A Model-Driven Cross-Platform App Development Process for Heterogeneous Device Classes

App development has gained importance since the advent of smartphones to enable the ubiquitous access to information. Until now, multi- or cross-platform approaches are usually limited to different platforms for smartphones and tablets. With the recent trend towards app-enabled mobile devices, a plethora of heterogeneous devices such as smartwatches and smart TVs continues to emerge. For app developers, the situation resembles the early days of smartphones but worsened by the widely differing hardware, platform capabilities, and usage patterns. In order to tackle the identified challenges of app development beyond the boundaries of individual device classes, a systematic process built on the model-driven paradigm is presented. In addition, we demonstrate its applicability using the MAML framework to create interoperable business apps for both smartphones and smartwatches from a common, platform-independent model

A Study of Deep Learning for Network Traffic Data Forecasting

We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance, especially in large networks. In a nutshell, we wish to predict, in advance, the bit rate for a transmission, based on low-dimensional connection metadata ("flows") that is available whenever a communication is initiated. Our study has several genuinely new points: First, it is performed on a large dataset (~50 million flows), which requires a new training scheme that operates on successive blocks of data since the whole dataset is too large for in-memory processing. Additionally, we are the first to propose and perform a more fine-grained prediction that distinguishes between low, medium and high bit rates instead of just "mice" and "elephant" flows. Lastly, we apply state-of-the-art visualization and clustering techniques to flow data and show that visualizations are insightful despite the heterogeneous and non-metric nature of the data. We developed a processing pipeline to handle the highly non-trivial acquisition process and allow for proper data preprocessing to be able to apply DNNs to network traffic data. We conduct DNN hyper-parameter optimization as well as feature selection experiments, which clearly show that fine-grained network traffic forecasting is feasible, and that domain-dependent data enrichment and augmentation strategies can improve results. An outlook about the fundamental challenges presented by network traffic analysis (high data throughput, unbalanced and dynamic classes, changing statistics, outlier detection) concludes the article.Comment: 16 pages, 12 figures, 28th International Conference on Artificial Neural Networks (ICANN 2019

Probleme des indischen Bildungssystems

In der aktuellen Diskussion über die Gewährung befristeter Arbeitsgenehmigungen für einige Zehntausend Computerspezialisten aus dem Ausland wird meistens davon ausgegangen, dass diese zum großen Teil aus Indien kommen werden. Auch wenn die Frage berechtigt ist, ob sich bei den sehr einschränkenden Bedingungen der so genannten "Greencard" überhaupt genügend Spezialisten aus Indien bereit finden werden, nach Deutschland zu kommen, wirft das Schlagwort der Gegner dieser Politik, "Kinder statt Inder", die Frage auf, ob in der Bildungspolitik bei uns alles falsch und in Indien alles richtig gemacht wurde. Während in Deutschland sicherlich Versäumnisse vorliegen und manche Verbesserungen notwendig sind, befasst sich der vorliegende Beitrag mit der indischen Bildungspolitik. Nach einer knappen Darstellung individueller und gesellschaftlicher bildungsökonomischer Ansätze werden die Entwicklungen der indischen Bildungspolitik dargestellt und die riesigen Probleme des Bildungssystems angesichts verfehlter Planung, dauernder Mittelknappheit und einer wachsenden Bevölkerung erläutert

Die andere Unruheregion - Anmerkungen zu den Nordoststaaten Indiens

Die Nordoststaaten Indiens haben in der internationalen politischen Auseinandersetzung nicht den gleichen Stellenwert wie Kaschmir und werden daher in der Diskussion vernachlässigt. Wegen der in der Vergangenheit und zum Teil heute noch bestehenden Einschränkungen bei der Bereisung des Gebiets wurde seine journalistische und auch wissenschaftliche Bearbeitung vernachlässigt. Einiges spricht aber dafür, dass die indischen Bemühungen um die nationale Integration der Nordoststaaten auf Probleme stößt, deren Lösung in naher Zukunft nicht zu erwarten ist. Der vorliegende Beitrag versucht, diese Problematik auf dem Hintergrund der geographischen, demographischen, historischen, politischen und wirtschaftlichen Gegebenheiten darzustellen

