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While the role of and consequences of being a bystander to face-to-face bullying has received some attention in the literature, to date, little is known about the effects of being a bystander to cyberbullying. It is also unknown how empathy might impact the negative consequences associated with being a bystander of cyberbullying. The present study focused on examining the longitudinal association between bystander of cyberbullying depression, and anxiety, and the moderating role of empathy in the relationship between bystander of cyberbullying and subsequent depression and anxiety. There were 1,090 adolescents (M-age = 12.19; 50% female) from the United States included at Time 1, and they completed questionnaires on empathy, cyberbullying roles (bystander, perpetrator, victim), depression, and anxiety. One year later, at Time 2, 1,067 adolescents (M-age = 13.76; 51% female) completed questionnaires on depression and anxiety. Results revealed a positive association between bystander of cyberbullying and depression and anxiety. Further, empathy moderated the positive relationship between bystander of cyberbullying and depression, but not for anxiety. Implications for intervention and prevention programs are discussed.
Business processes constantly generate, manipulate, and consume data that are managed by organizational databases. Despite being central to process modeling and execution, the link between processes and data is often handled by developers when the process is implemented, thus leaving the connection unexplored during the conceptual design. In this paper, we introduce, formalize, and evaluate a novel conceptual view that bridges the gap between process and data models, and show some kinds of interesting insights that can be derived from this novel proposal.
Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing, and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM (Long Short-Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long-term visual-language interactions by making use of history and future context information at high-level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different ways to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale, and vertical mirror are proposed to prevent over-fitting in training deep models. To understand how our models "translate" image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO, and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model). Our experiments also prove that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate the performance of transfer learning of the Bi-LSTM model significantly outperforms previous methods on the Pascal1K dataset.
The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.
The Potsdam answer set solving collection, or Potassco for short, bundles various tools implementing and/or applying answer set programming. The article at hand succeeds an earlier description of the Potassco project published in Gebser et al. (AI Commun 24(2):107-124, 2011). Hence, we concentrate in what follows on the major features of the most recent, fifth generation of the ASP system clingo and highlight some recent resulting application systems.
Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)]
Applications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk and resources of the data center without affecting the service quality of applications.
We introduce a type and effect system, for an imperative object calculus, which infers sharing possibly introduced by the evaluation of an expression, represented as an equivalence relation among its free variables. This direct representation of sharing effects at the syntactic level allows us to express in a natural way, and to generalize, widely-used notions in literature, notably uniqueness and borrowing. Moreover, the calculus is pure in the sense that reduction is defined on language terms only, since they directly encode store. The advantage of this non-standard execution model with respect to a behaviorally equivalent standard model using a global auxiliary structure is that reachability relations among references are partly encoded by scoping. (C) 2018 Elsevier B.V. All rights reserved.
While the role of and consequences of being a bystander to face-to-face bullying has received some attention in the literature, to date, little is known about the effects of being a bystander to cyberbullying. It is also unknown how empathy might impact the negative consequences associated with being a bystander of cyberbullying. The present study focused on examining the longitudinal association between bystander of cyberbullying depression, and anxiety, and the moderating role of empathy in the relationship between bystander of cyberbullying and subsequent depression and anxiety. There were 1,090 adolescents (M-age = 12.19; 50% female) from the United States included at Time 1, and they completed questionnaires on empathy, cyberbullying roles (bystander, perpetrator, victim), depression, and anxiety. One year later, at Time 2, 1,067 adolescents (M-age = 13.76; 51% female) completed questionnaires on depression and anxiety. Results revealed a positive association between bystander of cyberbullying and depression and anxiety. Further, empathy moderated the positive relationship between bystander of cyberbullying and depression, but not for anxiety. Implications for intervention and prevention programs are discussed.
Parsing of argumentative structures has become a very active line of research in recent years. Like discourse parsing or any other natural language task that requires prediction of linguistic structures, most approaches choose to learn a local model and then perform global decoding over the local probability distributions, often imposing constraints that are specific to the task at hand. Specifically for argumentation parsing, two decoding approaches have been recently proposed: Minimum Spanning Trees (MST) and Integer Linear Programming (ILP), following similar trends in discourse parsing. In contrast to discourse parsing though, where trees are not always used as underlying annotation schemes, argumentation structures so far have always been represented with trees. Using the 'argumentative microtext corpus' [in: Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon 2015 / Vol. 2, College Publications, London, 2016, pp. 801-815] as underlying data and replicating three different decoding mechanisms, in this paper we propose a novel ILP decoder and an extension to our earlier MST work, and then thoroughly compare the approaches. The result is that our new decoder outperforms related work in important respects, and that in general, ILP and MST yield very similar performance.
