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We present a pipeline for recognizing dynamic freehand gestures on mobile devices based on extracting depth information coming from a single Time-of-Flight sensor. Hand gestures are recorded with a mobile 3D sensor, transformed frame by frame into an appropriate 3D descriptor and fed into a deep LSTM network for recognition purposes. LSTM being a recurrent neural model, it is uniquely suited for classifying explicitly time-dependent data such as hand gestures. For training and testing purposes, we create a small database of four hand gesture classes, each comprising 40 × 150 3D frames. We conduct experiments concerning execution speed on a mobile device, generalization capability as a function of network topology, and classification ability ‘ahead of time’, i.e., when the gesture is not yet completed. Recognition rates are high (>95%) and maintainable in real-time as a single classification step requires less than 1 ms computation time, introducing freehand gestures for mobile systems.
Group-centered framework towards a positive design of digital collaboration in global settings
(2017)
Globally distributed groups require collaborative systems to support their work. Besides being able to support the teamwork, these systems also should promote well-being and maximize the human potential that leads to an engaging system and joyful experience. Designing such system is a significant challenge and requires a thorough understanding of group work. We used the field theory as a lens to view the essential aspects of group motivation and then utilized collaboration personas to analyze the elements of group work. We integrated well-being determinants as engagement factors to develop a group-centered framework for digital collaboration in a global setting. Based on the outcomes, we proposed a conceptual framework to design an engaging collaborative system and recommend system values that can be used to evaluate the system further.
Anonymity-preserving Methods for Client-side Filtering in Position-based Collaboration Approaches
(2017)
In recent times, a lot of attacks against central server infrastructures have been recognized. Those infrastructures have seen attacks ranging from attacks against Internt of Things (IoT) infrastructures, via attacks against public infrastructure to attacks against cryptocurrency exchanges and blockchain based infrastructures themselves, e.g., the already almost legendary Decentralized Autonomous Organization (DAO) hack. Measured by press coverage, attacks against cryptocurrency exchanges and infrastructures seem to be among the most prominently reported attacks, probably due to the large amount of money that is stolen during those attacks and the great (but obviously still quite risky) potential (and financial involvement) of the blockchain technology. Naturally, attacks like the ones we have seen recently in crease the notion of uncertainty of blockchain technologies among the people,mreflected in lower values of cryptocurrencies in general. Obviously, this demands for an overall increase of security of cryptocurrency based technologies. Therefore, this paper provides an architectural approach, based on a proxy,to increase security of publicly available nodes of a blockchain based technology. Furthermore, it provides a first evaluation of the approach based on the results of an extensive community test of a new cryptocurrency.
Wissensmanagement (WM) und IT-gestütztes Lernen sind gerade in kleinen Behörden der Öffentlichen Verwaltung (ÖV), wie z.B. in ländlichen
Gemeinden, noch ausbaufähig. Am Beispiel des EU-Projekts EAGLE werden
Projektergebnisse als Verbesserungsansätze für ein arbeitsprozessorientiertes, IT-gestütztes Lernen vorgestellt. Neuartige Plattform-Features und ihr ÖV-spezifischer Nutzen werden erläutert. Die Ergebnisse der Plattformvalidierung werden vorgestellt. Ferner werden Vorschläge gemacht, wie die Ergebnisse aus EAGLE mit WM und weiteren Wissensquellen der ÖV, wie z.B. der Registratur, zu einem Gesamtkonzept mit bereits vorhandenen Fortbildungs- und WM-Ansätzen verbunden werden können.
Practical application of object detection systems, in research or industry, favors highly optimized black box solutions. We show how such a highly optimized system can be further augmented in terms of its reliability with only a minimal increase of computation times, i.e. preserving realtime boundaries. Our solution leaves the initial (HOG-based) detector unchanged and introduces novel concepts of non-linear metrics and fusion of ROIs. In this context we also introduce a novel way of combining feature vectors for mean-shift grouping. We evaluate our approach on a standarized image database with a HOG detector, which is representative for practical applications. Our results show that the amount of false-positive detections can be reduced by a factor of 4 with a negligable complexity increase. Although introduced and applied to a HOG-based system, our approach can easily be adapted for different detectors.
The Bitcoin whitepaper states that security of the system is guaranteed as long as honest miners control more than half of the current total computational power. The whitepaper assumes a static difficulty, thus it is equally hard to solve a cryptographic proof-of-work puzzle for any given moment of the system history. However, the real Bitcoin network is using an adaptive difficulty adjustment mechanism. In this paper we introduce and analyze a new kind of attack on a mining difficulty retargeting function used in Bitcoin. A malicious miner is increasing his mining profits from the attack, named coin-hopping attack, and, as a side effect, an average delay between blocks is increasing. We propose an alternative difficulty adjustment algorithm in order to reduce an incentive to perform coin-hopping, and also to improve stability of inter-block delays. Finally, we evaluate the presented approach and show that the novel algorithm performs better than the original algorithm of Bitcoin.