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For the current requirements in knowledge and technology transfer, solutions exist that focus exclusively on certain areas and no longer do justice to today's complexity. For example, research information systems primarily offer a marketing function, and transfer and idea portals offer a possibility for collecting and exploiting ideas that requires a great deal of human resources. This paper deals with the design of a framework that links existing systems and approaches in a meaningful way and, based on the findings of innovation research, improves the co-creative collaboration of actors from science, business and society in an it-based way. Therefore, the authors propose the CCDTP-framework based on a requirements analysis.

This contribution focuses on a newly developed “Framework for Iterative Data Cleansing and Migration” (FiDM), which describes a process for achieving a quality-assured research information system, which could be implemented as a prototype. The open source solution VIVO, an ontology-based linked data software, was used to test the applicability of FiDM and to identify key requirements and findings for the general migration process. The use of FiDM supports public institutions in the partial standardization of the research management process and the integration of existing research data as well as metadata using appropriate research information systems. While ensuring data quality, networking between science, industry and the public can be established and the visibility of research achievements can be increased. FiDM was implemented and evaluated as an example at University of Applied Sciences Mittweida as part of the migration process to the RIS VIVO.

Computer and video games are the subject of intensive and wide-ranging research within the scientific community. The spectrum ranges from the analyses of the effects of games on the propensity to violence, well-being or on the the learning process. Previous works largely concluded positive effects of games on the learning behaviour, particularly at the motivational level. However, the analysis has mainly focused on specially designed educational games, not on games from the entertainment sector that are established on the market. Here, we address this gap by identifying suitable games and examining them for intersections with a catalogue of economical learning objectives. For this purpose, this catalogue is transformed into an adapted criteria catalogue and examined on the basis of five selected games. The main findings include that on average there is a 56% thematic overlap between the learning objectives catalogue, while 18% of the objectives were explained in a way that an abstracted application is possible even outside the gaming environment. Those findings highlight the potential value of these games for an application within the educational sector while suggesting the necessity for adjustments and additions to better address the objectives.

The shift towards digital teaching is leading to an increased need for interactive teaching methods. Game-Based Learning combines teaching content with a motivating application. The learning simulation Finanzmars uses elements from Game-Based Learning to prepare the contents of a classical lecture in the field of economics for a learning game. The simulation is aimed at students of business administration or similar courses of study. The contents can be edited and extended directly by the teaching staff using an external configuration tool. The knowledge transfer is flanked by Micro Learning, systematic introductions to increasingly complex subjects, interaction with game elements and increased motivation through in-game successes such as a premium currency. The player finds himself in an economic simulation with the goal of exploiting the resources of Mars in a profitable and optimized way. In the game, construction, upgrading and repair of buildings, the development of future technology through research, the export of resources and expansion to other celestial bodies are available. The functionalities are based on possible courses of action in the real world, whose mechanisms play a central role in the lecture on the topic of finance. By means of logging and the evaluation of in-game objectives, the teachers can track the progress of the students. Our evaluation of 12 Master's students in Business Administration shows that there is an increase in economic expertise. Furthermore, the evaluation of the associated standardized AttrakDiff questionnaire according to DIN EN ISO 9241–11 certifies that the application is action-oriented and user-friendly. The description of the environment with regard to the course of studies and the teaching concept was largely done in the publication “Finanzmars im Kosmos von Blended Learning” by Marc Ritter, Christian Roschke and Volker Tolkmitt, who received the Best Paper Award in Teaching at the CARF Lucerne Conference in 2019. This publication focuses on the systematic elaboration of game design and implementation with a view to increasing learning success.

Current research in the domain of video activity detection focuses on real-time activity detection. This includes multiple approaches in the mobile environment, such as the detection of correct motion sequences in the sports and health area or in safety-relevant environments. Current trends focus on the use of 3D CNNs. This work describes a approach to combine the results of a human skeleton point detector with an LSTM on mobile devices. Frameworks for pose detection are combined with LSTMs for activity detection with sensor data, optimized for the mobile area. The resulting system allows the direct detection of a person pose and the classification of activities in a video recorded with a smartphone. The successful application of the system in several field tests shows that the described approach works in principle and can be transferred to a resource-limited mobile environment by optimization.

The analysis of video footage regarding the identification of persons at defined locations or the detection of complex activities is still a challenging process. Nowadays, various (semi-)automated systems can be used to overcome different parts of these challenges. Object detection and their classification reach even higher detection rates when making use of the latest cutting-edge convolutional neural network frameworks. Integrated into a scalable infrastructure as a service data base system, we employ the combination of such networks by using the Detectron framework within Docker containers with case-specific engineered tracking and motion pattern heuristics in order to detect several activities with comparatively low and distributed computing efforts and reasonable results.