Virtual 3D city models represent and integrate a variety of spatial data and georeferenced data related to urban areas. With the help of improved remote-sensing technology, official 3D cadastral data, open data or geodata crowdsourcing, the quantity and availability of such data are constantly expanding and its quality is ever improving for many major cities and metropolitan regions. There are numerous fields of applications for such data, including city planning and development, environmental analysis and simulation, disaster and risk management, navigation systems, and interactive city maps.
The dissemination and the interactive use of virtual 3D city models represent key technical functionality required by nearly all corresponding systems, services, and applications. The size and complexity of virtual 3D city models, their management, their handling, and especially their visualization represent challenging tasks. For example, mobile applications can hardly handle these models due to their massive data volume and data heterogeneity. Therefore, the efficient usage of all computational resources (e.g., storage, processing power, main memory, and graphics hardware, etc.) is a key requirement for software engineering in this field. Common approaches are based on complex clients that require the 3D model data (e.g., 3D meshes and 2D textures) to be transferred to them and that then render those received 3D models. However, these applications have to implement most stages of the visualization pipeline on client side. Thus, as high-quality 3D rendering processes strongly depend on locally available computer graphics resources, software engineering faces the challenge of building robust cross-platform client implementations.
Web-based provisioning aims at providing a service-oriented software architecture that consists of tailored functional components for building web-based and mobile applications that manage and visualize virtual 3D city models. This thesis presents corresponding concepts and techniques for web-based provisioning of virtual 3D city models. In particular, it introduces services that allow us to efficiently build applications for virtual 3D city models based on a fine-grained service concept. The thesis covers five main areas:
1. A Service-Based Concept for Image-Based Provisioning of
Virtual 3D City Models It creates a frame for a broad range of services related to the rendering and image-based dissemination of virtual 3D city models.
2. 3D Rendering Service for Virtual 3D City Models This service provides efficient, high-quality 3D rendering functionality for virtual 3D city models. In particular, it copes with requirements such as standardized data formats, massive model texturing, detailed 3D geometry, access to associated feature data, and non-assumed frame-to-frame coherence for parallel service requests. In addition, it supports thematic and artistic styling based on an expandable graphics effects library.
3. Layered Map Service for Virtual 3D City Models It generates a map-like representation of virtual 3D city models using an oblique view. It provides high visual quality, fast initial loading times, simple map-based interaction and feature data access. Based on a configurable client framework, mobile and web-based applications for virtual 3D city models can be created easily.
4. Video Service for Virtual 3D City Models It creates and synthesizes videos from virtual 3D city models. Without requiring client-side 3D rendering capabilities, users can create camera paths by a map-based user interface, configure scene contents, styling, image overlays, text overlays, and their transitions. The service significantly reduces the manual effort typically required to produce such videos. The videos can automatically be updated when the underlying data changes.
5. Service-Based Camera Interaction It supports task-based 3D camera interactions, which can be integrated seamlessly into service-based visualization applications. It is demonstrated how to build such web-based interactive applications for virtual 3D city models using this camera service.
These contributions provide a framework for design, implementation, and deployment of future web-based applications, systems, and services for virtual 3D city models. The approach shows how to decompose the complex, monolithic functionality of current 3D geovisualization systems into independently designed, implemented, and operated service- oriented units. In that sense, this thesis also contributes to microservice architectures for 3D geovisualization systems—a key challenge of today’s IT systems engineering to build scalable IT solutions.
The rapid development and integration of Information Technologies over the last decades influenced all areas of our life, including the business world. Yet not only the modern enterprises become digitalised, but also security and criminal threats move into the digital sphere. To withstand these threats, modern companies must be aware of all activities within their computer networks.
The keystone for such continuous security monitoring is a Security Information and Event Management (SIEM) system that collects and processes all security-related log messages from the entire enterprise network. However, digital transformations and technologies, such as network virtualisation and widespread usage of mobile communications, lead to a constantly increasing number of monitored devices and systems. As a result, the amount of data that has to be processed by a SIEM system is increasing rapidly. Besides that, in-depth security analysis of the captured data requires the application of rather sophisticated outlier detection algorithms that have a high computational complexity. Existing outlier detection methods often suffer from performance issues and are not directly applicable for high-speed and high-volume analysis of heterogeneous security-related events, which becomes a major challenge for modern SIEM systems nowadays.
This thesis provides a number of solutions for the mentioned challenges. First, it proposes a new SIEM system architecture for high-speed processing of security events, implementing parallel, in-memory and in-database processing principles. The proposed architecture also utilises the most efficient log format for high-speed data normalisation. Next, the thesis offers several novel high-speed outlier detection methods, including generic Hybrid Outlier Detection that can efficiently be used for Big Data analysis. Finally, the special User Behaviour Outlier Detection is proposed for better threat detection and analysis of particular user behaviour cases.
The proposed architecture and methods were evaluated in terms of both performance and accuracy, as well as compared with classical architecture and existing algorithms. These evaluations were performed on multiple data sets, including simulated data, well-known public intrusion detection data set, and real data from the large multinational enterprise. The evaluation results have proved the high performance and efficacy of the developed methods.
