Thursday, May 16, 2019

On Content Models for Proximity Services

First time introduced in Release 12 of the 3GPP specifications, Proximity Services (ProSe in the above-mentioned specification) is a Device-to-Device (D2D) technology that allows two devices to detect each other and to communicate directly without traversing the Base Station or core network. In other words, it is a technology that is oriented (ultimately) on the direct connection of two devices. In this article, we are promoting the idea that proximity services are more than just support for a direct connection (in fact, search for candidates for a direct connection). The paper discusses content models (that is, information dissemination models) based on proximity data. In this case, a direct connection is simply one of the possible options for disseminating information.

Our new paper from FRUCT conference

Wednesday, April 17, 2019

Bikes in Smart Cities

Our new paper: On Bikes in Smart Cities

In this paper, we discuss data models and data mining for bicycles in Smart Cities. Mobilityissues (or Smart Mobility) are one of the main components of Smart Cities. Bicycles, as a transportcomponent in the cities, are on the rise all over the world. At least, it is true for all areas where the cli-mate even minimally allows it. The reasons are quite obvious. This is democratic and accessible thistype of transport, it is cheap and environmental friendliness. Of course, the promotion of a healthylifestyle also plays its role. The development of this type of transport (like any other) has many differentaspects. In this paper, we dwell on the issues of tracking the movement of cyclists and planning bike-sharing systems. All this information will serve as a set of metrics for any design in Smart Cities.

Read it here

Tuesday, March 12, 2019

Time series on railways

The article analyzes the patterns of use of railway stations in the Moscow region. The basis for the analysis is the data of smart cards on the entrances and exits of passengers. The technical tool is time series similarity analysis. As a result, the work identifies the main patterns of user behavior on the use of railway stations (railway transport). The results of the work were used in the design of new lines of urban railways. Obviously, the use patterns reflect the current state of the transport system and the urban environment. Accordingly, the recorded changes in usage patterns can serve as indicators and metrics for changes in the urban environment. Our paper in Arxiv

Wednesday, January 16, 2019

Open PhD positions

Announcement: 15 PhD positions fully funded for 3 years within A-WEAR Joint Doctorate Network

Target audience: fresh MSc graduates in various engineering fields (who have completed their first master no earlier than Fall 2015 or who will soon complete their MSc) and who are passionate about pursuing a PhD in a research field of high relevance to today’s society (wearable computing & IoT).

Job description: fully funded 36 months PhD positions towards double/joint PhD programs in 5 top European technical universities in Finland, Italy, Spain, Czech republic, and Romania
Gross salary (approx. in EUR/month): 3600 (FI), 2800 (ES), 2000 (RO), 2400 (CZ), 2900 (IT)
Application deadline: 28th of February 2019

Starting time of the PhD: Fall 2019

Selection criteria: Study records Bsc + Msc (20%); Work & research experience (15%); Motivation (20%); Clarity, relevance, innovativeness, and technical soundness of the ’Dissertation Essay’ (25%); Letters of recommendation (10%); Positive attitude, previous mobility experience, good communication skills (10%); English proficiency: fail/pass criterion.

We strive to improve the gender balance in our research groups and encourage female candidates to apply. At the end of the evaluation process, the recruitment committee will decide which candidates to select for each project, taking into account the candidates’ preferences and potential. In case of equal qualifications between a male and a female candidate for the final position, the balance at network level may affect the decision.

More information and link to the application page: or

Tuesday, January 08, 2019

Machine learning in software development

The subject of the article is the “coding style” concept and the main approaches to detecting the individual style of a programmer. The entire process of creating a software product from this point of view and the main features of programming style are analyzed. It emphasizes the relevance and commercial significance of the problem in terms of product support, plagiarism, work of a large developer’s community in a single repository, an evolution of developer skills. Computational stylometry issues, a possibility of using programming paradigms as an additional factor of style identification are considered. It offers the idea of creating a software tool that allows to identify the style of the author who wrote a particular program fragment and allows less experienced developers to follow the rules accepted in the major part of the repository and determined by coding style of "experts", which leads the code to a uniform format that is easier to maintain and make adjustments. Globally, this stage of analyzing the original (and then the modified code) allows improving the existing algorithms for automatic synthesis of programs.

Our new paper: Using Machine Learning Methods to Establish Program Authorship