Modeling Brain Circuitry over a Wide Range of Scales

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes (LM) see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the topology and the connection strength are integral parts of the brain's wiring diagram, being able to combine these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. In this paper, we briefly review the Computer Vision techniques we have developed at EPFL to address this need. They include delineating dendritic arbors from LM imagery, segmenting organelles from EM, and combining the two into a consistent representation

Dynamic Template Adjustment in Continuous Keystroke Dynamics

Dynamika úhozů kláves je jednou z behaviorálních biometrických charakteristik, kterou je možné použít pro průběžnou autentizaci uživatelů. Vzhledem k tomu, že styl psaní na klávesnici se v čase mění, je potřeba rovněž upravovat biometrickou šablonu. Tímto problémem se dosud, alespoň pokud je autorovi známo, žádná studie nezabývala. Tato diplomová práce se pokouší tuto mezeru zaplnit. S pomocí dat o časování úhozů od 22 dobrovolníků bylo otestováno několik technik klasifikace, zda je možné je upravit na online klasifikátory, zdokonalující se bez učitele. Výrazné zlepšení v rozpoznání útočníka bylo zaznamenáno u jednotřídového statistického klasifikátoru založeného na normované Euklidovské vzdálenosti, v průměru o 23,7 % proti původní verzi bez adaptace, zlepšení však bylo pozorováno u všech testovacích sad. Změna míry rozpoznání správného uživatele se oproti tomu různila, avšak stále zůstávala na přijatelných hodnotách.Keystroke dynamics is one of behavioural biometric characteristics which can be employed for continuous user authentication. As typing style on a keyboard changes in time, the template adapting is necessary. No study covered this topic yet, as far as the author knows. This master thesis tries to fill this gap. Several classification techniques were exercised with help of keystroke data from 22 volunteers in order to test if they can be improved to unsupervised online classifiers. A significant improvement in impostor recognition was noted at one-class statistical classifier based on normed Euclidean distance. The impostor could make 23.7 % actions less than in offline version on average but the improvement was obseved with all test sets. In contrary, the genuine user recognition varied from user to user but it still kept at acceptable values.

Multi-class Classification with Machine Learning and Fusion

Treball realitzat a TELECOM ParisTech i EADS FranceMulti-class classification is the core issue of many pattern recognition tasks. Several applications require high-end machine learning solutions to provide satisfying results in operational contexts. However, most efficient ones, like SVM or Boosting, are generally mono-class, which introduces the problem of translating a global multi-class problem is several binary problems, while still being able to provide at the end an answer to the original multi-class issue. Present work aims at providing a solution to this multi-class problematic, by introducing a complete framework with a strong probabilistic and structured basis. It includes the study of error correcting output codes correlated with the definition of an optimal subdivision of the multi-class issue in several binary problems, in a complete automatic way. Machine learning algorithms are studied and benchmarked to facilitate and justify the final selection. Coupling of automatically calibrated classifiers output is obtained by applying iterative constrained regularisations, and a logical temporal fusion is applied on temporal-redundant data (like tracked vehicles) to enhance performances. Finally, ranking scores are computed to optimize precision and recall is ranking-based systems. Each step of the previously described system has been analysed from a theoretical an empirical point of view and new contributions are introduced, so as to obtain a complete mathematically coherent framework which is both generic and easy-to-use, as the learning procedure is almost completely automatic. On top of that, quantitative evaluations on two completely different datasets have assessed both the exactitude of previous assertions and the improvements that were achieved compared to previous methods

Models, methods and information technologies of protection of corporate systems of transport based on intellectual identification of threats

In article results of researches on development of methods and models of intellectual recognition of threats to information systems of transport. The article to contain results of the researches, allowing to raise level of protection of the automated and intellectual information systems of the transportation enterprises (AISTE) in the conditions of an intensification of transportations.  The article to contain mathematical models and results of an estimation information systems having Internet connection through various communication channels. The article also considers the issues of research and protection of the AISTE under the condition of several conflict data request threads

From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected works), Vol. 2

This second volume dedicated to Dezert-Smarandache Theory (DSmT) in Information Fusion brings in new fusion quantitative rules (such as the PCR1-6, where PCR5 for two sources does the most mathematically exact redistribution of conflicting masses to the non-empty sets in the fusion literature), qualitative fusion rules, and the Belief Conditioning Rule (BCR) which is different from the classical conditioning rule used by the fusion community working with the Mathematical Theory of Evidence. Other fusion rules are constructed based on T-norm and T-conorm (hence using fuzzy logic and fuzzy set in information fusion), or more general fusion rules based on N-norm and N-conorm (hence using neutrosophic logic and neutrosophic set in information fusion), and an attempt to unify the fusion rules and fusion theories. The known fusion rules are extended from the power set to the hyper-power set and comparison between rules are made on many examples. One defines the degree of intersection of two sets, degree of union of two sets, and degree of inclusion of two sets which all help in improving the all existing fusion rules as well as the credibility, plausibility, and communality functions. The book chapters are written by Frederic Dambreville, Milan Daniel, Jean Dezert, Pascal Djiknavorian, Dominic Grenier, Xinhan Huang, Pavlina Dimitrova Konstantinova, Xinde Li, Arnaud Martin, Christophe Osswald, Andrew Schumann, Tzvetan Atanasov Semerdjiev, Florentin Smarandache, Albena Tchamova, and Min Wang

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p