TY  - CHAP
A1  - Zähl, Philipp M.
A1  - Theis, Sabine
A1  - Wolf, Martin R.
A1  - Köhler, Klemens
ED  - Chen, Jessie Y. C.
ED  - Fragomeni, Gino
T1  - Teamwork in software development and what personality has to do with it - an overview
T2  - Virtual, Augmented and Mixed Reality
N2  - Due to the increasing complexity of software projects, software development is becoming more and more dependent on teams. The quality of this teamwork can vary depending on the team composition, as teams are always a combination of different skills and personality types. This paper aims to answer the question of how to describe a software development team and what influence the personality of the team members has on the team dynamics. For this purpose, a systematic literature review (n=48) and a literature search with the AI research assistant Elicit (n=20) were conducted. Result: A person’s personality significantly shapes his or her thinking and actions, which in turn influences his or her behavior in software development teams. It has been shown that team performance and satisfaction can be strongly influenced by personality. The quality of communication and the likelihood of conflict can also be attributed to personality.
KW  - Teamwork
KW  - Software
KW  - Personality
KW  - Performance
KW  - Elicit
Y1  - 2023
SN  - 978-3-031-35633-9 (Print)
SN  - 978-3-031-35634-6 (Online)
U6  - https://doi.org/10.1007/978-3-031-35634-6_10
N1  - Virtual, Augmented and Mixed Reality: 15th International Conference. VAMR 2023. 
Held as Part of the 25th HCI International Conference. HCII 2023. 
Copenhagen, Denmark. July 23–28, 2023.
SP  - 130
EP  - 153
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Arndt, Tobias
A1  - Conzen, Max
A1  - Elsen, Ingo
A1  - Ferrein, Alexander
A1  - Galla, Oskar
A1  - Köse, Hakan
A1  - Schiffer, Stefan
A1  - Tschesche, Matteo
T1  - Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers
T2  - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments
N2  - This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.
KW  - Datasets
KW  - Neural networks
KW  - Anomaly detection
KW  - Quality control
KW  - Process optimization
Y1  - 2023
SN  - 9798400700699
U6  - https://doi.org/10.1145/3594806.3596558
N1  - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece, July 5 - 7, 2023
SP  - 535
EP  - 542
PB  - ACM
ER  - 
TY  - CHAP
A1  - Neth, Jannik
A1  - Schuba, Marko
A1  - Brodkorb, Karsten
A1  - Neugebauer, Georg
A1  - Höner, Tim
A1  - Hack, Sacha
T1  - Digital forensics triage app for android
T2  - ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security
N2  - Digital forensics of smartphones is of utmost importance in many criminal cases. As modern smartphones store chats, photos, videos etc. that can be relevant for investigations and as they can have storage capacities of hundreds of gigabytes, they are a primary target for forensic investigators. However, it is exactly this large amount of data that is causing problems: extracting and examining the data from multiple phones seized in the context of a case is taking more and more time. This bears the risk of wasting a lot of time with irrelevant phones while there is not enough time left to analyze a phone which is worth examination. Forensic triage can help in this case: Such a triage is a preselection step based on a subset of data and is performed before fully extracting all the data from the smartphone. Triage can accelerate subsequent investigations and is especially useful in cases where time is essential. The aim of this paper is to determine which and how much data from an Android smartphone can be made directly accessible to the forensic investigator – without tedious investigations. For this purpose, an app has been developed that can be used with extremely limited storage of data in the handset and which outputs the extracted data immediately to the forensic workstation in a human- and machine-readable format.
KW  - Android
KW  - Digital triage
KW  - Triage-app
Y1  - 2023
SN  - 9798400707728
U6  - https://doi.org/10.1145/3600160.3605017
N1  - ARES 2023: The 18th International Conference on Availability, Reliability and Security. August 29 - September 1, 2023. Benevento, Italy.
PB  - ACM
ER  - 
TY  - CHAP
A1  - Küppers, Malte
A1  - Schuba, Marko
A1  - Neugebauer, Georg
A1  - Höner, Tim
A1  - Hack, Sacha
T1  - Security analysis of the KNX smart building protocol
T2  - ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security
N2  - KNX is a protocol for smart building automation, e.g., for automated heating, air conditioning, or lighting. This paper analyses and evaluates state-of-the-art KNX devices from manufacturers Merten, Gira and Siemens with respect to security. On the one hand, it is investigated if publicly known vulnerabilities like insecure storage of passwords in software, unencrypted communication, or denialof-service attacks, can be reproduced in new devices. On the other hand, the security is analyzed in general, leading to the discovery of a previously unknown and high risk vulnerability related to so-called BCU (authentication) keys.
Y1  - 2023
U6  - https://doi.org/10.1145/3600160.3605167
N1  - ARES 2023: The 18th International Conference on Availability, Reliability and Security. August 29 - September 1, 2023. Benevento, Italy.

