Christoph Reich

Furtwangen University | HFU · Faculty of Computer Science

Montenegro restored its national independence in 2006, and in the 17 years since then, the country has made significant progress in digital transformation, which is especially important for its accession to the European Union. In this paper, this period of 17 years of Montenegrin digital transformation is reviewed. The work aims to provide comprehe...

For the purpose of improve the sustainability of products sold in the EU, Digital Product Passports is a mandatory solution. These passports would systematically gather information about products, such as detailing the materials involved to aid recycling and remanufacturing efforts, or recording the emissions produced throughout the manufacturing p...

Data scientists, researchers and engineers want to understand, whether machine learning models for object detection work accurate and precise. Networks like Yolo use bounding boxes as a result to localize the object in the image. The principal aim of this paper is to address the problem of a lack of an effective metric for evaluating the results of...

In recent years, the manufacturing sector has benefited from a variety of technological innovations regarding automation and utilization of artificial intelligence on the shopfloor. During the manufacturing processes of a product, a variety of meta data can be derived from each working step and considered during subsequent stages of its liefycle li...

Generative models and their possible applications are almost limitless. But there are still problems that such models have. On one hand, the models are difficult to train. Stability in training, mode collapse or non convergence, together with the huge parameter space make it extremely costly and difficult to train and optimize generative models. Th...

In the rapidly evolving realm of the Industrial Internet of Things (IIoT), securing shop floor operations, especially in audit processes, is of critical importance. This paper confronts the challenge of ensuring data integrity and trust in IIoT systems by leveraging the capabilities of blockchain technology. The unique characteristics of blockchain...

Up until now, it has been shown that big parts of the so called Industry 4.0 are impacted by Machine Learning (ML) in some way or another. In many shopfloor situations, there are different sensors involved and all data is eventually structured, accumulated and prepared for application in various ML-based scenarios, e.g., predictive maintenance of a...

Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of int...

As machine learning becomes increasingly pervasive, its resource demands and financial implications escalate, necessitating energy and cost optimisations to meet stakeholder demands. Quality metrics for predictive machine learning models are abundant, but efficiency metrics remain rare. We propose a framework for efficiency metrics, that enables th...

Hyperparameter tuning is an important aspect in machine-learning especially for deep generative models. Tuning models to stabilize training and to get the best accuracy can be a time consuming and protracted process. Generative models have a large search space requiring resources and knowledge to find the best parameters. Therefore, in most cases t...

The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability...

Health informatics plays a crucial role in modern healthcare provision. Training and continuous education are essential to bolster the healthcare workforce on health informatics. In this work, we present the training events within EU-funded DigNest project. The aim of the training events, the subjects offered, and the overall evaluation of the resu...

This poster presents a Montenegrin Digital Academic Innovation Hub aimed to support education, innovations, and academia-business cooperation in medical informatics (as one of four priority areas) at national level in Montenegro. The Hub topology and its organisation in the form of two main nodes, with services established within key pillars: Digit...

In this paper, we present a study on the utilization of smart medical wearables and the user manuals of such devices. A total of 342 individuals provided input for 18 questions that address user behavior in the investigated context and the connections between various assessments and preferences. The presented work clusters individuals based on thei...

The YOLO series of object detection algorithms, including YOLOv4 and YOLOv5, have shown superior performance in various medical diagnostic tasks, surpassing human ability in some cases. However, their black-box nature has limited their adoption in medical applications that require trust and explainability of model decisions. To address this issue,...

In today's technology-driven era, managing digital identities has become a critical concern due to the widespread use of online services and digital devices. This has led to a fragmented landscape of digital identities, burdening individuals with multiple usernames, passwords, and authentication methods. To address this challenge, digital wallets h...

The common corpus optimization method 'stop words removal' is based on the assumption that text tokens with high occurrence frequency can be removed without affecting classification performance. Linguistic information regarding sentence structure is ignored as well as preferences of the classification technology. We propose the Weighted Unimportant...

