Network Proximity for Content Discovery

Our new paper: I. D. Yousef and D. Namiot, “Network proximity for content discovery,” International Journal of Interactive Mobile Technologies (iJIM), vol. 9, no. 4, pp. 42–48, 2015.

The paper describes our approach for using wireless sensors on mobile phones for delivering new data to mobile subscribers. We propose a new practical approach for social context-aware data retrieval based on mobile phones as a sensor concept. This approach uses Wi-Fi and Bluetooth modules located on mobile phones as sensors for getting proximity information that can open (discover) access to any user-generated content or content published in the social networks. A special mobile service (context-aware browser client for Android) can present that information to mobile subscribers. The potential use-cases for the proposed approach include all projects associated with hyper-local news data. For example, news services in Smart City projects, proximity marketing, indoor data delivery, etc.

Meta-Data in SDN API

Our new paper: M. Sneps-Sneppe and D. Namiot, “Metadata in sdn api for wsn,” in New Technologies, Mobility and Security (NTMS), 2015 7th International Conference on, pp. 1–5, IEEE Paris, France, 2015.

This paper discusses the system aspects of the development of applied programming interfaces in Software-Defined Networking (SDN). SDN is a prospect software enablement for Wireless Sensor Networks (WSN). So, application layer SDN API will be the main application API for WSN. Almost all existing SDN interfaces use so-called Representational State Transfer (REST) services as a basic model. This model is simple and straightforward for developers, but often does not support the information (metadata) necessary for programming automation. In this article, we cover the issues of representation of metadata in the SDN API.

On events recognition in optical sensing systems

Aleksey Fedorov, Maxim Anufriev, Andrey Zhirnov, Konstantin Stepanov, Evgeniy Nesterov, Dmitry Namiot, Valery Karasik, Alexey Pnev "Gaussian mixture model for events recognition in optical time-domain reflectometry based sensing systems"

The novel approach for recognition of particular classes of non-conventional events in signals from phase-sensitive optical time-domain reflectometry is proposed. The proposed algorithmic solution is based on the adaptive filtering for de-nosing of signals and Gaussian Mixture Model with the feature space formed by the cepstral coefficients for their clustering. We use experimentally measured signals from phase-sensitive optical time-domain reflectometry based sensing systems for evidence of the suggested algorithm. Our results show that two classes of events can be detected and distinguished between two classes with the probability being close to 0.9. Proposed algorithmic solution can be used in real-time distributed fiber optic sensing systems for control of protected areas.

Twitter as a transport in information systems

Dmitry Namiot "Twitter as a Transport Layer Platform"

Internet messengers and social networks have become an integral part of modern digital life. We have in mind not only the interaction between individual users but also a variety of applications that exist in these applications. Typically, applications for social networks use the universal login system and rely on data from social networks. Also, such applications are likely to get more traction when they are inside of the big social network like Facebook. At the same time, less attention is paid to communication capabilities of social networks. In this paper, we target Twitter as a messaging system at the first hand. We describe the way information systems can use Twitter as a transport layer for own services. Our work introduces a programmable service called 411 for Twitter, which supports user-defined and application-specific commands through tweets.

To Deep or not to Deep

An interesting discussion: Will deep learning make other Machine Learning algorithms obsolete?

Our own answer - No. Very often, simpler algorithms like logistic regression will work fine. Deep Learning success depends on the data volume. It should be huge and it is not always true.

On Data Program Interfaces

Application Program Interfaces (API) or Data Program Interfaces (DPI)? Our new paper: Namiot, D., & Sneps-Sneppe, M. On Data Program Interfaces. Journal of ICT, 2, 269-288.

LSTM Networks

A good technical paper: Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies.