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Institute
Due to anthropogenic climate change, cities need to be restructured. New measures are necessary to achieve climate neutral cities. With the assistance of 3D Programs, concepts against flooding, heat and in favor of mobility transition can be visualized.
This thesis will emphasize why it is so relevant to take actions against climate change and will investigate which measures are helpful, especially in cities. In connection, the ideation of measures for the City of Xanten will be presented. Subsequently, the process of chosen Design Style and concepts for various Xanten locations will be explained and displayed. These elaborated concepts can be seen as a thought provoking impulse for at least Xanten, which may be realized in the future.
In dieser Bachelorarbeit wird anhand von Beispielen die Produktion von Audiodateien gezeigt, und wie diese beispielhaft in ein Videospiel eingefügt werden können. Hierzu werden zunächst die verschiedenen Programmtypen präsentiert, mit denen Audiodateien produziert werden können. Um einen möglichst geringen Kostenaufwand zu zeigen, werden Programme benutzt, die kostenlos oder kostengünstig erworben werden können. Danach wird an Beispielen gezeigt, wie mit den ausgewählten Programmen Stimmen, Geräusche und Musik aufgenommen und bearbeitet werden können. Abschließend wird an einer Spiele-Engine vorgeführt, wie die erstellten Audiodateien ins Spiel eingefügt und einprogrammiert werden. Durch diese Bachelorarbeit sollen vor allem Neueinsteiger (und Studenten) an Audio und Sound Design herangeführt werden, sodass sie mit den praktischen Unterweisungen in dieser Arbeit selbst Audiodateien produzieren können.
Vegetation monitoring is an important tool in a variety of fields, e.g. precision agriculture and plants’ phenotyping. The system design of existing non-invasive ground based systems is usually not open-source. Therefore, these systems cannot be reliably modified and thus are not suitable to advanced applications, such as prototyping of new sensors. This thesis describes the development of a ground based hyperspectral monitoring system for use in plant science and sensor testing. A well-documented and automated working prototype has been developed and initial tests show promising results. Further work in system modularity and redundancy is needed.
The development of technologies that created FinTechs have brought new possibilities for customers into life that provide a quick and convenient handling of operations by covering various segments of the financial industry. However, the innovations also implicate drawbacks: Traditional banks may be threatened due to a loss of customers. This thesis evaluates to which extent this case can be measured by executing quantitative research methods as well as qualitative research methods for the purpose of gaining information from a bank’s perspective as well as opinions from customers. The result of this empirical study is that there is a certain correlation between the development of FinTechs and a threat caused by FinTechs although the latter is rather weak. Furthermore, this thesis indicates the knowledge of customers regarding FinTechs and their willingness to face said innovations.
This master thesis presents a machine learning approach using the distributed data- processing framework Apache Spark and the programming language R. The aim is to predict the time series of the Appl Percentage (ApplPerc) from the workload manager of the z/OS mainframe system using SMF 72.3 and SMF 70.1 records as input. System Management Facility (SMF) data are binary log files that are used to collect system performance data and information about system behaviour. The SMFs show how Apache Spark can be used for the pre-processing. This includes data collection, data extraction, and data selection. The machine learning algorithms are implemented in R. The input SMF datasets will be split into training and test data, and thereby applied to different machine learning and deep learning models such as random forest regression, recurrent neural network, and k-nearest neighbor regression to predict the ApplPerc. The validation of the prediction models will be proved by using cross-validation techniques to evaluate the best applied parameters for each model and therefore to locate the model with the best performance. The process of data analysis is followed by a data-mining methodology called Cross-Industry Standard Process for Data Mining (CRISP-DM), which outlines the steps involved in performing the analysis.
“Right now, Bitcoin feels like the Internet before the browser” (Casares, 2020). This quote sums up most of the current thoughts on the phenomenon of a ground-breaking invention from 2008 by Satoshi Nakamoto since a major part of the world’s population is still missing out on sophisticated knowledge about it. Especially in times of digitalization, it is of enormous value to be informed about current developments in technology. This thesis aims to evaluate the future development of money, especially if Bitcoin has the potential to totally replace conventional currencies. Furthermore, the strengths and shortcomings of Bitcoin will be discussed. It also reaches to determine characteristics for a currency’s long-term success and which of those characteristics are fulfilled by Bitcoin. To answer the research question if Bitcoin has the potential to be an alternative currency and to test the developed hypotheses, pertinent literature is reviewed and an empirical study consisting of expert interviews which are examined using a qualitative structured content analysis method, are conducted. Afterwards, the results are critically discussed. The results of this thesis reveal that Bitcoin has the potential to become an alternative currency because it fulfills most of the criteria a currency needs to meet to be successful in the long run. Furthermore, it is evident that Bitcoin still faces vast barriers and needs to overcome enormous obstacles, which shifts the time horizon for becoming a success somewhere in the farther future. Additionally, apart from the development of cryptocurrencies, it is shown that money in general, will flow much more digitally in the short run. Besides that, this thesis offers insights about the promising future of Bitcoin’s underlying core technology, the Blockchain, in various fields of application.