All concepts proposed in this thesis were integrated into the prototype of the SIEM system, capable of high-speed analysis of Big Security Data, which makes this integrated SIEM platform highly relevant for modern enterprise security applications.
Remote sensing technology, such as airborne, mobile, or terrestrial laser scanning, and photogrammetric techniques, are fundamental approaches for efficient, automatic creation of digital representations of spatial environments. For example, they allow us to generate 3D point clouds of landscapes, cities, infrastructure networks, and sites. As essential and universal category of geodata, 3D point clouds are used and processed by a growing number of applications, services, and systems such as in the domains of urban planning, landscape architecture, environmental monitoring, disaster management, virtual geographic environments as well as for spatial analysis and simulation.
While the acquisition processes for 3D point clouds become more and more reliable and widely-used, applications and systems are faced with more and more 3D point cloud data. In addition, 3D point clouds, by their very nature, are raw data, i.e., they do not contain any structural or semantics information. Many processing strategies common to GIS such as deriving polygon-based 3D models generally do not scale for billions of points. GIS typically reduce data density and precision of 3D point clouds to cope with the sheer amount of data, but that results in a significant loss of valuable information at the same time.
This thesis proposes concepts and techniques designed to efficiently store and process massive 3D point clouds. To this end, object-class segmentation approaches are presented to attribute semantics to 3D point clouds, used, for example, to identify building, vegetation, and ground structures and, thus, to enable processing, analyzing, and visualizing 3D point clouds in a more effective and efficient way. Similarly, change detection and updating strategies for 3D point clouds are introduced that allow for reducing storage requirements and incrementally updating 3D point cloud databases. In addition, this thesis presents out-of-core, real-time rendering techniques used to interactively explore 3D point clouds and related analysis results. All techniques have been implemented based on specialized spatial data structures, out-of-core algorithms, and GPU-based processing schemas to cope with massive 3D point clouds having billions of points.
All proposed techniques have been evaluated and demonstrated their applicability to the field of geospatial applications and systems, in particular for tasks such as classification, processing, and visualization. Case studies for 3D point clouds of entire cities with up to 80 billion points show that the presented approaches open up new ways to manage and apply large-scale, dense, and time-variant 3D point clouds as required by a rapidly growing number of applications and systems.
Einsatz einer mobilen Lern-App - Ein Werkzeug zur Verbesserung von klinisch-praktischem Unterricht
(2018)
Der Unterricht am Krankenbett ist im Medizinstudium eine wertvolle Möglichkeit klinisch-praktische Fertigkeiten zu erlernen. Eine optimale Vorbereitung der Studierenden ist dabei Voraussetzung. Eine mobile Lern-App wurde entwickelt, die den Studierenden, neben Lernzielen, Kursinhalte und Anleitungen zu Untersuchungstechniken bietet, um die Vorbereitung auf einen klinisch-praktischen Kurs zu fördern und Kurzinformationen auch während des Kurses zur Verfügung zu stellen. 175 Studierende hatten die Möglichkeit die App parallel zu einem klinischen Untersuchungs-Kurs im Semester zu nutzen. Im Anschluss beantworteten die Studierenden einen Fragebogen zur Nützlichkeit und Vielseitigkeit der App und zur Zufriedenheit mit der App unter Verwendung eine 5-Punkt-Likert-Skala und zwei offenen Fragen. In diesem Beitrag wird das Kurskonzept zusammen mit der Lern-App, die Ergebnisse aus dem Fragebogen und unsere Schlussfolgerungen daraus vorgestellt. Studierende bewerteten die App grundsätzlich als hilfreich. Sie sollte dabei gründlich eingeführt werden. Patienten sollten über die Nutzung von Smartphones im Studentenunterricht zu Lernzwecken informiert werden.
Das Training sozioemotionaler Kompetenzen ist gerade für Menschen mit Autismus nützlich. Ein solches Training kann mithilfe einer spielbasierten Anwendung effektiv gestaltet werden. Zwei Minispiele, Mimikry und Emo-Mahjong, wurden realisiert und hinsichtlich User Experience evaluiert. Die jeweiligen Konzepte und die Evaluationsergebnisse sollen hier vorgestellt werden.
Die Veröffentlichung eines global frei verfügbaren Onlinekurses abseits der großen MOOC Plattformen bringt spezielle Herausforderungen mit sich. Neben technischen Herausforderungen sind eine effiziente Wissensvermittlung und die Erhaltung der Lernmotivation zentral. Der folgende Beitrag stellt Techniken zur Steigerung der Lerneffizienz und -motivation anhand des ARCS Modells vor. Er zeigt auf, wie die verschiedenen Techniken in der Entwicklung des Onlinekurses „Designing Sustainable Food Systems“ umgesetzt wurden und inwieweit sie erfolgreich waren.