@inproceedings{32308, author = {{Yigitbas, Enes and Krois, Sebastian and Renzelmann, Timo and Engels, Gregor}}, booktitle = {{Proceedings of the 10th International Conference on Serious Games and Applications for Health (SeGAH'22) }}, publisher = {{IEEE}}, title = {{{Comparative Evaluation of AR-based, VR-based, and Traditional Basic Life Support Training}}}, year = {{2022}}, } @article{32306, author = {{Weidmann, Nils and Yigitbas, Enes and Anjorin, Anthony and Srivastava, Ankita and Jose, Jane}}, journal = {{The Journal of Object Technology }}, title = {{{Human-in-the-Loop Large-Scale Model Transformations with the VICToRy Debugger }}}, year = {{2022}}, } @phdthesis{30239, author = {{Kolb, Christina}}, title = {{{Competitive Routing in Hybrid Communications Networks and Message efficient SetCover in AdHoc Networks}}}, doi = {{10.17619/UNIPB/1-1673 }}, year = {{2022}}, } @article{21096, abstract = {{While many research in distributed computing has covered solutions for self-stabilizing computing and topologies, there is far less work on self-stabilization for distributed data structures. However, when peers in peer-to-peer networks crash, a distributed data structure may not remain intact. We present a self-stabilizing protocol for a distributed data structure called the Hashed Patricia Trie (Kniesburges and Scheideler WALCOM'11) that enables efficient prefix search on a set of keys. The data structure has many applications while offering low overhead and efficient operations when embedded on top of a Distributed Hash Table. Especially, longest prefix matching for x can be done in O(log |x|) hash table read accesses. We show how to maintain the structure in a self-stabilizing way, while assuring a low overhead in a legal state and an asymptotically optimal memory demand of O(d) bits, where d is the number of bits needed for storing all keys.}}, author = {{Knollmann, Till and Scheideler, Christian}}, issn = {{0890-5401}}, journal = {{Information and Computation}}, title = {{{A self-stabilizing Hashed Patricia Trie}}}, doi = {{10.1016/j.ic.2021.104697}}, year = {{2022}}, } @inproceedings{32342, author = {{Ahmed, Qazi Arbab and Platzner, Marco}}, location = {{Pafos, Cyprus}}, publisher = {{IEEE Computer Society Annual Symposium on VLSI (ISVLSI,2022)}}, title = {{{On the Detection and Circumvention of Bitstream-Level Trojans in FPGAs}}}, year = {{2022}}, } @inproceedings{33517, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Software Business - 13th International Conference, {ICSOB} 2022, Bolzano, Italy, Proceedings }}, publisher = {{Springer}}, title = {{{Don’t Start from Scratch: A Modularized Architecture for Business Model Development Tools}}}, year = {{2022}}, } @inproceedings{33518, author = {{Gottschalk, Sebastian and Parvez, Sarmad and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Product-Focused Software Process Improvement - 23rd International Conference, {PROFES} 2022, Jyväskylä, Finland, Proceedings }}, title = {{{Designing Platforms for Crowd-based Software Prototype Validation: A Design Science Study}}}, year = {{2022}}, } @inproceedings{33515, author = {{Buschek, Daniel and Hauptmann, Hanna and Heuer, Hendrik and Loepp, Benedikt and Riener, Andreas and Yigitbas, Enes}}, booktitle = {{Proceedings of the Mensch Und Computer 2022 (MuC ’22) }}, title = {{{UCAI 2022 - 3rd International Workshop on User-Centered Artificial Intelligence}}}, year = {{2022}}, } @article{34006, author = {{Yigitbas, Enes and Gorissen, Simon and Weidmann, Nils and Engels, Gregor}}, journal = {{International Journal on Software and Systems Modeling (SoSyM) }}, title = {{{Design and Evaluation of a Collaborative UML Modeling Environment in Virtual Reality}}}, year = {{2022}}, } @article{33990, abstract = {{Deep neural networks (DNNs) are penetrating into a broad spectrum of applications and replacing manual algorithmic implementations, including the radio frequency communications domain with classical signal processing algorithms. However, the high throughput (gigasamples per second) and low latency requirements of this application domain pose a significant hurdle for adopting computationally demanding DNNs. In this article, we explore highly specialized DNN inference accelerator approaches on field-programmable gate arrays (FPGAs) for RadioML modulation classification. Using an automated end-to-end flow for the generation of the FPGA solution, we can easily explore a spectrum of solutions that optimize for different design targets, including accuracy, power efficiency, resources, throughput, and latency. By leveraging reduced precision arithmetic and customized streaming dataflow, we demonstrate a solution that meets the application requirements and outperforms alternative FPGA efforts by 3.5x in terms of throughput. Against modern embedded graphics processing units (GPUs), we measure >10x higher throughput and >100x lower latency under comparable accuracy and power envelopes.