The Industrial Internet of Things (IIoT) holds significant potential for improving efficiency, quality, and flexibility. In decentralized systems, there are no trust-based centralized authentication techniques, which are unsuitable for distributed networks or subnets, as they have a single point of failure. However, in a decentralized system, more...

0000−0001−9115−7668] , Fatemeh Stodt 2[0000−0003−0863−0907] , Christoph Reich 1[0000−0001−9831−2181] , and Nathan Clarke 3[0000−0002−3595−3800] Abstract. The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to health-care support, almost every area of daily life and i...

Supervised object detection models are trained to recognize certain objects. These models are classified into two types: single-stage detectors and two-stage detectors. The single-stage detectors just need one pass through the model to anticipate all the bounding boxes, whereas the two-stage detectors require to first estimate the image portions wh...

Industrial Internet of Things (IIoT) systems are enhancing the delivery of services and boosting productivity in a wide array of industries, from manufacturing to healthcare. However, IIoT devices are susceptible to cyber-threats such as the leaking of important information, products becoming compromised, and damage to industrial controls. Recently...

The digitization and general industrial development of Montenegro is a great challenge for engineering and science due to its special characteristics. As the accession of Montenegro to the European Union has been an ongoing agenda for over a decade now, and the accession of the country is expected by 2025, adapting the interconnectivity and smart a...

ML-based applications already play an important role in factories in areas such as visual quality inspection, process optimization, and maintenance prediction and will become even more important in the future. For ML to be used in an industrial setting in a safe and effective way, the different steps needed to use ML must be put together in an ML p...

Identity management, authentication, and attribute verification are among the main concerns in many IoT applications. Considering the privacy concerns, attribute verification became more important in many applications. Many of the proposed models in this field suffer from privacy and scalability issues as they depend on a centralized entity. In thi...

To deliver better healthcare to patients and advance healthcare solutions as well as to increase the efficiency of the manufacturing process and thus reduce material and energy consumption in production, more and more artificial intelligence (AI) methods are applied in the field of both, Medicine and Manufacturing. Some of the exciting applications...

Image augmentation has become an important part of the data preprocessing pipeline, helping to acquire more samples by altering existing samples by cutting, shifting, etc.. For some domains, augmenting existing images is not sufficient, due to missing samples in the domains (e.g., faulty work pieces or events that occur infrequently). In such a cas...

The ‘things’ layer in Internet of Things (IoT) consists of a massive number of devices, many of which are power and resource constrained. Decentralized Attribute-based Encryption (DABE) provides a one-to-many scheme that fits the distributed nature of IoT, however requires extensive computation power which makes its adoption difficult. In this work...

Purpose Ultrasound (US) and Shear Wave Elastography (SWE) imaging are non-invasive methods used for breast lesion characterization. While US and SWE images provide both morphological information, SWE visualizes in addition the elasticity of tissue. In this study a Discriminative Convolutional Neural Network (DCNN) model is applied to US and SWE ima...

The Internet of things (IoT) network resources generate enormous data. The generated real-time data can be used in various fields to improve the quality of the provided services and make smart decisions. One of the main concerns in such systems is validating the received data. Most data validations techniques rely and run on a centralized entity, w...

The traditional field of industrial manufacturing is in the process of being revolutionized as machines become smart and processes are translated to and perfected by digital systems. The application of Machine Learning (ML) has established itself as a smart technology in the manufacturing industry. The optimal operation and training of ML applicati...

Enormous potential of artificial intelligence (AI) exists in numerous products and services, especially in healthcare and medical technology. Explainability is a central prerequisite for certification procedures around the world and the fulfilment of transparency obligations. Explainability tools increase the comprehensibility of object recognition...

In the context of Industry 4.0, smart factories use advanced sensing and data analytic technologies to understand and monitor the manufacturing processes. To enhance production efficiency and reliability, statistical Artificial Intelligence (AI) technologies such as machine learning and data mining are used to detect and predict potential anomalies...

Health and health systems are not excluded from the influence of digitalization. In Montenegro, regarding the digitization process, when compared to other sectors, the health sector is lagging. In this poster presentation, we present an ambitious Erasmus+ DigN€ST project aimed on modernization of digitalization of healthcare system in Montenegro, a...

To achieve a fully connected network in Internet of Things (IoT) there are number of challenges that have to be overcome. Among those, a big challenge is how to keep all of the devices accessible everywhere and every time. In the IoT network, the assumption is that each IoT device can be reached by any client at any given time. In practice, this is...

The resources in Internet of Things (IoT) are distributed among different physical geographic locations. In centralized resource discovery, the resources are registered in a centralized third-party server, and the clients can discover any resource by querying the centralized entity. In the decentralized resource discovery, the task of resource regi...

In edge/fog computing infrastructures, the resources and services are offloaded to the edge and computations are distributed among different nodes instead of transmitting them to a centralized entity. Distributed Hash Table (DHT) systems provide a solution to organizing and distributing the computations and storage without involving a trusted third...

Distributed machine learning algorithms that employ Deep Neural Networks (DNNs) are widely used in Industry 4.0 applications, such as smart manufacturing. The layers of a DNN can be mapped onto different nodes located in the cloud, edge and shop floor for preserving privacy. The quality of the data that is fed into and processed through the DNN is...

Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project development management arises. Due to the multitude of tools available for different operational phases...

The resources in the Internet of Things (IoT) network are distributed among different parts of the network. Considering huge number of IoT resources, the task of discovering them is challenging. While registering them in a centralized server such as a cloud data center is one possible solution, but due to billions of IoT resources and their limited...

While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attribu...

The Internet of Things (IoT) consists of billions of resources distributed among different geographical locations. In centralized resource discovery, the resources are registered in a centralized third party server, and the clients can discover any resource by querying the centralized entity. In the decentralized resource discovery the task of reso...

The huge number of resources and their computation power capabilities in the Internet of Things (IoT) network brings new challenges comparing to traditional networks. Among those challenges is the used approach to register and discover the resources in IoT. In traditional network architectures, the resources are registered in a centralized trusted...

In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated...

Digital transformation strengthens the interconnection of companies in order to develop optimized and better customized, cross-company business models. These models require secure, reliable, and traceable evidence and monitoring of contractually agreed information to gain trust between stakeholders. Blockchain technology using smart contracts allow...

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and hum...

The number of devices connected together in the Internet of Things (IoT) are growing and therefore the demand of an efficient and secure method for discovering the resources in IoT is increasing. In most of IoT schemes, the resources are discovered based on their properties (i.e. their location, types, etc.) and the clients are able to discover the...

Containers have become popular in HPC environments to improve the mobility of applications and the delivery of user-supplied code. In this paper we evaluate Podman, an enterprise container engine that supports rootless containers , in combination with runc and crun as container runtimes using a real-world workload with LS-DYNA and the industry-stan...

In the Industry 4.0 context, especially when considering large factories producing costly goods, monitoring sensor values is important to ensure high quality. This reduces large costs for mending faulty products or recall of those. Different approaches are used to ensure efficient monitoring and validation of sensor values. The Distributed Data Val...

The generated real-time data on the Internet of Things (IoT) and the ability to gather and manipulate them are positively affecting various fields. One of the main concerns in IoT is how to provide trustworthy data. The data validation network ensures that the generated data by data sources in the IoT are trustworthy. However, the existing data val...

In this paper, Region based Distributed Hash Table (RDHT) is proposed that can used in fog computing infrastructure to create an overlay of fog nodes divided logically into multiple regions based on their physical locations. RDHT generates a single overlay and it can be generated without specific organizing entity or location based devices.

