Topological Data Analysis in Computer Vision
The paper will provide examples of computer vision tasks in which topological data analysis gave new effective solutions. Ideas underlying topological data analysis and its basic methods will be briefly described and illustrated with examples of computer vision problems. No prior knowledge in topological data analysis and computational geometry is assumed, a brief introduction to subject is given throughout the text.
The article is devoted to the history and problems of creating interfaces. Shows the complexity and importance of effective interfaces, noted that this problem is a system of multilevel interdisciplinary. The new systems should be given serious attention to issues of human efficiency level. Man is still the leading element in determining the efficiency of any ergatic system. The main means of control in ergatic systems including computers, is the graphic manipulator (GM), with which to control the on-screen controls. Are the main styles of user interface. The most popular are GUI-interface (GUI - GraphicalUserInterface) and based on them WUI-interface (WUI-WebUserInterface). The development of equipment and technology of computer modeling led to the active introduction of virtual reality technology to ensure the inclusion of people in artificial worlds. Their main feature - full control of all the parameters of the development and the emergence of a sense of presence in people who live in these environments, which are called immersive. Technology induced environments allow a number of new, not generally applicable to the present, of interfaces using specially engineered virtual environments. Much attention is paid to creating the most advanced systems - systems contact management, which are the camera and sophisticated software. The drawbacks of modern non-contact control. Is being developed to create a contactless intelligent interface, which will allow: to control with data from a video camera, which is installed on your computer have a high noise immunity, clearly identify the user to recognize the situational environment, have an acceptable cost.
The volume contains the abstracts of the 12th International Conference "Intelligent Data Processing: Theory and Applications". The conference is organized by the Russian Academy of Sciences, the Federal Research Center "Informatics and Control" of the Russian Academy of Sciences and the Scientific and Coordination Center "Digital Methods of Data Mining". The conference has being held biennially since 1989. It is one of the most recognizable scientific forums on data mining, machine learning, pattern recognition, image analysis, signal processing, and discrete analysis. The Organizing Committee of IDP-2018 is grateful to Forecsys Co. and CFRS Co. for providing assistance in the conference preparation and execution. The conference is funded by RFBR, grant 18-07-20075. The conference website http://mmro.ru/en/.
Key characteristics of non-fluent (Broca, motor) aphasia are, among others, verb finding difficulties and effortful speech output. These characteristics are related to different levels of speech production (lexical retrieval and motor execution). This study was aimed at identifying normative brain activation related to verb production in healthy individuals, as well as patterns of its reorganization depending on the locus of the linguistic deficit in patients with non-fluent aphasia.
Most of today’s machine learning techniques requires large manually labeled data. This problem can be solved by using synthetic images. Our main contribution is to evaluate methods of traffic sign recognition trained on synthetically generated data and show that results are comparable with results of classifiers trained on real dataset. To get a representative synthetic dataset we model different sign image variations such as intra-class variability, imprecise localization, blur, lighting, and viewpoint changes. We also present a new method for traffic sign segmentation, based on a nearest neighbor search in the large set of synthetically generated samples, which improves current traffic sign recognition algorithms.
Investigations of the neural correlates of face recognition have typically used old/new paradigms where subjects learn to recognize new faces or identify famous faces. Familiar faces, however, include one's own face, partner's and parents' faces. Using event-related fMRI, we examined the neural correlates of these personally familiar faces. Ten participants were presented with photographs of own, partner, parents, famous and unfamiliar faces and responded to a distinct target. Whole brain, two regions of interest (fusiform gyrus and cingulate gyrus), and multiple linear regression analyses were conducted. Compared with baseline, all familiar faces activated the fusiform gyrus; own faces also activated occipital regions and the precuneus; partner faces activated similar areas, but in addition, the parahippocampal gyrus, middle superior temporal gyri and middle frontal gyrus. Compared with unfamiliar faces, only personally familiar faces activated the cingulate gyrus and the extent of activation varied with face category. Partner faces also activated the insula, amygdala and thalamus. Regions of interest analyses and laterality indices showed anatomical distinctions of processing the personally familiar faces within the fusiform and cingulate gyri. Famous faces were right lateralized whereas personally familiar faces, particularly partner and own faces, elicited bilateral activations. Regression analyses show experiential predictors modulated with neural activity related to own and partner faces. Thus, personally familiar faces activated the core visual areas and extended frontal regions, related to semantic and person knowledge and the extent and areas of activation varied with face type.
An increased propensity for risk taking is a hallmark of adolescent behavior with significant health and social consequences. Here, we elucidated cortical and subcortical regions associated with risky and risk-averse decisions and outcome evaluation using the Balloon Analog Risk Task in a large sample of adolescents (n=256, 56% female, age 14 ± 0.6), including the level of risk as a parametric modulator. We also identified sex differences in neural activity. Risky decisions engaged regions that are parts of the salience, dorsal attention, and frontoparietal networks, but only the insula was sensitive to increasing risks in parametric analyses. During risk-averse decisions, the same networks covaried with parametric levels of risk. The dorsal striatum was engaged by both risky and risk-averse decisions, but was not sensitive to escalating risk. Negative-outcome processing showed greater activations than positive-outcome processing. Insula, lateral orbitofrontal cortex, middle, rostral, and superior frontal areas, rostral and caudal anterior cingulate cortex were activated only by negative outcomes, with a subset of regions associated with negative outcomes showing greater activation in females. Taken together, these results suggest that safe decisions are predicted by more accurate neural representation of increasing risk levels, whereas reward-related processes play a relatively minor role.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.