Linux is an operating system, which is used by a limited number of people. The main idea of this project is to develop a game that not only simulates Linux, but also provides an opportunity to learn various things about the use of this operating system.
The game, LinuxSimulationGame, is built with Unity and designed by following the requirements of a focus group. While in development, game tests are used to investigate the current state and quality of the game. Game tests provide feedback to the developer, which can be used to plan further changes. By using mechanics like space, objects, actions, rules and skills the game produces various situations in which a user is able to learn. By memorizing information based on a Linux operating system.
Both game tests and interviews have proved an interest in this kind of gameplay. LinuxSimulationGame is a game which could result in an increase of knowledge. This would require further testing. This document describes mechanics and data needed for creation of games like LinuxSimulationGame.
Natural gas is an indispensable source of energy for industrial and domestic purposes, hence distribution and trade demands competition. In gas distribution, optimizing is a crucial task involving control decision on the network elements with respect to trader’s demand at the boundary nodes. The application of artificial intelligence is becoming popular in diverse domains, making its role in gas transport networks more and more interesting. The opposite interest of the players in the network is the reason behind the thought of establishing a game analogy here. The agents as players act on the basis of the opponent’s action. Since both agents perform actions from different space, a neural network model is designed for the dispatcher agent to learn to make decisions for active elements based on its experience from the sample gas transport network. Behavior of the network and its elements is depicted using an optimized gas network simulator. The representation of the gas transport network and its state is digitally transformed so that it is able to extract the features by an artificial neural network representing the environment and its rules. With the help of the trained neural network, the dispatcher agent decides the control decisions with less accumulated penalties. The results and findings of the proposed method are subjected to a comparison with the interval halving method. The results of the study will open the door for further research possibilities.
Visualisierung von Kulturgütern am Beispiel der römischen Kaiserthermen in Trier als 3D Modell.
(2022)
This paper is a summary of the “Kaiserthermen Project'' for the “Landesmuseum Trier” and shows in detail how to convert a 3Ds Max 3D model for usage into an open source program like Blender. It tries to explain the basics of 3D modelling and compares it to the 3D modelling of cultural heritages. It should work as a form of manual to further convert different models into the FBX file format.
It also aims to explain the high value of cultural heritage as a 3D model and compares it to the 3D model of the “Trier Kaiserthermen”. It furthermore gives examples and reasons for the usage of 3D models in different fields.
It also presents the final 3D renderings and shows how they were created. At the end it explains how the perfect model for scientific research should be and how this project and paper improved this for the “Kaiserthermen”.
Seit 2009 das erste Mal der Bitcoin als Open-Source-Software veröffentlicht wurde, sind neue Kryptowährungen stetig dazu gekommen und weiterentwickelt worden. Einige Jahre später folgten Währungen wie Litecoin, Ripple und Ethereum. Inzwischen sind Kryptowährungen im Großteil der Welt legal und gelten in einigen Ländern bereits als offizielles Zahlungsmittel. Betrachtet man die weitere Entwicklung, stellt sich die Frage, ob Kryptowährungen eine funktionierende Alternative zu herkömmlichen Währungen bieten können, und ob diese unter aktuellen ökologischen Bedingungen tragbar wäre.
Ziel ist es, die Frage zu beantworten, welche Kriterien Kryptowährungen erfüllen müssten, um weiterhin einen hohen Stellenwert einzunehmen. Es wird eine Literaturrecherche durchgeführt, welche die Funktionsweise und Problematik verschiedener Mining-Konzepte erklärt, und ökologische Daten analysiert. Dabei zeigt sich, dass es Ansätze zu ökologischen Kryptowährungen gibt, die noch in der Entwicklung sind. Kryptowährungen, die diese Aspekte nicht berücksichtigen, werden
langfristig als Alternative ökologisch nicht tragbar sein.
Künstliche Intelligenz in Form von neuronalen Netzen wird in der öffentlichen Verwaltung bislang wenig eingesetzt. Im Zuge der Digitalisierung im Bereich der öffentlichen Verwaltung müssen bestehende Datensätze, welche nicht- oder schwer maschinenlesbar sind, über offene Schnittstellen und Datenstandards bereitgestellt werden. Schätzungsweise 80 % der erfassten Daten sind in der öffentlichen Verwaltung nicht- oder schwer maschinenlesbar. In dieser Arbeit soll geprüft werden, ob ein künstliches neuronales Netz, welches auf der ImageNet Datenbank basiert und durch weitere Beispiele trainiert worden ist, bestehende (nicht-maschinenlesbare) Datensätze in Tabellenform aus dem Bereich der Geoinformatik erkennen und in einer passenden Datei mit offenem Datenstandard (teil-)automatisiert speichern kann, um diese für den Einsatz in einer offenen urbanen Datenplattform vorzubereiten. Zudem werden Optimierungsvorschläge für eine Verbesserung des neuronalen Netzes genannt und umgesetzt. Im Laufe der Arbeit ergibt sich, dass das trainierte Modell für den praktischen Einsatz nicht ausreichend genau funktioniert, aber eine gute Basis für weitere Optimierungen liefert, etwa durch eine Vergrößerung des Trainingsdatensatzes, welche die Anforderung einer Steigerung der Rechenleistung voraussetzt.
Die kontinuierliche Digitalisierung der Arbeitswelt führt zu immer schnelllebigeren und komplexeren Arbeitsumfeldern, welche auf der effektiven Nutzung von Informations- und Kommunikationstechnologien (IKT) beruhen. Arbeitsfelder transformieren und verdichten sich mit rasanter Geschwindigkeit. Parallel steigt das durchschnittliche Alter der arbeitnehmenden Population und die Arbeitszufriedenheit sinkt. Besonders betroffen sind Erwachsene gehobenen Alters. Da die Arbeitszufriedenheit als starker Prädiktor für die physische- und psychische Gesundheit der Arbeitnehmenden wirkt, ist es notwendig zu verstehen, welche Konstrukte auf die Arbeitszufriedenheit wirken. Die vorliegende Studie betrachtet daher die mediierende Wirkung der digitalen Selbstwirksamkeit auf die Beziehung zwischen Alter und Arbeitszufriedenheit. Es wurde eine online-Befragung mit 124
Arbeitnehmenden im Alter von 17-63 Jahren durchgeführt. Es wurde erwartet, dass steigendes Alter in einer negativen Beziehung zu Arbeitszufriedenheit und digitaler Selbstwirksamkeit steht. Zusätzlich wurde angenommen, dass digitale Selbstwirksamkeit positiv auf Arbeitszufriedenheit wirkt. Es wurde eine indirekte Mediation, mit einer negativen Wirkung von Alter auf Digitale Selbstwirksamkeit und einer positiven Wirkung von digitaler Selbstwirksamkeit auf Arbeitszufriedenheit identifiziert. Die Wirkung der Geschlechtsidentifikation als Störvariable konnte ausgeschlossen werden. Praktische und theoretische Implikationen werden diskutiert.
Household appliances such as ovens and washing machines are essential in every household, to make it safe for potential users, the machine must be tested under extreme conditions to prove that even in case of malfunction no danger is posed, such tests are carried out after successfully building a system that is stable in essence. This research aims to develop a smart gas burning system built on reliable and secure communication channels bounded with the implemented protocol specifications. A close study of each protocol specifications as well as a bit-by-bit investigation is therefore performed, moreover, a full timing analysis of communicated data is also carried out to ensure validity and integrity of the data transmitted. It was found that logic analysis plays a very important role in constructing data frames that are accurate to the smallest time unit possible, such analysis was not possible using an old model oscilloscope, after successful construction, transmitting a predefined sequence of data successfully initiated the communication channel between all system components. With the implemented safety and security features, the system is safe to use, however, this does not make it ready for end users, this is merely a proof of concept that such system can be made smart. The results show that the microcontroller module implemented is very capable of giving the user the ability to control the system manually with attached knobs and wirelessly through the Access point server.
