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Our work finds the fine grained edits in context of neighbouring tokens in Wikipedia articles. We cluster those edits according to similar neighbouring context. We encode neighbouring context into vector space using word vectors. We evaluate clusters returned by our algorithm on extrinsic and intrinsic metric and compare it with previous work. We analyse the relation between extrinsic and intrinsic measurements of fine grained edit tokens.
Most social media platforms allow users to freely express their opinions, feelings, and beliefs. However, in recent years the growing propagation of hate speech, offensive language, racism and sexism on the social media outlets have drawn attention from individuals, companies, and researchers. Today, sexism both online and offline with different forms, including blatant, covert, and subtle lan- guage, is a common phenomenon in society. A notable amount of work has been done over identifying sexist content and computationally detecting sexism which exists online. Although previous efforts have mostly used peoples’ activities on social media platforms such as Twitter as a public and helpful source for collecting data, they neglect the fact that the method of gathering sexist tweets could be biased towards the initial search terms. Moreover, some forms of sexism could be missed since some tweets which contain offensive language could be misclassified as hate speech. Further, in existing hate speech corpora, sexist tweets mostly express hostile sexism, and to some degree, the other forms of sexism which also appear online was disregarded. Besides, the creation of labeled datasets with manual exertion, relying on users to report offensive comments with a tremendous effort by human annotators is not only a costly and time-consuming process, but it also raises the risk of involving discrimination under biased judgment.
This thesis generates a novel sexist and non-sexist dataset which is constructed via "UnSexistifyIt", an online web-based game that incentivizes the players to make minimal modifications to a sexist statement with the goal of turning it into a non-sexist statement and convincing other players that the modified statement is non-sexist. The game applies the methodology of "Game With A Purpose" to generate data as a side-effect of playing the game and also employs the gamification and crowdsourcing techniques to enhance non-game contexts. When voluntary participants play the game, they help to produce non-sexist statements which can reduce the cost of generating new corpus. This work explores how diverse individual beliefs concerning sexism are. Further, the result of this work highlights the impact of various linguistic features and content attributes regarding sexist language detection. Finally, this thesis could help to expand our understanding regarding the syntactic and semantic structure of sexist and non-sexist content and also provides insights to build a probabilistic classifier for single sentences into sexist or non-sexist classes and lastly find a potential ground truth for such a classifier.
Der vorliegenden Arbeit liegt die Frage zugrunde in wie weit die zunehmende Digitalisierung die Work-Life Balance beeinflusst, was auch gleichzeitig das Ziel dieser Arbeit beschreibt.
Dazu wird eine Literaturrecherche durchgeführt, in der eine direkte Korrelation vom subjektiven Work-Life Balance Empfinden mit dem empfundenen Stresslevel hergestellt wird. Mit Hilfe von Antonovskys Salutogenetischen Modell (1997) aus dem Bereich der Stressforschung werden Faktoren ermittelt, welche das individuelle Stressempfinden - und somit die empfundene Work-Life Balance - beeinflussen. Es wird ein Interviewleitfaden für ein problemzentriertes Interview nach Witzel (1985) erstellt, welches als Hauptuntersuchungsgrundlage die Beeinflussung der ermittelten Faktoren durch die Digitalisierung beinhaltet, welche anhand einer qualitativen Inhaltsanalyse nach Mayring (2014) ausgewertet werden.
Demnach hat die Digitalisierung Einfluss auf jeden dieser ermittelten Faktoren und damit zusammenhängend auf die Work-Life Balance. Dies zeigt, dass uns die Digitalisierung in fast allen Bereichen unseres beruflichen oder privaten Lebens beeinflusst. Ob dieser Einfluss positiv oder negativ ist, hängt von dem Individuum ab, welches diesen Faktor bewertet. Es konnten klare Unterscheidungen zwischen Personen festgestellt werden, die in einem IT-Beruf arbeiten und solche die dies nicht tun. Auf die Personen im IT-Beruf hatte die Digitalisierung einen deutlich besseren Einfluss auf die Work-Life Balance als auf solche, die nicht in solch einem Beruf arbeiten.