}}, author = {{Jentzsch, Felix and Umuroglu, Yaman and Pappalardo, Alessandro and Blott, Michaela and Platzner, Marco}}, journal = {{IEEE Micro}}, number = {{6}}, pages = {{125--133}}, publisher = {{IEEE}}, title = {{{RadioML Meets FINN: Enabling Future RF Applications With FPGA Streaming Architectures}}}, doi = {{10.1109/MM.2022.3202091}}, volume = {{42}}, year = {{2022}}, } @inbook{29727, author = {{Wohlleben, Meike Claudia and Bender, Amelie and Peitz, Sebastian and Sextro, Walter}}, booktitle = {{Machine Learning, Optimization, and Data Science}}, isbn = {{9783030954697}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction}}}, doi = {{10.1007/978-3-030-95470-3_8}}, year = {{2022}}, } @misc{29000, abstract = {{This thesis aims to provide a bidirectional chatbot solution for the requirement engineering process. The Sonderforschungsbereich (SFB) 901 intends to provide the composition of software service On-the-Fly (OTF). The sub-project (B1) of the SFB 901 project deals with the parameters of service configuration. OTF Computing aims to eradicate the dependency on the requirement engineers for the software development process. However, there is no existing bidirectional chatbot solution that analyses user software requirements and provides viable suggestions to the user regarding their service. Previously, CORDULA chatbot was developed to analyze the software requirements but cannot keep the conversation’s context. The Rasa framework is integrated with the knowledge base to solve the issue, the knowledge base provides domain-specific knowledge to the chatbot. The software description is passed through the natural language understanding process to give consciousness to the chatbot. This process involves various machine learning models, including app family classification, to correctly identify the domain for user OTF service. The statistical models like naïve Bayes, kNN and SVM are compared with transformer models for this classification task. Furthermore, the entities (functional requirements) are also separated from the user description. The chatbot provides the suggestion of requirements from the preliminary service template with the support of the knowledge base. Furthermore, the generated response is compared with the state-of-the-art DialoGPT transformer model and ChatterBot conversational library. These models are trained over the software development related conversational dataset. All the responses are ranked using the DialoRPT model, and the BLEU score to evaluates the models’ responses. Moreover, the chatbot mod- els are tested with human participants, they used and scored the chatbot responses based on effectiveness, efficiency and satisfaction. The overall response accuracy is also measured by averaging the user approval over the generated responses.}}, author = {{Ahmed, Mobeen}}, title = {{{Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing}}}, year = {{2022}}, } @inproceedings{44838, author = {{Yigitbas, Enes}}, publisher = {{die hochschullehre Jahrgang 8/2022}}, title = {{{Einsatz und Evaluation von Virtual Reality-Technologie in einem Informatik-Seminar}}}, year = {{2022}}, } @inproceedings{34293, author = {{Wolters, Dennis and Engels, Gregor}}, booktitle = {{ICSOB'22 Companion Proceedings}}, publisher = {{CEUR}}, title = {{{Model-driven Design and Management of Professional Education Programmes}}}, volume = {{3316}}, year = {{2022}}, } @misc{45242, author = {{N., N.}}, title = {{{A Scalable and Extensible Architecture for a Crowd-Based Prototype Validation Platform}}}, year = {{2022}}, } @misc{45241, author = {{N., N.}}, title = {{{Conception and Implementation of a Situation-specific Design Thinking Tool}}}, year = {{2022}}, } @misc{45255, author = {{Anonymous, Anonymous}}, title = {{{ Comparison of Open Source Software in Quantum Computing from the Perspective of Software Development}}}, year = {{2022}}, } @misc{45256, author = {{Anonymous, Anonymous}}, title = {{{A Detailed Survey and Comparison of the Selected Homomorphic Encryption Libraries}}}, year = {{2022}}, } @misc{45257, author = {{Beckmann, Marvin}}, title = {{{Analysis of an Interactive Lattice Based Aggregated Signature Scheme}}}, year = {{2022}}, } @misc{45715, author = {{Tcheussi Ngayap, Vanessa Ingrid}}, title = {{{FreeRTOS on a MicroBlaze Soft-Core Processor with Hardware Accelerators}}}, year = {{2022}}, }