The modern work environment is subject to constant change with a notable shift from individual to team-based work structures. This transition necessitates an improved un-derstanding of how managerial actions influence the dynamics within these teams, to optimally align them for success. Despite the substantial research about leader effec-tiveness and team effectiveness based on organizational performance indicators, it is rather infrequent that the direct influence of leader behavior on teams is explored. This thesis aims to address this gap by answering how leader behaviors impact team pro-cesses in the context of German medium to large-scale IT-enterprises. Using a quanti-tative cross-sectional methodology, individuals working in team-based structures under direct supervision were surveyed. Analyzing data from 94 respondents via hierarchical linear regression analysis, several significant relationships were identified. In general, leader behavior has a strong positive impact on team processes. Furthermore, task-oriented leaders have the greatest positive influence on action processes, while relation-oriented leaders have the greatest positive influence on interpersonal processes. Change-oriented leaders also significantly influence action processes and interpersonal processes, albeit less strongly than their counterparts. These results provide implications for team management practices, emphasizing the importance of leader behavior for aligning team processes in the direction of success.
This thesis deals with the consumer purchasing behavior in the fast fashion segment. The central focus includes analyzing factors such as purchase intention, e-commerce trade, consequences and environmental impacts. The first section defines terms and compares current studies to each other. This is followed by a methodical discussion. In order to assess consumer purchasing behavior, semi-structured interviews were conducted, and digital diary entries were written by participants from generation Y and Z. Based on these results shopper types were identified.
Floods are the most common, devastating, and frequently occurring natural disaster nowadays.
Because of climate change, Europe is expected to see an even higher number of floods in the coming decade. As floods cannot be prevented, understanding the pattern and causes and being able to forecast could significantly reduce the losses during the flood. In July 2021, Germany faced a catastrophic flood, taking more than 180 people’s lives and causing around 40 billion euros of economic loss. To study the flooded region and the region at high risk, the Flood event 2021 has been visualized in this research. The Flood map has been created using a sentinel-1 image. In the visualization, floods in the Rhine River and Arh River can be seen, and the change in the pattern of flood from 12th July to 16th July has also been observed.
Different models exist that try to predict floods and warn as early as possible. Since Machine learning is popular nowadays in every sector, ML models have been implemented in this study to forecast the flood. The SARIMA, Random Forest, and LSTM have been implemented using historical data to predict the flood. The Random Forest model performed better than the remaining two models. The Isolation Forest model has also been implemented to classify the data into Flood and No Flood. This model performs well with the Flood classes but struggles in capturing the No Flood class.
API Driven Form Rendering
(2023)
This master’s thesis investigates the challenges and opportunities associated with dynamic form rendering in user interfaces, specifically in the context of Galeria Karstadt Kaufhof (GKK), a key player in the German retail sector. Collaborating with CODUCT Solutions GmbH, this research aims to enhance GKK’s operational efficiency and user experience in their digital transformation journey, focusing on Project Baldeney, a cloud-native platform that automates content management workflows.
The thesis begins by outlining the limitations of traditional, static methods of form rendering, highlighting the implications for development time and error frequency. It identifies the fragmentation in the existing API-driven architectures as a critical issue affecting system reliability and user experience. Against this backdrop, the work proposes a unified, API-driven framework to dynamically render forms for many user actions, such as data input and transactions.
The research takes a deep dive into existing systems—particularly Informatica’s Product Information Management (PIM) software and the new software in development Article Workbench to identify user experience issues, inefficiencies, and the complexities involved.
Through this examination, it offers a targeted solution to integrate various components of form management, like building, parsing, validation, and rendering, into a cohesive system.
The thesis aims to contribute to software development and user experience design substantially, by targeting improved development efficiency, reduced errors, and enhanced system reliability. By achieving these objectives, the research aspires to furnish GKK with
a robust solution for dynamic form rendering, thereby enhancing both user satisfaction and system performance in the fast-evolving digital landscape.
During the past years, it is noticeable that the e-commerce industry has emerged drastically, offering accessibility to a variety of products to the customer where they can buy products from the comfort of their home. However, it is associated with a lot of new challenges for e-commerce businesses, particularly in understanding and managing the customer behavior patterns with the rising online product returns. Since the accessibility of online shopping has risen, assessing the critical factors related to product return and prediction has become a really challenging task for e-commerce vendors. This study also aims to segment the customers based on customer behavior prediction into two categories, i.e., high return risk and low return risk customers, and further develop strategies to reduce the online returns.
This study is rather structured into four integral parts, where each part provides the comprehensive analysis. The first aspect involves analyzing and identifying the customer behavior patterns leading to product returns. The second aspect predicts the online product return based on selected features and the third is to segment the customers based on online product return prediction and categorize them into high return risk, and low return risk customers. The final step involves development of strategies to reduce the product return based on the intense analysis conducted.
To achieve this meaningful research outcome, data analysis is conducted to understand the customer behavior patterns, and a Random Forest feature selector is used to identify the customer behavior patterns that lead to product return. Based on the identified features, classification models were applied to classify and predict whether the customer is going to return product or not. Furthermore, in these seven classification models such as Logistic Regression, Ada Boost, Decision Tree, Naive Bayes, XG Boost, K-Nearest Neighbors, and Random Forest were implemented and used to compare the performance of the classification models to find out the best performing model. Lastly, the segmentation of customers is carried out based on the online product prediction using Logistic regression with classification threshold method into high return risk and low return risk customer categories.
The results obtained help in understanding the customer behavior and reduce the online product return by developing the strategies. This study will eventually help the online businesses in reducing returns which will also enhance the customer satisfaction.
Ziel der vorliegenden Abschlussarbeit war es, ausgewählte Multibankingapps anhand des von Yu-Kai Chou entworfenen Gamification Frameworks Octalysis (Y.-k. Chou, 2019) zu analysie- ren, um daraufhin eine Aussage über die Anwendbarkeit des Frameworks auf Multibankingapps treffen zu können. Mithilfe des Design Pattern Canvas (Žavcer u. a., 2015) wurde ein Arte- fakt geschaffen, welches Entwicklern solcher Apps erlaubt, die im Framework beschriebenen Core Drives auf die Anwendung abzustimmen. Die Anwendbarkeit des Frameworks wurde zwar durch die Analyse bestätigt, jedoch ergaben sich weitere Abhängigkeiten, welche grundlegend für eine erfolgreiche Anwendung sind. Darunter zählt beispielsweise die Offenheit der Nutzer gegenüber Finanzthemen.
News is one type of information that has the potential to influence a large number of people. People have received news since the beginning of time through various birds, short letters, and other means. When newspapers were invented, information was available everywhere on paper. The news is no longer limited to paper-based platforms, thanks to the digitalization of online platforms. The online news platform is now available to read the news in a matter of seconds. As a result, news can easily connect people, and it is being used to spread fake news. Fake news is spread to gain attention for the wrong reasons. Different languages are used in our world to express our thoughts and feelings. There are specific materials for each language. The English language is the most studied topic when it comes to identifying fake news. Data and research resources are few in other languages, hence there is little research done. Of these, Bengali is one of the most widely spoken. Our ultimate goal is to create a tree that contains elements of both English and Bengali. Research on fake news and web scraping was used to get the language news data. Multilingual transformer models m-BERT and xlm-ROBERTa with long text or tokens are used to detect fake news (512 tokens or any token size). The two models were compared using two different datasets (with stop words and the other without) using three different fine-tuning freeze approaches (Freeze, No Freeze, and Freeze Embed). The results show that the dataset with stop words had a somewhat better performance than the dataset omitting stop words. The xlm-RoBERTa model outperforms the m-BERT model in terms of F1-score and accuracy.
Since the last federal election in germany, the CDU is no longer the strongest force and suffered large losses of votes. The SPD, Bündnis 90/Die Grünen and the FDP now make up the german parliament. Parts of the election campaign are taking place via social media. But how do the individual parties use modern media? Do the parties differ in the way they communicate their messages and, as a result, do they generate votes in diffe-rent ways? I will examine these questions in more detail in this bachelor's thesis, starting with the reunification of germany and including the last federal elections. The social me-dia platform I am investigating for political communication is twitter.
I presented the past and current parliamentary election results and differences in eastern and western germany. To do this, I used sentiment analysis to examine tweets extracted from twitter at the federal and local/state political level, for their content and the accom-panying reactions. I created a detailed representation of the different social media appearances of politicians and determined whether appropriate conclusions can be drawn based on my results and data.
My analysis shows that at the parliamentary level, Annalena Baerbock of Bündnis 90/Die Grünen, followed by Dr. Alice Weidel of the AfD and Christian Lindner of the FDP, have the most likes and retweets. Olaf Scholz, with 826 tweets, is the most active on twitter in 2021. At federal and local/state level, the topics of all parties are often similar. There are differences with regard to the core issues of the respective parties. I have illustrated this in each case.
