Ivan S. Grechikhin

A. V. Savchenko, K. V. Demochkin, I. S. Grechikhin

In this paper user modeling task is examined by processing a gallery of photos and videos on a mobile device. We propose novel engine for user preference prediction based on scene recognition, object detection and facial analysis. At first, all faces in a gallery are clustered and all private photos and videos with faces from large clusters are processed on the embedded system in offline mode. Other photos may be sent to the remote server to be analyzed by very deep models. The visual features of each photo are obtained from scene recognition and object detection models. These features are aggregated into a single user descriptor in the neural attention block. The proposed pipeline is implemented for the Android mobile platform. Experimental results with a subset of Photo Event Collection, Web Image Dataset for Event Recognition and Amazon Fashion datasets demonstrate the possibility to process images very efficiently without significant accuracy degradation. The source code of Android mobile application is publicly available at https://github.com/HSE-asavchenko/mobile-visual-preferences.

Ivan S. Grechikhin

Vehicle Routing Problem is a well-known problem in logistics and transportation, and the variety of such problems is explained by the fact that it occurs in many real-life situations. It is an NP-hard combinatorial optimization problem and finding an exact optimal solution is practically impossible. In this work, Site-Dependent Truck and Trailer Routing Problem with hard and soft Time Windows and Split Deliveries is considered (SDTTRPTWSD). In this article, we develop a heuristic with the elements of Tabu Search for solving SDTTRPTWSD. The heuristic uses the concept of neighborhoods and visits infeasible solutions during the search. A greedy heuristic is applied to construct an initial solution.