information from textual data. These information help addressing users' information needs.
In this thesis, we utilize the knowledge represented in user-generated content (UGC) originating from various social media services to improve text mining results. These social media platforms provide a plethora of information with varying focuses. In many cases, an essential feature of such platforms is to share relevant content with a peer group. Thus, the data exchanged in these communities tend to be focused on the interests of the user base. The popularity of social media services is growing continuously and the inherent knowledge is available to be utilized. We show that this knowledge can be used for three different tasks.
Initially, we demonstrate that when searching persons with ambiguous names, the information from Wikipedia can be bootstrapped to group web search results according to the individuals occurring in the documents. We introduce two models and different means to handle persons missing in the UGC source. We show that the proposed approaches outperform traditional algorithms for search result clustering. Secondly, we discuss how the categorization of texts according to continuously changing community-generated folksonomies helps users to identify new information related to their interests. We specifically target temporal changes in the UGC and show how they influence the quality of different tag recommendation approaches. Finally, we introduce an algorithm to attempt the entity linking problem, a necessity for harvesting entity knowledge from large text collections. The goal is the linkage of mentions within the documents with their real-world entities. A major focus lies on the efficient derivation of coherent links.
For each of the contributions, we provide a wide range of experiments on various text corpora as well as different sources of UGC.
The evaluation shows the added value that the usage of these sources provides and confirms the appropriateness of leveraging user-generated content to serve different information needs.
Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data’s full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.
Duplicate detection is the task of identifying all groups of records within a data set that represent the same real-world entity, respectively. This task is difficult, because (i) representations might differ slightly, so some similarity measure must be defined to compare pairs of records and (ii) data sets might have a high volume making a pair-wise comparison of all records infeasible. To tackle the second problem, many algorithms have been suggested that partition the data set and compare all record pairs only within each partition. One well-known such approach is the Sorted Neighborhood Method (SNM), which sorts the data according to some key and then advances a window over the data comparing only records that appear within the same window. We propose several variations of SNM that have in common a varying window size and advancement. The general intuition of such adaptive windows is that there might be regions of high similarity suggesting a larger window size and regions of lower similarity suggesting a smaller window size. We propose and thoroughly evaluate several adaption strategies, some of which are provably better than the original SNM in terms of efficiency (same results with fewer comparisons).
Adaption von Lernwegen in adaptierten Lehrmaterialien für Studierende mit Berufsausbildungsabschluss
(2023)
Obwohl immer mehr Menschen nicht direkt ein Studium aufnehmen, sondern zuvor eine berufliche Ausbildung absolvieren, werden die in der Ausbildung erworbenen Kompetenzen von den Hochschulen inhaltlich und didaktisch meist ignoriert. Ein Ansatz, diese Kompetenzen zu würdigen, ist die formale Anrechnung von mitgebrachten Kompetenzen als (für den Studienabschluss erforderliche) Leistungspunkte. Eine andere Variante ist der Einsatz von speziell für die Zielgruppe der Studierenden mit Vorkenntnissen adaptiertem Lehr-Lernmaterial. Um darüber hinaus individuelle Unterschiede zu berücksichtigen, erlaubt eine weitere Adaption individueller Lernpfade den Lernenden, genau die jeweils fehlenden Kompetenzen zu erwerben. In diesem Beitrag stellen wir die exemplarische Entwicklung derartigen Materials anhand des Kurses „Datenbanken“ für die Zielgruppe der Studierenden mit einer abgeschlossenen Ausbildung zum Fachinformatiker bzw. zur Fachinformatikerin vor.