http://dl.gi.de/handle/20.500.12116/40038 Thu, 23 Jul 2026 21:37:08 GMT 2026-07-23T21:37:08Z http://dl.gi.de:80/bitstream/id/c08433b5-3478-4991-8626-23d546465c63/ http://dl.gi.de/handle/20.500.12116/40038 http://dl.gi.de/handle/20.500.12116/40051 COMBI: Artificial Intelligence for Computer-Based Forensic Analysis of Persons Becker, Sven; Heuschkel, Marie; Richter, Sabine; Labudde, Dirk During the prosecution process the primary objective is to prove criminal offences to the correct perpetrator to convict them with legal effect. However, in reality this may often be difficult to achieve. Suppose a suspect has been identified and is accused of a bank robbery. Due to the location of the crime, it can be assumed that there is sufficient image and video surveillance footage available, having recorded the perpetrator at the crime scene. Depending on the surveillance system used, there could be even high-resolution material available. In short, optimal conditions seem to be in place for further investigations, especially as far as the identification of the perpetrator and the collection of evidence of their participation in the crime are concerned. However, perpetrators usually act using some kind of concealment to hide their identity. In most cases, they disguise their faces and even their gait. Conventional investigation approaches and methods such as facial recognition and gait analysis then quickly reach their limits. For this reason, an approach based on anthropometric person-specific digital skeletons, so-called rigs, that is being researched by the COMBI research project is presented in this publication. Using these rigs, it should be possible to assign known identities, comparable to suspects, to unknown identities, comparable to perpetrators. The aim of the COMBI research project is to study the anthropometric pattern as a biometric identifier as well as to make it feasible for the standardised application in the taking of evidence by the police and prosecution. The approach is intended to present computer-aided opportunities for the identification of perpetrators that can support already established procedures. Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40051 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40052 News null Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40052 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40050 Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques Solanke, Abiodun A.; Biasiotti, Maria Angela The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process. Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40050 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40046 Automatic Generation of Personalised and Context-Dependent Textual Interventions During Neuro-rehabilitation Felske, Timon; Bader, Sebastian; Kirste, Thomas In this paper we present our system that synthesises personalised and context dependent texts during robot guided exercises for neuro-rehabilitation. This system is used to generate texts for the communication between a care robot and patients. We present requirements that a system in such a medical domain has to meet. Afterwards the results of a systematic literature review are presented. We present our solution based on the RosaeNLG system. It supports different language levels and referring expressions in a real-time text generation system, so that generated texts can be adapted to the reader in the best possible way. We evaluate our system with respect to the requirements. The contribution of the paper is twofold: We present a set of requirements for Natural Language Generation (NLG) in medical domains and we show how to extend RosaeNLG with an external dialogue memory to handle complex referring expressions in medical real time settings. Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40046 2022-01-01T00:00:00Z