The recent technological advancements have shed light on some futuristic inventions and possibilities. One of these inventions and technologies has been home automation especially due to the rise of Internet of Things. One of the main areas of advancement in home automation has been Voice-operated speakers. Given the surprising success of the Amazon Echo ‐‐ a smart speaker that responds to your voice commands, plays music, and controls your smart home ‐‐ this area has become a competitive one. With Google Home entering the arena, backed by the company's ubiquitous search engine, the Echo's place on top is no longer secure. This growth will, evidently, increase the poll of sellers and would make the decision-making process an arduous one, both, for producers and the buyers of these products. This study looks into the decision-making criteria for the selection of the best voice operated speaker by consumers in order to facilitate the decision-making process involving smart, voice activated speakers. The Hierarchical Decision Model (HDM) has been used to establish a model based on perspectives, criteria, and alternatives. Furthermore, with the aim of better demonstrating the practicality of the study, five real voice operated speaker products are evaluated based on the perspectives and criteria weights obtained from the HDM model and scores.

Modern attacks are using sophisticated and innovative techniques. The utilization of cryptography, self-modified code, and integrated attack frameworks provide more possibilities to circumvent most existing perimeter security approaches, such as firewalls and IDS. Even Application Layer Gateways (ALG) which enforce the most restrictive network access can be exploited by using advanced attack techniques. In this paper, we propose a new attack for circumventing ALGs. By using polymorphic and encrypted shellcode, multiple shellcode stages, protocol compliant and encrypted shell tunneling, and reverse channel discovery techniques, we are able to effectively bypass ALGs. The proposed attack consists of four phases with certain requirements and results. We implemented the initial shellcode as well as the different stages and conducted the practical attack using an existing ALG. The possibility to prevent this attack with existing approaches is discussed and further research in the area of perimeter security and log management is motivated.

In response to the emerging deployment of IPv6 on network devices, this paper proposes the integration of IPv6 on Lock-Keeper, an implementation of a high level security system for preventing online attacks. It is designed to permit the secure data exchange over physically separated networks in an IPv4-based environment. A new intercommunication module is added to manage IPv4/IPv6 handoff inside the Lock-Keeper, which provides several benefits. First, the Lock-Keeper gains the flexibility to work in IPv4/IPv6 environments. Second, an application layer gateway to bridge IPv4 and IPv6 networks is achieved. Third, the IP-layer protocol isolation is realized inside the Lock-Keeper to enhance the security of the protected network by exchanging data between physically separated networks using different IP protocols.

Intrusion Detection Systems (IDS) have been widely deployed in practice for detecting malicious behavior on network communication and hosts. The problem of false-positive alerts is a popular existing problem for most of IDS approaches. The solution to address this problem is correlation and clustering of alerts. To meet the practical requirements, this process needs to be finished as soon as possible, which is a challenging task as the amount of alerts produced in large scale deployments of distributed IDS is significantly high. We identify the data storage and processing algorithms to be the most important factors influencing the performance of clustering and correlation. We propose and implement the utilization of memory-supported algorithms and a column-oriented database for correlation and clustering in an extensible IDS correlation platform. The utilization of the column-oriented database, an In-Memory Alert Storage, and memory-based index tables leads to significant improvements on the performance. Different types of correlation modules can be integrated and compared on this platform. A plugin concept for Receivers provides flexible integration of various sensors and additional IDS management systems. The platform can be distributed over multiple processing units to share memory and processing power. A standardized interface is designed to provide a unified view of result reports for end users. The efficiency of the proposed platform is tested by practical experiments with several alert storage approaches, different simple algorithms, as well as local and distributed deployment.

Intrusion Detection Systems (IDS) have been used widely to detect malicious behavior in network communication and hosts. IDS management is an important capability for distributed IDS solutions, which makes it possible to integrate and handle different types of sensors or collect and synthesize alerts generated from multiple hosts located in the distributed environment. Sophisticated attacks are difficult to detect and make it necessary to integrate multiple data sources for detection and correlation. Attack graph (AG) is used as an effective method to model, analyze, and evaluate the security of complicated computer systems or networks. The attack graph workflow consists of three parts: information gathering, attack graph construction, and visualization. This paper proposes the integration of the AG workflow with an IDS management system to improve alert and correlation quality. The vulnerability and system information is used to prioritize and tag the incoming IDS alerts. The AG is used during the correlation process to filter and optimize correlation results. A prototype is implemented using automatic vulnerability extraction and AG creation based on unified data models.