Abstract—Generative Adversarial Networks (GANs) are part of the deep generative model family and able to generate synthetic samples based on the underlying distribution of real-world data. With expanding interest new discoveries and recent advances are hard to follow. Recent advancements to stabilize training, will help GANs to open up new domains...

Schleifen ist zu einem Standard-Produktionsschritt für die Präzisionsfertigung von Metallteilen geworden. Nicht optimale Prozessbedingungen während des Schleifprozesses können jedoch zu einer unangemessenen lokalen Temperaturerhöhung in der Randzone des Werkstücks führen und den sogenannten „Schleifbrand“ verursachen. Dies kann zu Gefügeveränderung...

Increased collaborative production and dynamic selection of production partners within industry 4.0 manufacturing leads to ever-increasing automatic data exchange between companies. Automatic and unsupervised data exchange creates new attack vectors, which could be used by a malicious insider to leak secrets via an otherwise considered secure chann...

The digital transformation of companies is expected to increase the digital interconnection between different companies to develop optimized, customized, hybrid business models. These cross-company business models require secure, reliable, and traceable logging and monitoring of contractually agreed information sharing between machine tools, operat...

The digital transformation of companies is expected to increase the digital interconnection between different companies to develop optimized, customized, hybrid business models. These cross-company business models require secure, reliable, and traceable logging and monitoring of contractually agreed information sharing between machine tools, operat...

Containers have to be secured in a multi-tenant environment. To secure the use of containerized environments, the effectiveness of a rule-based security monitoring approach have been investigated. The approach of this paper can be used to detect a wide range of potentially malicious behaviour of workloads in containerized environments. Additionally...

Billions of resources are connected to each other through the Internet of Things (IoT). Due to the huge number of connected resources in IoT, the challenge is how to discover the appropriate resources efficiently. The task becomes more challenging by taking into consideration the limited storage and computation power of devices and the distributed...

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery fr...

Ensuring data quality is central to the digital transformation in industry. Business processes such as predictive maintenance or condition monitoring can be implemented or improved based on the available data. In order to guarantee high data quality, a single data validation system are usually used to validate the production data for further use. H...

Cylindrical grinding is an important process in the manufacturing industry. During this process, the problem of grinding burn may appear, which can cause the workpiece to be worthless. In this work, a machine learning neural network approach is used to predict grinding burn based on the process parameters to prevent damage. A small dataset of 21 sa...

Current cloud architectures do not comply with today's digital forensics procedures-largely due to the fundamental dynamic nature of the cloud. Data acquisition is the first and arguably the most important process within digital forensics-to ensure data integrity and admissibility. Currently investigators have no option but to rely on the Cloud Ser...

Following the trend of Industry 4.0, automation in different manufacturing processes has triggered the use of intelligent condition monitoring systems, which are crucial for improving productivity and availability of production systems. To develop such an intelligent system, semantic technologies are of paramount importance. This paper introduces a...

Within the product life cycle, manufacturing processes are steps through which raw materials are assembled and transformed into final products. When different applications share information concerning manufacturing processes, the lack of information interoperability is a challenging issue that emerges during the manufacturing process implementation...

Condition monitoring is performed to identify the functioning state of a machine or a mechanical system. It is an important task by which the machine or mechanical system deterioration tendency and the location of a failure can be detected. In recent years, ontologies have shown promising results to enhance knowledge sharing in condition monitoring...

Within manufacturing processes, anomalies such as machinery faults and failures may lead to the outage situation of production lines. The outage of production lines is detrimental for the availability of production systems and may cause severe economic loss. To avoid the economic loss that may be caused by the outage situation, the prediction of an...

Cybersecurity becomes ever more important since the industry is transforming towards an Industrial Internet of Things (IIoT). Essential parts of the whole security concept are securing the communication between clients and servers on different business layers, like plant floor network and enterprise network, separation of information model and auth...