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Industrial Control Systems (ICS) succumb to an ever evolving variety of threats. Additionally, threats are increasing in number and get more complex. This requires a holistic and up-to-date security concept for ICS as a whole. Usually security concepts are applied and updated based on regularly performed ICS security assessments. Such ICS security assessments require high effort and extensive knowledge about ICS and its security. This is often a problem for small and mediumsized enterprises (SME), which do not have sufficient respective sufficiently skilled human resources. This paper defines in a first step requirements on the knowledge needed to perform an ICS security assessment and the life cycle of this knowledge. Afterwards the ICS security knowledge and its life cycle are developed and discussed considering the requirements and related work.
In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics.
Integrating distributional and lexical information for semantic classification of words using MRMF
(2016)
Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multirelational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
Editorial for the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016)
(2016)
Knowledge Organization Systems (KOS), in the form of classification systems, thesauri, lexical databases, ontologies, and taxonomies, play a crucial role in digital information management and applications generally. Carrying semantics in a well-controlled and documented way, Knowledge Organisation Systems serve a variety of important functions: tools for representation and indexing of information and documents, knowledge-based support to information searchers, semantic road maps to domains and disciplines, communication tool by providing conceptual framework, and conceptual basis for knowledge based systems, e.g. automated classification systems. New networked KOS (NKOS) services and applications are emerging, and we have reached a stage where many KOS standards exist and the integration of linked services is no longer just a future scenario. This editorial describes the workshop outline and overview of presented papers at the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) in Hannover, Germany.
Discovery and efficient reuse of technology pictures using Wikimedia infrastructures. A proposal
(2016)
Multimedia objects, especially images and figures, are essential for the visualization and interpretation of research findings. The distribution and reuse of these scientific objects is significantly improved under open access conditions, for instance in Wikipedia articles, in research literature, as well as in education and knowledge dissemination, where licensing of images often represents a serious barrier.
Whereas scientific publications are retrievable through library portals or other online search services due to standardized indices there is no targeted retrieval and access to the accompanying images and figures yet. Consequently there is a great demand to develop standardized indexing methods for these multimedia open access objects in order to improve the accessibility to this material.
With our proposal, we hope to serve a broad audience which looks up a scientific or technical term in a web search portal first. Until now, this audience has little chance to find an openly accessible and reusable image narrowly matching their search term on first try - frustratingly so, even if there is in fact such an image included in some open access article.
The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.
Das ProFormA-Aufgabenformat wurde eingeführt, um den Austausch von Programmieraufgaben zwischen beliebigen Autobewertern (Grader) zu ermöglichen. Ein Autobewerter führt im ProFormA-Aufgabenformat spezifizierte „Tests“ sequentiell aus, um ein vom Studierenden eingereichtes Programm zu prüfen. Für die Strukturierung und Darstellung der Testergebnisse existiert derzeit kein graderübergreifender Standard. Wir schlagen eine Erweiterung des ProFormA-Aufgabenformats um eine Hierarchie von Bewertungsaspekten vor, die nach didaktischen Aspekten gruppiert ist und entsprechende Testausführungen referenziert. Die Erweiterung wurde in Graja umgesetzt, einem Autobewerter für Java-Programme. Je nach gewünschter Detaillierung der Bewertungsaspekte sind Testausführungen in Teilausführungen aufzubrechen. Wir illustrieren unseren Vorschlag mit den Testwerkzeugen Compiler, dynamischer Softwaretest, statische Analyse sowie unter Einsatz menschlicher Bewerter.
Smart Cities require reliable means for managing installations that offer essential services to the citizens. In this paper we focus on the problem of evacuation of smart buildings in case of emergencies. In particular, we present an abstract architecture for situation-aware evacuation guidance systems in smart buildings, describe its key modules in detail, and provide some concrete examples of its structure and dynamics.