Abstract We present a word similarity gold standard for Akkadian, a language documented in ancient Mesopotamian sources from the 24th century BCE until the first century CE. The gold standard comprises 300 word pairs ranked by their paradigmatic similarity by five independently working Assyriologists. We use the gold standard to tune PMI + SVD and fastText models to improve their performance. We also present a hyper-parametrized PMI + SVD model for building count-based word embeddings, that aims to deal with the data sparsity and repetition issues encountered in Akkadian texts. Our model combines Dirichlet smoothing with context distribution smoothing, and uses context similarity weighting to down-sample distortion caused by formulaic litanies and partially or fully duplicated passages.

Abstract In this paper we present a number of digital approaches in the field of Byzantine sigillography conducted in two projects currently running at the University of Cologne. We describe how technologies and methodologies from the Digital Humanities can help overcome some of the limitations in Byzantine sigillography that result from and contribute to its status as a ‘rare subject’. Building on a long tradition and leveraging methods and techniques from the Digital Humanities, this paper describes some important steps already taken towards a digital renewal of the discipline. We are well aware that it takes much more than a locally organised group of scholars to establish any discipline anew, and so this paper aims to be a stimulus to future work.

Abstract Digital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.

Abstract With decreasing die size and the ability to store multiple bits in a single cell, resistive random access memory (ReRAM) can be used to increase storage density, making it a promising technology for the next generation of memory. However, multi-level write operations suffer from impairments such as large latency, high energy consumption, and reliability issues. In this paper, we study different mechanisms affecting the “multi-level incremental step pulse with verify algorithm” (M-ISPVA) on a 1-transistor-1-resistor (1T1R) model in transient simulation at the component and the circuit level with focus on resistance control and energy consumption during the entire process of state transitions. By dividing the M-ISPVA into a triggering and a controlling period, we discovered the transistor to operate in the ohmic region during the triggering period as a voltage-controlled resistance and in the saturation region during the controlling period as a voltage-controlled current limiter. Controlling the gate voltage in the triggering period can move the triggering point to a desired write voltage and in the controlling period can increase or decrease resistance steps per pulse to attain a desired speed of resistance change. In addition, the major energy portion is consumed for reset operation during triggering and for set operation during the controlling period. To optimize write performance, extra precaution must be taken when defining resistance states with target read-out current and gate voltage with the focus on evenly balanced latencies between all transitions. A direct multi-level write operation shows 67.5 % latency and 62.5 % energy saving compared to indirect ones, but suffers from only unidirectional control, making it non-feasible. In case of a 4 k bit memory, the more reliable M-ISPVA faces almost 37 % higher latency and energy compared to the basic ISPVA.

it -Information Technology is a strictly peer-reviewed scientific journal.It is the oldest German journal in the field of information technology.Today, the major aim of it -Information Technology is highlighting issues on ongoing newsworthy areas in information technology and informatics and their application.It aims at presenting the topics with a holistic view.It addresses scientists, graduate students, and experts in industrial research and development.it -Information Technology is the organ of the faculties Technical Informatics and Computer Science in the Life Sciences of the German Informatics Society (GI -Gesellschaft für Informatik e.V.) and faculty Technical Informatics of the Information Technology Society (ITG) within the VDE (Verband der Elektrotechnik, Elektronik und Informationstechnik e.V.). it -Information Technology was founded as Elektronische Rechenanlagen in 1959.To live up to the changes in the scientific and technological world it changed its name and shape several times

Abstract The defense of a computer network requires defenders to both understand when an attack is taking place and understand the larger strategic goals of their attackers. In this paper we explore this topic through the replication of a prior study “Extracting Attack Narratives from Traffic Datasets” by Mireles et al. [Athanasiades, N., et al., Intrusion detection testing and benchmarking methodologies, in First IEEE International Workshop on Information Assurance. 2003, IEEE: Darmstadt, Germany]. In their original research Mireles et al. proposed a framework linking a particular cyber-attack model (the Mandiant Life Cycle Model) and identification of individual attack signatures into a process as to provide a higher-level insight of an attacker in what they termed as attack narratives. In our study we both replicate the original authors work while also moving the research forward by integrating many of the suggestions Mireles et al. provided that would have improved their study. Through our analysis, we confirm the concept that attack narratives can provide additional insight beyond the review of individual cyber-attacks. We also built upon one of their suggested areas by exploring their framework through the lens of Lockheed Martin Cyber Kill Chain. While we found the concept to be novel and potentially useful, we found challenges replicating the clarity Mireles et al. described. In our research we identify the need for additional research into describing additional components of an attack narrative including the nonlinear nature of cyber-attacks and issues of identity and attribution.

Abstract This article summarizes selected aspects of a dissertation project and prior publications related to the DFG-funded RecomRatio research project. As such, it provides an end-to-end overview of a research project that aims at extracting and utilizing Emerging Knowledge represented by two concepts that we define as Emerging Named Entities and Emerging Argument Entities to support medical argumentation retrieval. We use these two concepts to model novelty in general scientific literature and, in particular, in medical argumentation. Therefore, this paper will provide an overview of Emerging Knowledge and definitions of Emerging Named Entities and Emerging Argument Entities. It includes a review of state-of-the-art and related work. A preparatory study shows that Emerging Argument Entities are in use in the medical literature. Based on the state of the art review and the preparatory study, a conceptual system design based on Emerging Named Entity Recognition and a state-of-the-art Argumentation Mining framework (ArgumenText) is introduced to extract Emerging Argument Entities from medical literature and make them available for Argument Retrieval. The conceptual system design supports two Argument Retrieval use cases: 1.) Ranking of result sets based on Emerging Argument Entities, and 2.) Highlighting Emerging Argument Entities within result sets. A case study for the extraction and visualization of Emerging Named Entities and Emerging Argument Entities is implemented based on the conceptual design. This proof-of-concept system is used to conduct technical evaluations regarding the Emerging Named Entity Recognition. Furthermore, prior results of an expert-based evaluation are presented. The article finishes with a conclusion and brief outlook of future work, e. g., supporting the Argument Interchange Format.

Abstract There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary. Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines’ instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 % for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.