Gasification of biomass is known for its valuable capabilities in small, flexible and decentral applications for cogeneration of power and heat. However, the produced raw syngas contains a variety of unwanted higher hydrocarbons such as aromatics and tar compounds in varying amounts adversely for a stable operation of such gasification plants. This cooperative project between the University of Bayreuth (CVT, ZET), the UAS Hof and the company WS Wärmeprozesstechnik aims to develop a new downdraft biomass gasification technology. Herein, the main innovation is the use of the charcoal directly formed by biomass pyrolysis in the gasifier as an adsorption agent for the unwanted tar components in a subsequent cooled adsorption section. The technology with regard to the formation of a suitable adsorption agent is in analogy to the common production of activated carbon by treatment of charcoal with CO2 and/or H2O at temperatures of around 900°C to achieve a high surface area. But here, the activated carbon is directly produced within the downdraft gasification reactor by the controlled, i.e. uncompleted conversion of the biomass coke formed by pyrolysis. Hence, the coke is only partly gasified to syngas and the residual amount is used as activated carbon for tar adsorption. By subsequent cooling of the activated charcoal and the raw product gas to a temperature of 70°C suitable for adsorption, the tar components are almost completely adsorbed and removed from the syngas to make it usable for cogeneration without further treatment. To gain all relevant informations about the main parameters of the overall process (char formation/conversion, formation and subsequent adsorption of tar), respective measurements were conducted in order to finally create a reliable numerical model of the process. The respective results, e.g. regarding kinetic data of char conversion in the gasification unit and adsorption data achieved with naphthalene as a model substance for the tar, will be presented.

In this work, honeypots were set up on several public cloud infrastructures of Amazon, Microsoft and Google located in different regions around the world, including North America, Asia and Europe. The honeypots, simulating different popular services like SSH and VNC, were used to collect data over a period of two month, resulting in over 170 million log entries. Further analysis of the log entries regarding attack patterns and geographic characteristics are presented in this paper. For example, the attacks originated from 216 countries involving 268,614 unique IPs, dominated by China with a share of 25.83%.

Given the importance of an early anomaly detection, Intrusion Detection Systems (IDSs) are introduced in Supervisory Control And Data Acquisition (SCADA). Agents or probes form the cornerstone of any IDS by capturing network packets and extracting relevant information. However, IDSs are facing unprecedented challenges due to the escalation in the number, scale and diversity of attacks. Software-Defined Network (SDN) then comes into play and can provide the required flexibility and scalability. Building on that, we introduce Traffic Agent Controllers (TACs) that monitor SDN-enabled switches via Open-Flow. By using lightweight statistical metrics such as Kullback-Leibler Divergence (KLD), we are able to detect the slightest anomalies, such as stealth port scans, even in the presence of background traffic. The obtained metrics can also be used to locate the anomalies with precision over 90% inside a hierarchical network topology.

The main objectives of the Intelligent Transportation Systems (ITS) vision is to improve road safety, traffic management, and mobility by enabling cooperative communication among participants. This vision requires the knowledge of the current state of the road traffic, which can be obtained by collecting Floating Car Data (FCD) information using Dedicated Short-Range Communication (DSRC) based on the IEEE 802.11p standard. Most of the existing FCD collection protocols have been evaluated via simulations and mathematical models, while the real-world implications have not been thoroughly investigated. This paper presents an open-source implementation of two state-of-the-art FCD collection algorithms, namely BASELINE and DISCOVER. These algorithms are implemented in an open-source vehicular prototyping platform and validated in a real-world experimental setup.

It is our great pleasure to welcome you to the 30th Anniversary ACM Conference on Hypertext and Social Media at Hof University, Germany, on September 17-20, 2019. The ACM Hypertext conference is a premium venue for high quality peer-reviewed research on hypertext theory, systems and applications. It is concerned with all aspects of modern hypertext research including social media, semantic web, dynamic and computed hypertext and hypermedia as well as narrative systems and applications. The theme of Hypertext 2019 is "HYPERTEXT - TEAR DOWN THE WALL". This motto of the 30th ACM Hypertext conference goes hand in hand with the 30th anniversary of the fall of the Berlin Wall. Inspired by the historical events in Germany, Hypertext 2019 aims at reunifying different hypertext research directions and communities. Therefore, apart from the regular research tracks, Hypertext 2019 will feature a research track on 30 Years of Hypertext as well as an exhibition/creative track. 2019 will also be the 30th anniversary of the WWW. It is a perfect time to join in, reflect our common roots, and discuss how we can jointly address our current and future challenges. The conference will take place at the Institute of Information Systems (iisys) at Hof University, Germany. Hof lies midway between Frankfurt and Prague, Munich and Berlin and is very close to the former German-German border, in particular to the village of Modlareuth, called "little Berlin", which used to be divided by a wall. After exactly 20 years, Hypertext 2019 will take place in Germany again for the 2nd time. Hypertext 2019 is co-locating with the ACM Document Engineering Conference (DocEng'19) organized in Berlin, Germany, between September 23-16.

Welcome to the Human Factors in Hypertext 2019 workshop (HUMAN'19) in Hof, Germany, the second workshop of a young series of workshops for the ACM Conference on Hypertext and Social Media. It has a strong focus on hypertext users and thus complements the machine analytics research that we experienced in previous conferences. The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains. Furthermore, the workshop raises the question of how original hypertext ideas (e.g., Doug Engelbart's "augmenting human intellect" or Frank Halasz' "hypertext as a medium for thinking and communication") can improve today's hypertext systems. Historically, hypertext research strongly connects to human factors. Hypertext pioneers, such as Doug Engelbart or Ted Nelson, focused on the usage of and interaction with hypertext. This workshop combines original hypertext research ideas with recent hypertext research trends. In addition, it consolidates different hypertext research areas from the viewpoint of human factors. Thus, HUMAN'19 fosters cross-cutting discussions and the development of new ideas.

Erfolg wird sich in der Digitalen Transformation nicht von allein einstellen, sondern hängt vor allem von der intelligenten Ausgestaltung einiger weniger Kernaspekte ab. Dies ist bereits durch die ersten beiden Artikel zur "Digitalen Transformation im Mittelstand" deutlich geworden, in denen den Faktoren "Mensch" und "Prozess" eine herausragende Bedeutung zugesprochen wurden. Den Grundpfeiler einer erfolgreichen Digitalen Transformation werden jedoch noch sehr viel mehr als bisher die dem Unternehmen zur Verfügung stehenden "Daten" bilden, die im Zentrum dieses dritten und abschließenden Teils der Artikelserie stehen.

Historically, there has been a tendency to consider hypertext as a type of system, perhaps characterized by provision of links or other structure to users. In this paper, we consider hypertext as a method of inquiry, a way of viewing arbitrary systems. In this view, what are traditionally called "navigational hypertext systems" might be considered as information retrieval systems, "spatial hypertext systems" as brainstorming systems, etc., while their "hypertext" nature results from the way in which such systems are conceived, developed, and/or presented. The benefit of such a shift is the ability to apply this hypertextual method of inquiry to systems not normally considered part of the hypertext community. In this paper, we specifically apply this view to artificial intelligence, and examine how this application can be productive.

The task of organizing and retrieving knowledge is often elaborative and involves different types of media including digital or analog. In this paper we describe a system that is based on related research in the fields of spatial hypertext, information retrieval, and visualization. It utilizes a 2D space on which users can add, remove, or manipulate information entities (so-called user nodes) visually. A spatial parser recognizes the evolving structure and queries a knowledge base for helpful other information entities (so-called suggestions nodes). Similar to user nodes, those suggestions are presented as visual objects in the space. We propose a physics model to simulate their behavior. Their characteristics encode the relevance of suggestions to user nodes and to each other. This enables human recipients to interpret the given visual clues and, thus, identify information of interest. The way users organize nodes spatially influences the parsed spatial structures, i.e., the placement of suggestion nodes. This allows the creation of complex queries without any prior knowledge, yet the users do not have to be aware of that, because they can express their thoughts implicitly by manipulating their nodes. We discuss the strengths of a physics based simulation to encode context visually and point to open issues and potential solutions. On the basis of an implemented demonstrator we show the benefits compared to similar and related applications in the field of information visualization, especially when it comes to tasks where a high portion of creativity is involved and the information space is not well known.

In this paper, we describe our project DemoMedia, a software demonstrator that combines hypertext and recommender functionality in the context of video acquisition or use. DemoMedia fills the gap that exists in today's video platforms which include recommender functionalities, but only trivial support for users to structure information. Thus, users are forced to write down notes from or about videos (needed for various reasons) on additional media, such as paper. This opens a media gap between video platform and note-taking or communication to others. DemoMedia becomes a note taking and communication tool for the user, as it offers a knowledge space on which users can freely arrange and associate information. Furthermore, its intelligent parsers compute relations that are implicitly expressed and queries knowledge bases for relevant information or related videos. Those get positioned on the space in a semantically meaningful way. DemoMedia and the underlying component-based open hypermedia system Mother combine both the machine's capability of extracting knowledge from huge amounts of data and the human capability of sensemaking, intuition, and creativity.

In the context of the Internet of Things (IoT), there is the need to manage huge amounts of time series sensor data, if high frequency device monitoring and predictive analytics are targeted for improving the overall process quality in production or supervision of quality management. The key challenge here is to be able to collect, transport, store and retrieve such high frequency data from multiple sensors with minimum resource usage, as this allows to scale such systems with low costs. For evaluating the performance impact of such an IoT scenario, we produce 1000 datasets per second for five sensors. We send them to three different types of popular database management systems (i.e., MariaDB, MongoDB and InfluxDB) and measure the resource impacts of the writing and reading operations over the whole processing pipeline. These measurements are CPU usage, network usage, disk performance and usage, and memory usage results plus a comparison of the difficulty for the developers to engineer such a processing pipeline. In the end, we have a recommendation depending on the needs, which database management system is best suited for processing high frequency sensor data in an IoT context.

In addition to the volatility of the market demand and shorter product lifecycles, nowadays manufacturing companies have to deal with another market dimension, namely the digitization in context of Industry 4.0. This also offers a huge potential to handle internal and external constraints, such as globalization, customization and flexibility. In this context, the following paper presents a methodology to visualize and analyze the information processes along a company’s internal value chain. This enables the holistic identification and assessment of information processes and offers a basis for target-oriented need for action with a qualitative and quantitative analysis to achieve a sustainable competitiveness. Based on the results of the analysis, selected optimization actions can be applied more effectively. The application of the developed methodology is demonstrated in a use case within a manufacturing company.

We investigate the amount of information leakage through unencrypted metadata in Android's file-based encryption (FBE) which was introduced as an alternative to the previously dominating full-disk encryption (FDE) in Android 7.0. We propose a generic method, and provide appropriate tooling, to reconstruct forensic events on Android smartphones encrypted with FBE. Based on a dataset of 3903 applications, we show that metadata of files can be used to reconstruct the name, version and installation date of all installed apps. Furthermore, we show that, depending on a specific app, information leakages through metadata can even be used to reconstruct a user's behavior. For the example of WhatsApp, we show that the point of time a user sent or received her last message can be traced back even though the phone was encrypted. Our approach requires access to the raw data of an encrypted disk only but does not require access to a powered-on device or the bootloader, such as known attacks against FDE including cold boot and evil maid. We conclude that FBE is significantly more insecure than FDE and was presumably elected for usability reasons like direct boot.