Die Privatisierung der Staatsunternehmen: das Disinvestment-Desaster

Nach den einschneidenden Wirtschaftsreformen im Juni 19911 war man geneigt anzunehmen, dass der Prozess der Liberalisierung der indischen Wirtschaft im Wesentlichen abgeschlossen oder zumindest von diesem Zeitpunkt an ein Selbstläufer sei. Diese Vorstellung hat sich als falsch erwiesen. Es gibt verschiedene Bereiche, zum Beispiel im Außenhandel, bei den offenen und versteckten Subventionen, auf den Finanzmärkten und auf dem Arbeitsmarkt sowie bei den Infrastrukturinvestitionen, in denen noch große Anstrengungen erforderlich sind, um die indische Wirtschaft auch auf dem Weltmarkt konkurrenzfähig zu machen und damit der Armut im eigenen Lande erfolgreich den Kampf anzusagen

Social Norms and Preventive Behaviors in Japan and Germany During the COVID-19 Pandemic

Background: According to a recent paper by Gelfand et al., COVID-19 infection and case mortality rates are closely connected to the strength of social norms: “Tighter” cultures that abide by strict social norms are more successful in combating the pandemic than “looser” cultures that are more permissive. However, countries with similar levels of cultural tightness exhibit big differences in mortality rates. We are investigating potential explanations for this fact. Using data from Germany and Japan—two “tight” countries with very different infection and mortality rates—we examined how differences in socio-demographic and other determinants explain differences in individual preventive attitudes and behaviors. Methods: We compared preventive attitudes and behaviors in 2020 based on real-time representative survey data and used logit regression models to study how individual attitudes and behaviors are shaped by four sets of covariates: individual socio-demographics, health, personality, and regional-level controls. Employing Blinder-Oaxaca regression techniques, we quantified the extent to which differences in averages of the covariates between Japan and Germany explain the differences in the observed preventive attitudes and behaviors. Results: In Germany and Japan, similar proportions of the population supported mandatory vaccination, avoided travel, and avoided people with symptoms of a cold. In Germany, however, a significantly higher proportion washed their hands frequently and avoided crowds, physical contact, public transport, peak-hour shopping, and contact with the elderly. In Japan, a significantly higher proportion were willing to be vaccinated. We also show that attitudes and behaviors varied significantly more with covariates in Germany than in Japan. Differences in averages of the covariates contribute little to explaining the observed differences in preventive attitudes and behaviors between the two countries. Conclusion: Consistent with tightness-looseness theory, the populations of Japan and Germany responded similarly to the pandemic. The observed differences in infection and fatality rates therefore cannot be explained by differences in behavior. The major difference in attitudes is the willingness to be vaccinated, which was much higher in Japan. Furthermore, the Japanese population behaved more uniformly across social groups than the German population. This difference in the degree of homogeneity has important implications for the effectiveness of policy measures during the pandemic

Tradeoff between User Experience and BCI Classification Accuracy with Frequency Modulated Steady-State Visual Evoked Potentials

Steady-state visual evoked potentials (SSVEPs) have been widely employed for the control of brain-computer interfaces (BCIs) because they are very robust, lead to high performance, and allow for a high number of commands. However, such flickering stimuli often also cause user discomfort and fatigue, especially when several light sources are used simultaneously. Different variations of SSVEP driving signals have been proposed to increase user comfort. Here, we investigate the suitability of frequency modulation of a high frequency carrier for SSVEP-BCIs. We compared BCI performance and user experience between frequency modulated (FM) and traditional sinusoidal (SIN) SSVEPs in an offline classification paradigm with four independently flickering light-emitting diodes which were overtly attended (fixated). While classification performance was slightly reduced with the FM stimuli, the user comfort was significantly increased. Comparing the SSVEPs for covert attention to the stimuli (without fixation) was not possible, as no reliable SSVEPs were evoked. Our results reveal that several, simultaneously flickering, light emitting diodes can be used to generate FM-SSVEPs with different frequencies and the resulting occipital electroencephalography (EEG) signals can be classified with high accuracy. While the performance we report could be further improved with adjusted stimuli and algorithms, we argue that the increased comfort is an important result and suggest the use of FM stimuli for future SSVEP-BCI applications

Online Tracking of the Contents of Conscious Perception Using Real-Time fMRI

Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subject’s conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory