A Collaborative Cybersecurity Education Program

This chapter presents an implementation of a cybersecurity education program. The program aims to address some issues identified in current cybersecurity teaching in higher education on a European level, like the fragmentation of cybersecurity expertise or resource shortage, resulting in few higher education institutions to offer full degree programs. As a result of the Erasmus+ strategic partnership project SecTech, the program tries to overcome those issues by introducing collaborative development to cybersecurity education. SecTech lays the foundations for a collaborative education program, like the definition of a clear content, module and delivery structure, and the appropriate tool support to facilitate collaboration and content reuse. Additional effort is required to achieve long-term success, including the creation of a community that drives the content creation and maintenance, as well as an independent governance structure to steer the project in the long-term. While the project focuses on European collaboration, a global community is envisioned.

VPMBench: a test bench for variant prioritization methods

Background Clinical diagnostics of whole-exome and whole-genome sequencing data requires geneticists to consider thousands of genetic variants for each patient. Various variant prioritization methods have been developed over the last years to aid clinicians in identifying variants that are likely disease-causing. Each time a new method is developed, its effectiveness must be evaluated and compared to other approaches based on the most recently available evaluation data. Doing so in an unbiased, systematic, and replicable manner requires significant effort. Results The open-source test bench “VPMBench” automates the evaluation of variant prioritization methods. VPMBench introduces a standardized interface for prioritization methods and provides a plugin system that makes it easy to evaluate new methods. It supports different input data formats and custom output data preparation. VPMBench exploits declaratively specified information about the methods, e.g., the variants supported by the methods. Plugins may also be provided in a technology-agnostic manner via containerization. Conclusions VPMBench significantly simplifies the evaluation of both custom and published variant prioritization methods. As we expect variant prioritization methods to become ever more critical with the advent of whole-genome sequencing in clinical diagnostics, such tool support is crucial to facilitate methodological research.

Covid-on-the-Web: Exploring the COVID-19 Scientific Literature through Visualization of Linked Data from Entity and Argument Mining

The unprecedented mobilization of scientists, consequent of the COVID-19 pandemics, has generated an enormous number of scholarly articles that is impossible for a human being to keep track and explore without appropriate tool support. In this context, we created the Covid-on-the-Web project, which aims to assist the access, querying, and sense making of COVID-19 related literature by combining efforts from semantic web, natural language processing, and visualization fields. Particularly, in this paper, we present (i) an RDF dataset, a linked version of the “COVID-19 Open Research Dataset” (CORD-19), enriched via entity linking and argument mining, and (ii) the “Linked Data Visualizer” (LDViz), 28 which assists the querying and visual exploration of the referred dataset. The LDViz tool assists the exploration of different views of the data by combining a querying management interface, which enables the definition of meaningful subsets of data through SPARQL queries, and a visualization interface based on a set of six visualization techniques integrated in a chained visualization concept, which also supports the tracking of provenance information. We demonstrate the potential of our approach to assist biomedical researchers in solving domain-related tasks, as well as to perform exploratory analyses through use case scenarios.

A novel identification technique of machine tool support stiffness under the variance of structural weight

The preload-dependent stiffness of machine tool support was investigated in this study. A novel identification method of support stiffness has been proposed through the experimental modal analysis and finite element method. The support stiffness was identified with different machine weight during the assembling process of a machining center. Specifically, the structure weight increase of a machine tool in the assembly process causes its center of gravity to shift. Accordingly, the variance of support reaction affects the support stiffness. To explore the variance of support stiffness, the researchers of this study collaborated with a machine tool manufacturer. Impact testing was performed on each assembly stage. Additionally, finite element analysis was used to establish equations between the reaction force versus stiffness of supports under the structural weight variance. The obtained equations were used to predict the natural frequency and vibration mode of structures in various assembly stages. The maximum error between the experimental and simulated natural frequencies was 7.1%, and the minimum modal assurance criterion was 0.77. Finally, a modal analysis model that updates support stiffness automatically, which could be adopted by machine builders to develop new machine tool, is proposed.

The mechanism of edge cutting machining of rolled iron from oxide scale

A new purifying method of metal surface cleaning from oxide scale using a special rotating edge tool is described, which allows to solve the problem of increasing the extent of surface purification from oxide scale more efficiently, reliably and effectively. The process of the surface cleaning of flat- rolled products used for the manufacture of hollow sheres from oxide scale by mechanical action of a cutting tool on various layers of oxide scale is simulated. The force required to shift an oxide scale and the boundary conditions of the ratio hold-down pressure of the tool to the workpiece, the friction force in the tool support and the impact force of the scale on the tool, at which it is possible to remove the scale from the rolled surface, are determined.

Implementation and evaluation of the DAOM framework and support tool for designing blockchain decentralized applications

Inter-organizational collaboration is an important aspect of organizational operations. Traditional systems that support organizations in executing these collaborations are inefficient, not inter-operable and insecure. Novel functions provided by blockchain technology yields the potential for addressing problems that affect organizational collaborations by enabling tamper-proof, transparent, and secure systems for the exchange of information between organizations. Still, a proper approach for building blockchain-decentralized applications (DApps) that support inter-organizational collaborations is missing. The DAOM framework addresses this gap by providing a model-driven design approach for building DApps. This paper shows the development of the semantics of the DAOM framework, implementation of the support tool, and the evaluation of the DAOM framework and support tool. We conducted an evaluation to understand the usefulness of the DAOM framework in developing blockchain DApps and the effectiveness of the support tool in producing DAOM diagram models. The evaluation result shows that the framework is useful and applicable for developing DApps for inter-organizational collaborations. Furthermore, evaluation of the tool support shows that DApps can be modelled efficiently and correctly with the implemented enterprise-modelling software.

Solver-based gradual type migration

Gradually typed languages allow programmers to mix statically and dynamically typed code, enabling them to incrementally reap the benefits of static typing as they add type annotations to their code. However, this type migration process is typically a manual effort with limited tool support. This paper examines the problem of automated type migration: given a dynamic program, infer additional or improved type annotations. Existing type migration algorithms prioritize different goals, such as maximizing type precision, maintaining compatibility with unmigrated code, and preserving the semantics of the original program. We argue that the type migration problem involves fundamental compromises: optimizing for a single goal often comes at the expense of others. Ideally, a type migration tool would flexibly accommodate a range of user priorities. We present TypeWhich, a new approach to automated type migration for the gradually-typed lambda calculus with some extensions. Unlike prior work, which relies on custom solvers, TypeWhich produces constraints for an off-the-shelf MaxSMT solver. This allows us to easily express objectives, such as minimizing the number of necessary syntactic coercions, and constraining the type of the migration to be compatible with unmigrated code. We present the first comprehensive evaluation of GTLC type migration algorithms, and compare TypeWhich to four other tools from the literature. Our evaluation uses prior benchmarks, and a new set of "challenge problems." Moreover, we design a new evaluation methodology that highlights the subtleties of gradual type migration. In addition, we apply TypeWhich to a suite of benchmarks for Grift, a programming language based on the GTLC. TypeWhich is able to reconstruct all human-written annotations on all but one program.