Neural Attention Mechanism and Linear Squeezing of Descriptors in Image Classification for Visual Recommender Systems
In this paper, we analyze effective methods of multi-label classification of image sets in development of visual recommender systems. We propose a two-step algorithm, which at the first step performs fine-tuning of a convolutional neural network for extraction of visual features. At the second stage, the algorithm concatenates the obtained feature vectors of each image from the input set into one descriptor using modifications of a neural aggregation module based on linear squeezing of the feature space and an attention mechanism. We perform an experimental study for the dataset Amazon Product Data solving a problem of classification of customer interests based on photos of the products they have purchased. We show that one of the highest F1-measure indicators can be achieved for a one-level attention block with squeezing of the feature vectors.
In this paper, we explain how Galois connection and related operators between sets of users and items naturally arise in user-item data for forming neighbourhoods of a target user or item for Collabora- tive Filtering. We compare the properties of these operators and their ap- plicability in simple collaborative user-to-user and item-to-item setting. Moreover, we propose a new neighbourhood-forming operator based on pair-wise similarity ranking of users, which takes intermediate place be- tween the studied closure operators and its relaxations in terms of neigh- bourhood size and demonstrates comparatively good Precision-Recall trade-off. In addition, we compare the studied neighbourhood-forming operators in the collaborative filtering setting against simple but strong benchmark, the SlopeOne algorithm, over bimodal cross-validation on MovieLens dataset.
In this paper we study the image recognition tasks, in which images are described by high dimensional feature vectors extracted with deep convolutional neural networks and principal component analysis. In particular, we focus on the problem of high computational complexity of statistical approach with non-parametric estimates of probability density implemented by the probabilistic neural network. We propose the novel statistical classification method based on the density estimators with the orthogonal expansions using trigonometric series. It is shown that this approach makes it possible to overcome the drawbacks of the probabilistic neural network caused by the memory-based approach of instance-based learning. Our experimental study with Caltech-101 and CASIA WebFaces demonstrates that the proposed approach reduces error rate at 1-5%, and increases computational speed in 1.5-6 times when compared to the original probabilistic neural network for small samples of reference images.
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.
In this paper we focus on the problem of multi-label image recognition for visually-aware recommender systems. We propose a two stage approach in which a deep convolutional neural network is firstly fine-tuned on a part of the training set. Secondly, an attention-based aggregation network is trained to compute the weighted average of visual features in an input image set. Our approach is implemented as a mobile fashion recommender system application. It is experimentally show on the Amazon Fashion dataset that our approach achieves an F1-measure of 0.58 for 15 recommendations, which is twice as good as the 0.25 F1-measure for conventional averaging of feature vectors.
The article analyzes changes in attitudes to and interpretations of Russian "greatpowerness' (velikoderzhavnost') between the years of 2000 and 2014, that is to say during President Putin's period of rule. The concept of Russia as the great power was changing during this time in two respects: first, there was an increasing reticence of self-assessments; second, we observe prioritization of protecting the country's own, mostly regional, interests as opposed to the expansion which would be caracteristic of a great power. Moreover, this period clearly demonstrates contradictions and dangers, engendered in the process of losing self-perception as that of the great power. The readiness of Russian political elite to part bit by bit with the status of the great power and to go to the status of a regional power is combined (as the events around Ukraine have shown) with unwillingnessto sustain the new status of the country with the help of the capabilities of a soft power.Lack of these, as well as of the skills in their use, and finally, a desire to raise the rating of trust in the government with the help of "a small victorious war" have formed the basis for the aggressive upsurge towards Ukraine. In the absence of serious hard and soft capabilities, the splashes of aggressiveness in Russian foreign policy and of anti-Western sentiments in domestic political life are unlikely to have any lasting effects. They are able, however, to generate extremely negative long-term consequences for the country.
The paper deals with unconstrained face recognition task for the small sample size problem based on computation of distances between high-dimensional off-the-shelf features extracted by deep convolution neural network. We present the novel statistical recognition method, which maximizes the likelihood (joint probabilistic density) of the distances to all reference images from the gallery set. This likelihood is estimated with the known asymptotically normal distribution of the Kullback–Leibler discrimination between nonnegative features. Our approach penalizes the individuals if their feature vectors do not behave like the features of observed image in the space of dissimilarities of the gallery images. We provide the experimental study with the LFW (Labeled Faces in the Wild), YTF (YouTube Faces) and IJB-A (IARPA Janus Benchmark A) datasets and the state-of-the-art deep learning-based feature extractors (VGG-Face, VGGFace2, ResFace-101, CenterFace and Light CNN). It is demonstrated, that the proposed approach can be applied with traditional distances in order to increase accuracy in 0.3–5.5% when compared to known methods, especially if the training and testing images are significantly different.
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.