Modern attacks are using sophisticated and innovative techniques. Teaching and training programs have to focus on the practical aspects of security, i.e., on attack techniques and defense methods, such as Intrusion Detection Systems (IDS). Team exercises provide an effective method to increase practical experience in security, as each team can gather knowledge on offensive as well as defensive techniques while learning from each other. The proposed laboratory provides effective security training by offering practical scenarios to practice attack and defense. The scenarios are parameterized and configured automatically to improve manageability. By using virtualized components as well as dedicated network infrastructure components, we can create complex scenarios that reflect real network environments. We designed and implemented several scenarios with various levels of difficulty. Finally, the teaching experiences for students and teachers are described based on the execution of the scenarios in multiple training sessions.

Simulation is an efficient way to model a real system by a computer program and is used to study and evaluate the characteristics or behaviors of the system. In this paper, we present an effective simulation tool that is designed for simulating the working procedure of the Lock-Keeper system, which is a high level security device for physically separating two networks. Due to the special mechanism of data exchange within the Lock-Keeper system, it is a challenging task to specify the Lock-Keeper's performance for a given application scenario. To get enough performance data, the intuitive way is to practically conduct numerous testing experiments and then analyze their results, which is extremely time consuming. Therefore, we are motivated to design and implement a simulator to predict the Lock-Keeper performance theoretically. Compare with most of available network simulation tools, the proposed simulator is capable of simulating the transfer of application layer data, i.e., file based data streams. The simulator is built based on a simple model of the Lock-Keeper data exchange procedure. Information on a target application scenario can be specified within an XML file, which is used as the input for the later calculation. Several kinds of reports are generated by this specialized simulator to indicate how the data is exchanged through Lock-Keeper. To verify the simulation results, we conduct several experiments, which practically test data transfer for the scenarios using the real Lock-Keeper system. The comparison between theoretical simulation and practical testing proves the effectiveness of our proposed simulation tool.

Intrusion Detection Systems (IDS) have been used widely to detect malicious behaviors in network communication and hosts. IDS management is an important capability for distributed IDS solutions, which makes it possible to integrate and handle different types of sensors or collect and synthesize alerts generated from multiple hosts located in the distributed environment. Facing new application scenarios in Cloud Computing, the IDS approaches yield several problems since the operator of the IDS should be the user, not the administrator of the Cloud infrastructure. Extensibility, efficient management, and compatibility to virtualization-based context need to be introduced into many existing IDS implementations.Additionally, the Cloud providers need to enable possibilities to deploy and configure IDS for the user. Within this paper, we summarize several requirements for deploying IDS in the Cloud and propose an extensible IDS architecture for being easily used in a distributed cloud infrastructure.

Performance is a critical aspect for all kinds of security solutions. The Lock-Keeper is a high-level security solution which implements the concept of physical separation. To evaluate and predict the performance of Lock-Keeper, the internal data transfer process has to be analyzed and simulated. As the Lock-Keeper consists of three independent systems, connected through a hardware-based switch, with file-based communication, the internal data transfer process is relatively complex. In this paper, we propose a mathematical model which can be used to formally represent most of the Lock-Keeper data transfer scenarios. The model is verified by practical experiments for three different application scenarios. We conclude that the performance of the Lock-Keeper system highly depends on the application scenarios.

Efficient Intrusion Detection System (IDS) management is a prominent capability for distributed IDS solutions, which makes it possible to integrate and handle different types of sensors or collect and synthesize alerts generated from multiple hosts located in a loosely coupled environment. Extensibility is the main requirement for most of IDS management systems. The concept of virtualization has been introduced into many popular IDS implementations due to the advantage on isolation and fast recovery in case of being compromised. Advanced capability for combining these newly emerged Virtual Machine (VM) based IDS approaches is another requirement for IDS management. This paper proposes an extensible IDS management architecture based on a new design of Event Gatherer component. By using the known IDS standard IDMEF and a plug-in concept, the Event Gatherer ensures flexibility and compatibility.Experiments are carried out to demonstrate the extensibility and virtualization-compatibility of the proposed IDS management architecture.

Archiving large datasets requires parallel processing of both data and metadata for timely execution. This paper describes the work in progress to use various processing techniques, including multi-threading of data and metadata operations, distributed processing, aggregation, and conditional processing to achieve increased archival performance for large datasets.

