Security Requirements Specification and Tracing within Topological Functioning Model

Specification and traceability of security requirements is still a challenge since modeling and analysis of security aspects of systems require additional efforts at the very beginning of software development. The topological functioning model is a formal mathematical model that can be used as a reference model for functional and non-functional requirements of the system. It can also serve as a reference model for security requirements. The purpose of this study is to determine the approach to how security requirements can be specified and traced using the topological functioning model. This article demonstrates the suggested approach and explains its potential benefits and limitations

A Hybrid Metric for Navigation of Autonomous Intralogistics Vehicles in Mixed Indoor and Outdoor Operation

While autonomous guided vehicle systems are increasingly used in homogeneous and structured environments, their use in complex and variable scenarios is usually limited. Established algorithms for the navigation of systems use static maps with deterministic metrics, which can only achieve optimal results in clearly defined environments. In dynamic and extensive deployment scenarios, which are also dependent on a large number of influencing parameters, autonomous intralogistics systems cannot yet be deployed dynamically. One example here is mixed transport between buildings under changing weather conditions. As a solution for dynamic navigation, we propose a hybrid metric in combination with topological maps and cyclic environmental sensing. Based on a quantification of influencing factors on each intralogistics entity, an optimal and dynamic navigation of every system can be performed at any time. The individual components are implemented in the context of an autonomous tow truck system and evaluated in different application scenarios. The results show significant added value in use cases with sudden weather changes and complex route networks

Mathematics in Medical Diagnostics - 2022 Proceedings of the 4th International Conference on Trauma Surgery Technology

The 4th event of the Giessen International Conference Series on Trauma Surgery Technology took place on April, the 23rd 2022 in Warsaw, Poland. It aims to bring together practical application research, with a focus on medical imaging, and the TDA experts from Warsaw. This publication contains details of our presentations and discussions

RON-BEAM DEBUG AND FAILURE ANALYSIS OF INTEGRATED CIRCUITS

A current research project at IMAG/TIM3 Laboratory aims at an integrated test system combining the use of the Scanning Electron Microscope (SEM), used in voltage contrast mode, with a new high-level approach of fault location in complex VLSI circuits, in order to reach a complete automated diagnosis process. Two research themes are induced by this project, which are: prototype validation of known circuits, on which CAD information is available, and failure analysis of unknown circuits, which are compared to reference circuits. For prototype validation, a knowledge-based approach to fault location is used. Concerning failure analysis, automatic image comparison based on pattern recog- nition techniques is performed. The purpose of the paper is to present these two methodologies, focusing on the SEM-based data acquisition process

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the "significant" interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) "significant" peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly "global" exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education.Comment: Pre-print version. Published version available at http://dx.doi.org/10.1038/srep0644

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer's disease with magnetoencephalography

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer's disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships

New algorithms for the compilation of glacier inventories

Thesis (M.S.) University of Alaska Fairbanks, 2013Glacier inventories are used for many applications in glaciology, however, their manual compilation is time-consuming. Here, we present two new algorithms for the automatic compilation of glacier inventories. The first approach is based on hydrological modeling tools and separates glacier complexes into individual glaciers, requiring a digital elevation model (DEM) and glacier complex outlines as input. Its application to > 60,000 km² of ice in Alaska (~98% success rate) and southern Arctic Canada (~97% success rate) indicates the method is robust if DEMs and glacier complex outlines of good quality are available. The second algorithm relies on glacier outlines and a DEM and derives centerlines in a three-step 'cost grid -- least cost route' procedure. First, termini and heads are determined for every glacier. Second, centerlines are derived by determining the least cost route on a previously determined cost grid. Third, the centerlines are split into branches, followed by the attribution of a branch order. Application to > 21,000 Alaska glaciers shows that ~5.5% of the glacier heads and ~3.5% of the termini require manual correction. With corrected heads and termini, ~1.5% of the actual derived centerlines need edits. Comparison with alternative approaches reveals that the centerlines vary significantly depending on the algorithm used.Chapter 1. General introduction -- Chapter 2. A new semi-automatic approach for dividing glacier complexes into individual glaciers -- Chapter 3. A new method for deriving glacier centerlines applied to glaciers in Alaska and northwest Canada -- Chapter 4. General conclusions