Geschäftsregeln sind zu einem wichtigen Instrument geworden, um die Einhaltung der Vorschriften in ihren Geschäftsprozessen zu gewährleisten. Aber die Sammlung dieser Geschäftsregeln kann verschiedene widersprüchliche Elemente beinhalten. Dies kann zu einer Verletzung der zu erreichenden Compliance führen. Diese widersprüchlichen Elemente sind daher eine Art Inkonsistenzen oder Quasi-Inkonsistenzen in der Geschäftsregelbasis. Ziel dieser Arbeit ist es, zu untersuchen, wie diese Quasi-Inkonsistenzen in Geschäftsregeln erkannt und analysiert werden können. Zu diesem Zweck entwickeln wir eine umfassende Bibliothek, die es ermöglicht, Ergebnisse aus dem wissenschaftlichen Bereich der Inkonsistenzmessung auf Geschäftsregelformalismen anzuwenden, die tatsächlich in der Praxis verwendet werden.
In dieser Arbeit wird eine Unterrichtsreihe beschrieben, welche aus den drei Bereichen „mathematische Relationen“, „Datenbanken in Sozialen Netzwerken“ und „Datenschutz“ zusammengesetzt ist. Zu jedem Bereich wird ein eigener Unterrichtsentwurf präsentiert.
Außerdem wurde im Rahmen der vorliegenden Arbeit ein Programm zur Visualisierung der Relationen des Sozialen Netzwerks Instahub entworfen, welches im Anschluss an die Beschreibung der Unterrichtsreihe aufgeführt wird.
Data visualization is an effective way to explore data. It helps people to get a valuable insight of the data by placing it in a visual context. However, choosing a good chart without prior knowledge in the area is not a trivial job. Users have to manually explore all possible visualizations and decide upon ones that reflect relevant and desired trend in the data, are insightful and easy to decode, have a clear focus and appealing appearance. To address these challenges we developed a Tool for Automatic Generation of Good viSualizations using Scoring (TAG²S²). The approach tackles the problem of identifying an appropriate metric for judging visualizations as good or bad. It consists of two modules: visualization detection: given a data-set it creates a list of combination of data attributes for scoring and visualization ranking: scores each chart and decides which ones are good or bad. For the later, an utility metric of ten criteria was developed and each visualization detected in the first module is evaluated on these criteria. Only those visualizations that received enough scores are then presented to the user. Additionally to these data parameters, the tool considers user perception regarding the choice of visual encoding when selecting a visualization. To evaluate the utility of the metric and the importance of each criteria, test cases were developed, executed and the results presented.
As a result of the technical progress, processes have to be adjusted. On the one hand, the digital transformation is absolutely necessary for every organization to operate efficient and sustainable, on the other hand whose accomplishment is a tremendous challenge. The huge amount of personal data, which accrue in this context, is an additional difficulty.
Against the background of the General Data Protection Regulation (GDPR), this thesis focuses on process management and ways of optimizing processes in a Human Resources Department. Beside the analysis of already existing structures and workflows, data management and especially the handling of personal data in an application process are examined. Both topics, the process management and the data protection are vitally important by itself, but it is necessary to implement the requirements of data protection within the appropriate position of a corresponding process. Relating to this, the thesis deals with the research question of what barriers may occur by a sustainable process integration and to which extend the GDPR prevent an unobstructed workflow within the Human Resources Department of the Handwerkskammer Koblenz. Additionally, answering the question of which subprocesses are convenient for a process automation is highly significant.
In scope of these questions Business Process Management is the solution. By means of the graphical representation standard, Business Process Model and Notation, a process model with the relevant activities, documents and responsibilities of the recruitment process is designed. Based on a target-actual comparison it becomes apparent, that standardized process steps with less exceptions and a large amount of information are basically convenient for automation respectively partial automation. After the different phases of the recruitment process are documented in detail, a Workflow-Management-System can ex-port the transformed models, so the involved employees just have to carry out a task list with assigned exercises. Against the background of the data protection regulations, access rights and maturities can be determined. Subsequently only authorized employees have admission to the personal data of applicants. Because of impending sanctions by violation against the GDPR, the implementation of the relevant legal foundations within the recruitment process is necessary and appropriate. Relating to the defined research questions, it appears that in principle not every activity is appropriate for a process automation. Especially unpredictable and on a wide range of factors depending subprocesses are unsuitable. Additionally, media discontinuities and redundant data input are obstacles to an enduring process integration. Nevertheless, a coherent consideration of the topics of business process management and the data protection regulations is required.