As tall buildings and long-span bridges become lighter and more flexible, they are more susceptible to effects of vibration. Undesirable responses from earthquakes and strong wind events can vary from total collapse of the structure to uncomfortable accelerations for human beings. This paper provides theory and numerical simulations for different control algorithms of an uncertain structural system against earthquakes. The simulated system is a four-degree-of-freedom building with a normal model and a perturbed model, in view of uncertainties. Control algorithms include LQR and several proposed robust models including a regular gain robust control, a small gain robust control, and a clipped robust control. Features of the robust control include H/sub /spl infin// disturbance attenuation, H/sub 2/ optimality, relative stability, and unstructured uncertainties in system, control input, and disturbance input matrices. Numerical simulations for the uncertain building models excited by the 1940 El Centro, California, earthquake with intensity 1.0 (real) and 0.5 are presented graphically. Simulations show greatly improved performance for the robust control algorithms.

The High Performance Storage System (HPSS) is currently deployed on the open and secure networks at Los Alamos National Laboratory (LANL). Users of the Accelerated Strategic Computing Initiative (ASCI) system with 6,144 processors and our similar Advanced Computing Laboratory (ACL) system, both from SGI/Cray, access HPSS for their data storage. We discuss our current HPSS configurations and how our users access HPSS. We analyze the performance between HPSS and these systems. We also discuss our projected storage and storage performance requirements for the next several years and what we are planning to meet those needs.

The magnetorheological (MR) damper is a semi-active control device that has received much attention by the vibration control community. Of primary interest is its fast response to a variable control signal as well as its low power requirements. The highly nonlinear dynamic nature of this device, however, has proven to be a significant challenge for researchers who wish to characterize its behavior. Research by others has shown that a system of nonlinear differential equations can successfully be used to describe the behavior of a MR damper. The paper presents an alternative for modeling a damper in the form of a Takagi-Sugeno-Kang fuzzy inference system. An ANFIS (adaptive neuro-fuzzy inference system) is used to determine 27 nonlinear premise parameters and 96 linear consequent parameters that describe the behavior of the SD-1000 model MR damper. Data used for training and checking of the model is generated from numerical simulation of nonlinear differential equations. The resulting fuzzy inference system is shown to satisfactorily represent behavior of the magnetorheological damper while greatly reducing computational requirements. Use of the neuro-fuzzy model increases the feasibility of real time simulation.

The paper describes a novel approach for reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The control technique is presented and tested in a numerical study that involves two types of building models. The energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on development of a correlation between accelerations of the building (controller input) and voltage applied to the MR damper (controller output). This correlation forms the basis for development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing the effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that possess similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

Tall, slender structures and long bridges that inherit numerous uncertainties due to model errors, stress calculations, material properties, and load environments, may undergo large forces from natural hazards such as earthquakes and strong wind events. The paper develops a robust active control approach with parametric uncertainties in the system and control input, and unstructured uncertainties in disturbance input matrices based on an uncertain structural system. Special single valued decomposition (SVD) is applied to structured uncertain structures. The robust control law provides robust relative stability, an H/sub /spl infin//-norm disturbance attenuation and H/sub 2/ optimality. The H/sub /spl infin// norm of the transfer function from the external disturbance forces (e.g., earthquake, wind, and etc.) to the observed system states is restricted by a prescribed attenuation index /spl delta/. Preservation of robust H/sub 2/ optimality of uncertain structural systems is discussed. Considered uncertainties are both structured uncertainties and norm-bounded unstructured uncertainties. Numerical simulations that use the robust controller show significant reduction in vibrations. The resulting approach to robust control may be applied to analysis and design of practical structural systems.

This paper develops a robust active control approach to treat structured uncertainties in system, control input, and disturbance input matrices based on an uncertain structural system. Special single value decomposition is applied to all structured uncertain structures. In addition, unstructured uncertainties in the disturbance input matrix are also considered along with other structured uncertainties. Robust active control provides both robust /spl alpha/-degree relative stability and H/sub /spl infin// disturbance attenuation. The H/sub 2/ norm of the transfer function from the external disturbance forces to the observed system states is restricted by a prescribed attenuation index. Considered uncertainties are both structured uncertainties and norm-bounded unstructured uncertainties, especially structured ones. The preservation of robust H/sub 2/ optimality of uncertain structural systems is discussed. Numerical simulations for an uncertain building model excited by the 1940 El Centro, California, earthquake data are described. Results may be applied to robust control analysis and design of structural systems.

A magnetorheological (MR) damper is a recently invented electromechanical semi-active control device that holds promise for applications to ameliorate structural vibration. However, nonlinear characteristics of MR dampers are delaying field installation. This paper provides a linearization scheme for MR damper behaviour using a neural network that is applicable for an online control environment.