Article No.: 87
SP  - 1
EP  - 7
PB  - ACM
ER  - 
TY  - CHAP
A1  - Schulte-Tigges, Joschua
A1  - Matheis, Dominik
A1  - Reke, Michael
A1  - Walter, Thomas
A1  - Kaszner, Daniel
ED  - Krömker, Heidi
T1  - Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain
T2  - HCII 2023: HCI in Mobility, Transport, and Automotive Systems
N2  - Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.
KW  - V2X
KW  - Transiton of Control
KW  - Minimum Risk Manoeuvre
KW  - Operational Design Domain
KW  - Connected Automated Vehicle
Y1  - 2023
SN  - 978-3-031-35677-3 (Print)
SN  - 978-3-031-35678-0 (Online)
U6  - https://doi.org/10.1007/978-3-031-35678-0_12
N1  - 5th International Conference, MobiTAS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023.
SP  - 200
EP  - 210
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Klöser, Lars
A1  - Büsgen, André
A1  - Kohl, Philipp
A1  - Kraft, Bodo
A1  - Zündorf, Albert
ED  - Conte, Donatello
ED  - Fred, Ana
ED  - Gusikhin, Oleg
ED  - Sansone, Carlo
T1  - Explaining relation classification models with semantic extents
T2  - Deep Learning Theory and Applications
N2  - In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.

We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
KW  - Relation classification
KW  - Natural language processing
KW  - Natural language understanding
KW  - Information extraction
KW  - Trustworthy artificial intelligence
Y1  - 2023
SN  - 978-3-031-39058-6 (Print)
SN  - 978-3-031-39059-3 (Online)
U6  - https://doi.org/10.1007/978-3-031-39059-3_13
N1  - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023.
SP  - 189
EP  - 208
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Freyer, Nils
A1  - Thewes, Dustin
A1  - Meinecke, Matthias
ED  - Gusikhin, Oleg
ED  - Hammoudi, Slimane
ED  - Cuzzocrea, Alfredo
T1  - GUIDO: a hybrid approach to guideline discovery & ordering from natural language texts
T2  - Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1
N2  - Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
KW  - Natural Language Processing
KW  - Text Mining
KW  - Process Model Extraction
KW  - Business Process Intelligence
Y1  - 2023
SN  - 978-989-758-664-4
U6  - https://doi.org/10.5220/0012084400003541
SN  - 2184-285X
N1  - 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy.
SP  - 335
EP  - 342
ER  - 
TY  - CHAP
A1  - Nikolovski, Gjorgji
A1  - Limpert, Nicolas
A1  - Nessau, Hendrik
A1  - Reke, Michael
A1  - Ferrein, Alexander
T1  - Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles
T2  - 2023 IEEE Intelligent Vehicles Symposium (IV)
N2  - The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
KW  - Mpc
KW  - Control
KW  - Path-following
KW  - Navigation
KW  - Automation
Y1  - 2023
SN  - 979-8-3503-4691-6 (Online)
SN  - 979-8-3503-4692-3 (Print)
U6  - https://doi.org/10.1109/IV55152.2023.10186806
N1  - IEEE Symposium on Intelligent Vehicle, 4.-7. June 2023, Anchorage, AK, USA.
PB  - IEEE
ER  - 
TY  - CHAP
A1  - Kohl, Philipp
A1  - Freyer, Nils
A1  - Krämer, Yoka
A1  - Werth, Henri
A1  - Wolf, Steffen
A1  - Kraft, Bodo
A1  - Meinecke, Matthias
A1  - Zündorf, Albert
ED  - Conte, Donatello
ED  - Fred, Ana
ED  - Gusikhin, Oleg
ED  - Sansone, Carlo
T1  - ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP
T2  - Deep Learning Theory and Applications
N2  - Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.

However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.

The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
KW  - Active learning
KW  - Query learning
KW  - Natural language processing
KW  - Deep learning
KW  - Reproducible research
Y1  - 2023
SN  - 978-3-031-39058-6 (Print)
SN  - 978-3-031-39059-3 (Online)
U6  - https://doi.org/10.1007/978-3-031-39059-3_16
N1  - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023.
SP  - 235
EP  - 253
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Büsgen, André
A1  - Klöser, Lars
A1  - Kohl, Philipp
A1  - Schmidts, Oliver
A1  - Kraft, Bodo
A1  - Zündorf, Albert
ED  - Cuzzocrea, Alfredo
ED  - Gusikhin, Oleg
ED  - Hammoudi, Slimane
ED  - Quix, Christoph
T1  - From cracked accounts to fake IDs: user profiling on German telegram black market channels
T2  - Data Management Technologies and Applications
N2  - Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.
KW  - Clustering
KW  - Natural language processing
KW  - Information extraction
KW  - Profile extraction
KW  - Text mining
Y1  - 2023
SN  - 978-3-031-37889-8 (Print)
SN  - 978-3-031-37890-4 (Online)
U6  - https://doi.org/10.1007/978-3-031-37890-4_9
N1  - 10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022
SP  - 176
EP  - 202
PB  - Springer
CY  - Cham
ER  -