Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database

In this paper, we aim to estimate the Winner of world-wide film festival from the exhibited movie poster. The task is an extremely challenging because the estimation must be done with only an exhibited movie poster, without any film ratings and box-office takings. In order to tackle this problem, we have created a new database which is consist of all movie posters included in the four biggest film festivals. The movie poster database (MPDB) contains historic movies over 80 years which are nominated a movie award at each year. We apply a couple of feature types, namely hand-craft, mid-level and deep feature to extract various information from a movie poster. Our experiments showed suggestive knowledge, for example, the Academy award estimation can be better rate with a color feature and a facial emotion feature generally performs good rate on the MPDB. The paper may suggest a possibility of modeling human taste for a movie recommendation.Comment: 4 pages, 4 figure

Cognitive Approach in Development of Innovation Management Models for Company

AbstractThe tendency of transforming from resource and export based economy to resource and innovation based economy (the tendency that is characteristic for both Russian and Azerbaijani economies) requires promotion of innovative activities in all levels of management – federal, regional, corporate and in the level of separate entities. (Makarov V.L., Вvarshavskiy А.Y. and etc., 2004) In connection with this, the question of development and introduction of effective systems of innovation management, and in the first turn, company innovation management systems, (CIM), which are the main constitutional elements of national economy systems, becomes essential. (Bazilevich A.I., 2009).In modern complex economic conditions depth of processing CIM issues and substantiality of adopted management decisions is in the first turn and sufficiently determined by the quality of subsystem of scientific support. (Fatkhutdinov R.A.,1998).The report considers opportunity of use of cognitive approach for developing CIM models. The methodology of development of cognitive models of CIM sufficiently differs from generally accepted schemes discussed in Materials of inter. conf. “Cognitive Analysis and Management of Development of Situations”. CASC’2007. Main differences are that, along with using basic provisions of cognitive approach, the methodology takes into account specific characters of subject field of CIM reflected in publications, and, secondly, considers reproductive character of the process of developing CIM models, providing for wide use of progressive technological practice of innovations

Full Page Handwriting Recognition via Image to Sequence Extraction

We present a Neural Network based Handwritten Text Recognition (HTR) model architecture that can be trained to recognize full pages of handwritten or printed text without image segmentation. Being based on an Image to Sequence architecture, it can be trained to extract text present in an image and sequence it correctly without imposing any constraints on language, shape of characters or orientation and layout of text and non-text. The model can also be trained to generate auxiliary markup related to formatting, layout and content. We use character level token vocabulary, thereby supporting proper nouns and terminology of any subject. The model achieves a new state-of-art in full page recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR APIs. It is deployed in production as part of a commercial web application

УСТОЙЧИВОСТЬ БЮДЖЕТНОЙ СИСТЕМЫ И ПРОБЛЕМЫ ВОССТАНОВЛЕНИЯ РОСТА ЭКОНОМИКИ РОССИИ

The article describes methodological principles and ways of formation of a budgetary policy to ensure long-term stability of the budgetary system of the Russian Federation. The authors have formulated the concepts of balanced and sustainable economic and budget growth as well as the budgetary system stability; identified and studied the sources of the budgetary policy and budget system instability along with conditions ensuring long-term sustainability of the budgetary system; introduced quantitative criteria for evaluation of balance and stability of the budget system and budgetary policy. В статье рассматриваются методологические основы поиска и формирования направлений совершенствования бюджетной политики, обеспечивающих условия долговременной устойчивости бюджетной системы Российской Федерации. Авторы статьи сформулировали понятия сбалансированного и устойчивого роста экономики, бюджета, устойчивости бюджетной системы; выявили и изучили источники нестабильности бюджетной политики и бюджетной системы; условия, обеспечивающие долговременную устойчивость бюджетной системы; ввели количественные критерии оценки сбалансированности и устойчивости этих понятий.

Anytime Recognition of Objects and Scenes

Humans are capable of perceiving a scene at a glance, and obtain deeper understanding with additional time. Computer visual recognition should be similarly robust to varying computational budgets --- a property we call Anytime recognition. We present a general method for learning dynamic policies to optimize Anytime performance in visual recognition. We approach this problem from the perspective of Markov Decision Processes, and use reinforcement learning techniques. Crucially, decisions are made at test time and depend on observed data and intermediate results. Our method is applicable to a wide variety of existing detectors and classifiers, as it learns from execution traces and requires no special knowledge of their implementation.We first formulate a dynamic, closed-loop policy that infers the contents of the image in order to decide which single-class detector to deploy next. We explain effective decisions for reward function definition and state-space featurization, and evaluate our method on the PASCAL VOC dataset with a novel costliness measure, computed as the area under an Average Precision (AP) vs. Time curve. In contrast to previous work, our method significantly diverges from predominant greedy strategies and learns to take actions with deferred values. If execution is stopped when only half the detectors have been run, our method obtains 66% better mean AP than a random ordering, and 14% better performance than an intelligent baseline.The detection actions are costly relative to the inference performed in executing our policy. Next, we apply our approach to a setting with less costly actions: feature selection for linear classification. We explain strategies for dealing with unobserved feature values that are necessary to effectively classify from any state in the sequential process. We show the applicability of this system to a challenging synthetic problem and to benchmark problems in scene and object recognition. On suitable datasets, we can additionally incorporate a semantic back-off strategy that gives maximally specific predictions for a desired level of accuracy. Our method delivers best results on the costliness measure, and provides a new view on the time course of human visual perception.Traditional visual recognition obtains significant advantages from the use of many features in classification. Recently, however, a single feature learned with multi-layer convolutional networks (CNNs) has outperformed all other approaches on the main recognition datasets. We propose Anytime-motivated methods for speeding up CNN-based detection approaches while maintaining their high accuracy: (1) a dynamic region selection method using novel quick-to-compute features; and (2) the Cascade CNN, which adds a reject option between expensive convolutional layers and allows the network to terminate some computation early. On the PASCAL VOC dataset, we achieve an 8x speed-up while losing no more than 10% of the top detection performance.Lastly, we address the problem of image style recognition, which has received little research attention despite the significant role of visual style in conveying meaning through images. We present two novel datasets: 80K Flickr photographs annotated with curated style labels, and 85K paintings annotated with style/genre labels. In preparation for Anytime recognition, we perform a thorough evaluation of different image features for image style prediction. We find that features learned in a multi-layer network perform best, even when trained with object category labels. Our large-scale learning method also results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style

PEDAGOGICAL DESIGN IN THE FORMATION OF THE PROFESSIONAL COMPETENCE OF A SPECIALIST IN THE SPHERE OF PHYSICAL CULTURE AND SPORT

in the article concerns the questions of the role of pedagogical designing in educational process, in formation professional-pedagogical competence of the students of the faculty of physical culture and sport