Сборник трудов V Международной конференции и молодёжной школы "Информационные технологии и нанотехнологии" (ИТНТ 2019)
In this paper we focus on the problem of user prediction in visual product recommender systems based on the given set of photos of products purchased by the user previously. We studied neural aggregation methods for image features extracted by the deep neural networks. We propose the novel two-stage algorithm. At first, the image features are learned by fine-tuning the convolutional neural network. At the second stage, we sequentially combine the known learnable pooling techniques (neural aggregation network and context gating) in order to compute a single descriptor for particular user as a weighted average of image features. It is experimentally shown for the Amazon product dataset that F1-measure for our approach is more than 20% higher when compared to conventional averaging of the feature vector.
The article discusses the strategy of «mixing» methods, particularly prevalent in the Western research tradition. Covers the methods of text analysis, demonstrated the difference between formal or approach on the example of the study of the image of modern Russia in the texts of the American edition of «New York Times», where attention is paid to algorithms work with texts. It is shown that for the study of such phenomena as the image of the country, the combination of formal or approaches to the analysis of the text is a necessary and natural research phenomenon.
This study investigated the method of semantic image analysis by using a set of neuron-like detectors of foreground objects. This method is intended to find different types of foreground objects and to determine properties of these objects. As a result of semantic analysis the semantic descriptor of the image is created. The descriptor is a set of foreground objects of the image and a set of properties for each object. The distance between images is defined as distance between their semantic descriptors. Using the concept of distance between images, "semantically similarity" between images or videos is defined.
Academic rewards and honors are proven to correlate with h-index, although it was not the decision criterion for them till recent years. Once h-index becomes the rule-setting scientometric ranking measure in the zero-sum game for academic positions and research resources as suggested by its advocates, the rational behavior of competing academics is expected to converge towards its game- theoretic solution. This paper derives the game-theoretic solution, its evidence in scientometric data and discusses its consequences on the development of science. DBLP database of 07/2017 was used for mining. Additionally, the openly available scientometric datasets are introduced as a good alternative to commercial datasets of comparable size for public research in behavioral sciences.
In the paper we present a new notion of stochastic monotone measure and its application to image processing. By definition, a stochastic monotone measure is a random value with values in the set of monotone measures and it can describe a choice of random features in image processing. In this case, a monotone measure describes uncertainty in the problem of choosing the set of features with the highest in information value and its stochastic behaviour is explained by a noise that can corrupt images.
There is a review and analysis of methods for digital image retrieval in this paper.
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
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
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.