TY - CHAP A1 - Revina, Aleksandra A1 - Kalabina, Elena A1 - Belyak, Olga T1 - Employee Engagement and Soft Skills in the Digitalization of the Economy. Preliminary Study Results T2 - Kumar, V., Kyriakopoulos, G.L., Akberdina, V., Kuzmin, E. (eds) Digital Transformation in Industry . DTI 2022. Lecture Notes in Information Systems and Organisation, vol 61. Springer, Cham N2 - The fast-paced digitalization and automation make hard skills acquired by employees in their academic study programs outdated and irrelevant very fast. Hence, researchers and practitioners have recently demonstrated a growing interest in studying soft skills in various settings, including leadership, education, and training. Additionally, in times of digital transformation, businesses are promoting employee engagement evermore, particularly in the present context of crises and instability. The research intends to investigate this issue from the viewpoint of employee soft skills. We specifically look into the link between employees’ soft skills and workplace engagement. While doing our research in two different countries, we distinguish between commercial and non-commercial organizations as well as add an international perspective. Based on an acknowledged European skills framework, we focus our study on such soft skills as teamwork, communication, and conflict management and use recognized surveys to quantify them. We use a well-known Gallup methodology to evaluate employee engagement. Using correlation analysis, we investigate the impact of the three selected skills on employee engagement while considering the organization type and country, factors that may play an important role in the digital transformation of businesses and companies. Y1 - 2023 U6 - https://doi.org/10.1007/978-3-031-30351-7_34 SP - 467 EP - 479 PB - Springer ER - TY - JOUR A1 - Mansoor, Nazneen A1 - Schwarz, Klaus A1 - Creutzburg, Rainer T1 - Importance of OSINT/SOCMINT for modern disaster management evaluation - Australia, Haiti, Japan JF - Electronic Imaging N2 - Open-source technologies (OSINT) and Social Media Intelligence (SOCMINT) are becoming increasingly popular with investigative and government agencies, intelligence services, media companies, and corporations. These OSINT and SOCMINT technologies use sophisticated techniques and special tools to efficiently analyze the continually growing sources of information. There is a great need for training and further education in the OSINT field worldwide. This report describes the importance of open source or social media intelligence for evaluating disaster management. It also gives an overview of the government work in Australia, Haiti, and Japan for disaster management using various OSINT tools and platforms. Thus, decision support for using OSINT and SOCMINT tools is given, and the necessary training needs for investigators can be better estimated. Y1 - 2023 U6 - https://doi.org/10.2352/EI.2023.35.3.MOBMU-354 VL - 35 IS - 3 SP - 354-1 EP - 354-14 PB - Society for Imaging Science and Technology (IS&T) ER - TY - JOUR ED - Wilms, Andreas T1 - Jahrbuch 2022/2023 der Absolventinnen und Absolventen T3 - Jahrbuch ... der Absolventinnen und Absolventen / Technische Hochschule Brandenburg - 2022/2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:522-opus4-32514 PB - Technische Hochschule Brandenburg CY - Brandenburg an der Havel ER - TY - JOUR A1 - Vishnevskaya, Valeria A1 - Schwarz, Klaus A1 - Creutzburg, Reiner T1 - Practical OSINT investigation - Similarity calculation using Reddit user profile data JF - Electronic Imaging N2 - This paper presents a practical Open Source Intelligence (OSINT) use case for user similarity measurements with the use of open profile data from the Reddit social network. This PoC work combines the open data from Reddit and the part of the state-of-the-art BERT model. Using the PRAW Python library, the project fetches comments and posts of users. Then these texts are converted into a feature vector - representation of all user posts and comments. The main idea here is to create a comparable user's pair similarity score based on their comments and posts. For example, if we fix one user and calculate scores of all mutual pairs with other users, we will produce a total order on the set of all mutual pairs with that user. This total order can be described as a degree of written similarity with this chosen user. A set of "similar" users for one particular user can be used to recommend to the user interesting for him people. The similarity score also has a "transitive property": if $user_1$ is "similar" to $user_2$ and $user_2$ is similar to $user_3$ then inner properties of our model guarantees that $user_1$ and $user_3$ are pretty "similar" too. In this way, this score can be used to cluster a set of users into sets of "similar" users. It could be used in some recommendation algorithms or tune already existing algorithms to consider a cluster's peculiarities. Also, we can extend our model and calculate feature vectors for subreddits. In that way, we can find similar to the user's subreddits and recommend them to him. Y1 - 2023 U6 - https://doi.org/10.2352/EI.2023.35.3.MOBMU-356 VL - 35 IS - 3 SP - 356-1 EP - 356-10 PB - Society for Imaging Science and Technology (IS&T) ER - TY - JOUR A1 - Skylar, Artem A1 - Schwarz, Klaus A1 - Creutzburg, Reiner T1 - Practical OSINT investigation in Twitter utilizing AI-based aggressiveness analysis JF - Electronic Imaging N2 - Open-source intelligence is gaining popularity due to the rapid development of social networks. There is more and more information in the public domain. One of the most popular social networks is Twitter. It was chosen to analyze the dependence of changes in the number of likes, reposts, quotes and retweets on the aggressiveness of the post text for a separate profile, as this information can be important not only for the owner of the channel in the social network, but also for other studies that in some way influence user accounts and their behavior in the social network. Furthermore, this work includes a detailed analysis