Based on the politicians' use of twitter, I could not draw any concrete conclusions about voter behaviour. For this it would be important to evaluate other media, e.g. facebook.
Die Entwicklungen des maschinellen Lernens und damit einhergehend auch der künstlichen Intelligenz umfassen immer mehr Anwendungsfälle im täglichen Leben. Je komplexer und autonomer diese Systeme werden, desto schwieriger lässt sich ihr Verhalten prognostizieren und es ist nicht auszuschließen, dass Situationen entstehen, die ethische und rechtliche Fragen aufwerfen. Das Ziel dieser Arbeit ist die Untersuchung der erforderlichen Rahmenbedingungen für den Umgang mit diesen Technologien. Neben der Erklärung wesentlicher Begriffe aus dem Bereich der KI und Ethik werden durch die Recherche geeigneter Literatur über Pflegeroboter und dem autonomen Fahren zwei Anwendungsbereiche vorgestellt, die einen Bedarf an moralischen Maschinen aufweisen. Maßnahmen zur Regulierung innerhalb der Europäischen Union erfolgen durch das Anwenden bestehender und Erlassen neuer Gesetze sowie über die Definition ethischer Leitlinien, die ebenfalls als Teil der vorliegenden Arbeit behandelt werden.
Sign language is an important factor in the integration of deaf and hard of hearing people into society. Due to the limited number of people who speak sign language, there is a communication barrier that needs to be addressed. In recent years, sign language has been an important field of research. There have been numerous attempts at trying to find a solution to this problem. This thesis focuses on the German finger alphabet, which has not been researched as much. It examines whether it is possible to recognize the gestures of the German finger alphabet in real time with a convolutional neural network. For that, literature is consulted, and a prototype is developed. The prototype includes a newly created dataset with 2,500 images distributed over 25 gestures, a convolutional neural network, and software for real-time translation of a video stream. The prototype demonstrates the feasibility of realizing such a task. The convolutional neural network achieves an accuracy of 99.61%. On a powerful desktop computer, the prediction of a single frame takes between 36 and 40 ms. However, there are some limitations and restrictions to the quality of prediction caused by factors such as lighting and background. Additionally, certain similar gestures, such as M and N, are difficult to distinguish. Based on these results, ideas and suggestions for future studies are presented.
The popularity of using transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) for various Natural Language Processing (NLP) tasks is increasing rapidly. Unfortunately, the research is very limited for low-resource languages like Nepali. This study examines the utilisation of pre-trained BERT-based NERNepal for Named Entity Recognition (NER) tasks in Nepali text. The main goal is to investigate the efficiency of the NERNepal model, which has been pre-trained and fine-tuned on Nepali corpus data for NER. This research provides new insights by evaluating the NERNepal model on two distinct datasets. It addresses unique linguistic challenges specific to Nepali. The study also offers a detailed analysis of the model's strengths and weaknesses. By focusing on diverse datasets, this study shows how adaptable the model is and how its performance varies. These aspects have not been explored extensively before. The EverestNER dataset is one of the largest human-annotated datasets in Nepal so far, and the Nepali_NER dataset is also BIO-annotated for the NER task, which helped to compare the model’s prediction. The performance was better on the Nepali_NER dataset in comparison with another selected dataset. The EverestNER dataset contained many complex words for ORG connected with many tokens for a single entity with different contextual meanings and different annotations for the same word in different tokens as per context. Because of this, it created more confusion for the prediction, especially for the ORG entity. It had a similar issue with another dataset as well, but the label annotation was better in comparison. Furthermore, the research tries to clarify the difficulties and constraints related to utilising pre-trained BERT models for NER in low-resource languages such as Nepali. The study focuses on research areas on the efficacy of the model and its performance on two different datasets. Post-training with the EverestNER train dataset was attempted, but due to computational resource limitations, only a maximum of 3 epochs was possible, which did not improve the evaluation. It also has implications for enhancing language processing tools for Nepali. The results show the ability of a pre-trained BERT-based model to improve NER skills for Nepali text. However, further study is required to overcome the current obstacles to identifying complex words. The availability of high computational resources and the possibility of combining other NER approaches with a transformer-based model could increase the performance and robustness of the model.