and evaluation of the Tweety library capabilities and situations in which it can be effectively applied. Lastly, this work includes the creation and description of a compiled neural network whose purpose is to predict changes in the number of likes, reposts, quotes, and retweets from the aggressiveness of the post text for a separate profile. Y1 - 2023 U6 - https://doi.org/10.2352/EI.2023.35.3.MOBMU-355 VL - 35 IS - 3 SP - 355-1 EP - 355-15 PB - Society for Imaging Science and Technology (IS&T) ER - TY - JOUR A1 - Narasimhan, Pranesh Kumar A1 - Bhosale, Chinmay A1 - Pervez, Muhammad Hasban A1 - Naqvi, Najiba Zainab A1 - Ecevit, Mert Ilhan A1 - Schwarz, Klaus A1 - Creutzburg, Reiner T1 - Open-source Intelligence (OSINT) investigation in Facebook JF - Electronic Imaging N2 - Open Source Intelligence (OSINT) has come a long way, and it is still developing ideas, and lots of investigations are yet to happen in the near future. The main essential requirement for all the OSINT investigations is the information that is valuable data from a good source. This paper discusses various tools and methodologies related to Facebook data collection and analyzes part of the collected data. At the end of the paper, the reader will get a deep and clear insight into the available techniques, tools, and descriptions about tools that are present to scrape the data out of the Facebook platform and the types of investigations and analyses that the gathered data can do. Y1 - 2023 U6 - https://doi.org/10.2352/EI.2023.35.3.MOBMU-357 VL - 35 IS - 3 SP - 357-1 EP - 357-12 PB - Society for Imaging Science and Technology (IS&T) ER - TY - CHAP A1 - Tetzlaff, Laura A1 - Purohit, Anne-Maria A1 - Spallek, Jacob A1 - Holmberg, Christine A1 - Schrader, Thomas T1 - Evaluating Interoperability in German Critical Incident Reporting Systems T2 - German Medical Data Sciences 2023 – Science. Close to People. Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023, Heilbronn, Germany N2 - In industrialised countries, one in ten patients suffers harm during hospitalization. Critical Incident Reporting Systems (CIRS) aim to minimize this by learning from errors and identifying potential risks. However, a lack of interoperability among the 16 CIRS in Germany hampers their effectiveness. Y1 - 2023 U6 - https://doi.org/10.3233/SHTI230722 SP - 249 EP - 257 PB - IOS Press ER - TY - CHAP A1 - Pepe, Alberto A1 - Buchholz, Sven A1 - Lasenby, Joan T1 - Clifford Group Equivariant Neural Network Layers for Protein Structure Prediction T2 - Northern Lights Deep Learning Conference, VOL 233, 09-11 Januar 2024, Tromso, Norwegen N2 - We employ Clifford Group Equivariant Neural Network (CGENN) layers to predict protein coordinates in a Protein Structure Prediction (PSP) pipeline. PSP is the estimation of the 3D structure of a protein, generally through deep learning architectures. Information about the geometry of the protein chain has been proven to be crucial for accurate predictions of 3D structures. However, this information is usually flattened as machine learning features that are not representative of the geometric nature of the problem. Leveraging recent advances in geometric deep learning, we redesign the 3D projector part of a PSP architecture with the addition of CGENN layers . CGENNs can achieve better generalization and robustness when dealing with data that show rotational or translational invariance such as protein coordinates, which are independent of the chosen reference frame. CGENNs inputs, outputs, weights and biases are objects in the Geometric Algebra of 3D Euclidean space, i.e. G3,0,0, and hence are interpretable from a geometrical perspective. We test 6 approaches to PSP and show that CGENN layers increase the accuracy in term of GDT scores by up to 2.1\%, with fewer trainable parameters compared to linear layers and give a clear geometric interpretation of their outputs. Y1 - 2024 UR - https://proceedings.mlr.press/v233/pepe24a/pepe24a.pdf SP - 1 EP - 7 PB - PMLR ER - TY - CHAP A1 - Markgraf, Klaus A1 - Dietrich, Benjamin A1 - Müller, Katja A1 - Flassig, Robert A1 - Flassig, Peter T1 - FINEconcepts - Wissenstransfer und Energiesystemoptimierung mithilfe des digitalen Zwillings T2 - NWK, HS Harz, 2023 N2 - Climate change, but also geopolitical circumstances, are moving topics such as energy efficiency and renewable energies more and more into the focus of the population, economy , and politics. As a result, the will to optimize new and existing energy systems extends from private individuals to companies and even entire communities. This work describes the development and usage of a new software called FINEconcepts which creates a digital twin of an energy system. This virtual model can then be used to optimize the energy system based on annual costs, CO2 emissions or other relevant criteria such as self-sufficiency. Because all system components, which include renewable technologies as well, can be added as a building block with chosen but changeable parameters, the software allows the user to explore and awaken interest and understanding of technologies that were previously considered too costly, irrelevant, or unrealistic. Implemented projects in small and large companies as well as in residential areas did prove, that the usage of FINEconcepts leads not only to more efficient energy systems by increasing the use of renewable energy, but also increased knowledge and understanding in terms of energy. Besides economics, ecology and security, understanding is an equally important factor in achieving a sustainable energy supply. Y1 - 2023 UR - https://www.hs-harz.de/dokumente/extern/Forschung/NWK2023/Beitraege/FINEconcepts_-_Wissenstransfer_und_Energiesystemoptimierung_mithilfe_des_digitalen_Zwillings.pdf SP - 428 EP - 435 PB - HS Harz ER - TY - JOUR A1 - Wang, Jing A1 - Harth, Kirsten A1 - Puzyrev, Dmitry A1 - Stannarius, Ralf T1 - The effect of obstacles near a silo outlet on the discharge of soft spheres JF - New Journal of Physics KW - fluid dynamics Y1 - 2022 U6 - https://doi.org/10.1088/1367-2630/ac8bea VL - 24 PB - IOP ER -