Keywords: Named Entity Recognition, Natural Language Processing, BERT, low resources language
Despite the remarkable achievements of large transformer-based pre-trained models like BERT, GPT in several Natural Language Processing (NLP) tasks including Sentiment Analysis (SA), challenges are still present for subdued source languages like Nepali. Nepali language is written in Devanagari script, has complex grammatical structure and diverse linguistic features. Due to the absence of balanced datasets, and computational resources for Nepali, achieving optimal result with the latest architecture remains challenging. For this reason, publicly available NLP modelsfor Nepali are very less, making research in this area difficult. This paper attempts to addressthis gap through the use of pre-trained transformer models specially tailored for Nepali from Hugging Face including BERT, DistilBERT, ALBERT, and DeBERTa for sentiment analysis in Nepali tweets on relatively balanced datasets. The models are trained on large Nepali datasets and optimized for NLP tasks involving Devanagari scripts. To evaluate the model’s performance, various tokenization strategies are investigated in order to capitalize on transformer-based embedding with the SoftMax function and confusion matrix. The outcomes of models are compared using the same datasets. The study’s results shows that DistilBERT achieved the highest accuracy rate of 88% in Nepali sentiment analysis tasks, followed by BERT and DeBERTa at 83% and 80%, respectively. However, ALBERT showed a low accuracy of 70%. The result of this approach shares valuable viewpoints for the field of sentiment analysis in diverse linguistic contexts.
The home office has become established in the offices of numerous companies and administrations since the events of the global COVID-19 pandemic and has posed numerous challenges. Particularly in Germany, administrative authorities were confronted with the challenge of quickly responding to the events of the pandemic and taking appropriate measures to curb the spread of the virus in the offices of administrations.
This paper examines the use of the home office in administration during the pandemic by combining quantitative and qualitative methods such as statistical analyses, interviews, and case studies. In doing so, it sheds light on the experiences of employees as well as the challenges and potentials of home office use. The context aims to demonstrate that the investigation of home office use in administration during the pandemic was crucial in understanding the effects of the newly designed work practices on administrative organizations to better shape future work models.
The aim of this study is to gain insights that contribute to the further development of the home office concept in administration. This includes evaluating the long-term effects on work efficiency, work-life balance, and corporate culture. These insights contribute to assessing the effectiveness and efficiency of the home office in administration and lay the groundwork for how optimized work arrangements can be created. They also provide valuable insight into the adaptability of administrative structures to continuously changing work environments.
The results of this study will help develop practical solutions and optimize the implementation of the home office in administration. The findings lay new foundations for future research that could address further aspects of home office use in administration, such as long-term effects on organizational performance and employee satisfaction, as well as the development of guidelines for effective implementation of hybrid work models.
Nowadays, a loyalty program is one of the most significant marketing tools for winning genuine customer loyalty. Valeo, a global automotive supplier, also offers the Valeo Specialist Club loyalty program and rewards garage or workshop members who only purchase Valeo spare parts. The core intent of this study is to understand the importance of the Valeo loyalty application, members’ insights, and their experiences for improving the future planning, aftermarket performance, and management of the Value Specialist Club. In today’s competitive market, products, services, and customer loyalty are valuable assets, so the program was created to enhance member satisfaction and loyalty with the Value Specialist Club. A mixed-method approach was used to answer the study question and findings, improve member satisfaction, and refine marketing strategies, using a survey as primary data and Qlik Sense as secondary data. Besides, using a survey methodology, the study collected 79 responses from different countries (the United Kingdom, Germany, Belgium, and the Netherlands) and analyzed the data with only 58 Specialist Club member's responses. A reliability examination was additionally accomplished to measure the accuracy of the collected survey data. Qlik Sense, Microsoft 365, and Excel 2016 were used for data analysis and visualization, which involved descriptive analysis, correlation coefficients, and text sentiment analysis. The study results show positive trends in member satisfaction across four markets, but a decreasing engagement rate is noticeable. In general, members from all countries were satisfied with Valeo products and services, contrasting with moderate dissatisfaction with the Valeo Specialist Club app and customer service. The study also found a strong correlation between the Valeo Specialist Club app, loyalty service, and customer service, suggesting improvements to meet member expectations and preferences. Nevertheless, the study also emphasizes the need to enhance the Valeo Specialist Club registration process, loyalty program features, and benefits to align with current trends, foster increased member satisfaction, and sustain loyalty within the independent aftermarket. Furthermore, the conclusions of this study will aid in developing upcoming marketing plans.