Thursday, December 21, 2023

On Audit and Certification of Machine Learning Systems

Obviously, machine learning applications are being used more and more in a wide variety of fields. The general rule today is that in the absence of analytical models, one always turns to machine learning. In itself, machine learning has become synonymous with artificial intelligence. The reverse is also true - artificial intelligence today is machine learning. Sometimes this definition is somewhat limited, and they only talk about artificial neural networks and deep learning in the context of artificial intelligence, but this does not change the essence of the matter. At the same time, it is also obvious that the spread of machine learning technologies leads to the need for their application in the so-called critical areas, where there are special requirements for confirming the operability and quality of software. These areas include, for example, avionics, nuclear power, autonomous vehicles, etc. Audit and, of course, certification are the procedures for evaluating machine learning models. - from our new paper

Friday, December 01, 2023

Certification & audit for machine learning systems

Presentation on audit of machine learning systems. Auditing should be a mandatory procedure for industrial AI systems.

Friday, November 10, 2023

On the analysis of individual data on transport usage

The percentage of the world's urban population is currently more than 50\% and will increase according to UN forecasts. Urban infrastructure must develop along with population growth. This article provides an overview of methods for improving the city's transport infrastructure based on data analysis. The article presents methods for reducing harmful emissions, optimizing the operation of taxis and public transport, as well as recognizing transportation modes and some other tasks. These methods operate with data describing the transport behavior of individual users of the transport network. The sources of such data are smart card validators, GPS sensors, and smartphone accelerometers. The article reveals the advantages and disadvantages of using each of the data types, as well as presents alternative ways to obtain them. These methods, along with methods for aggregated data analysis, can become the main part of a single platform that will allow city authorities in the process of improving the transport infrastructure. We propose architecture of this platform which will allows developers to extend range of available algorithms and methods dynamically.

DOI: 10.14357/20790279230104

Thursday, November 09, 2023

A Survey of Model Inversion Attacks and Countermeasures

This article provides a detailed overview of the so-called Model Inversion(MI) attacks. These attacks aim at Machine-Learning-as-a-Service (MLaaS) platforms, and the goal is to use some well-prepared adversarial samples to attack target models and gain sensitive information from ML models, such as items from the dataset on which ML model was trained or ML model's parameters. This kind of attack now becomes an enormous threat to ML models, therefore, it is necessary to research this attack, understand how it will affect ML models, and based on this knowledge, we can propose some strategies that may improve the robustness of ML models.

DOI: 10.14357/20790279230110

Friday, June 02, 2023

GSMA Open API


GSMA announced the creation of open telephony interfaces for third-party providers.
Lack of third party support has always been the Achilles' heel of telecom, both wired and wireless.
The need for such APIs is obvious, attempts have been made to create them, but there is no result.
Will a new attempt succeed or is it already too late?

from our presentation on FRUCT-2023

Wednesday, April 19, 2023

Local Services Based on Non-standard Wi-Fi Direct Usage Model

This article discusses a new model for building applied mobile services that use location information and operate in a certain limited spatial area. As a basis for building such applications, a new interpretation of the standard features of Wi-Fi Direct is used. The Wi-Fi Direct specification, in addition to defining the form of device connection, also introduces the concept of a service, when one device offers some service functions to another within the framework of a Wi-Fi Direct connection. Each device can both represent several services and send out search requests for other services. Based on the network proximity architecture, where connections are not used, and wireless network advertising tools are used to convey user information, Wi-Fi Direct services can be considered as key-value databases that exist on mobile devices and can be searched by keys in some local areas. It is these storages that underlie the two models of application services presented in the article that use the spatial proximity of mobile devices: direct messaging between devices without centralized control and the hyper-local Internet model.

source

Tuesday, May 03, 2022

On a formal verification of machine learning systems

The paper deals with the issues of formal verification of machine learning systems. With the growth of the introduction of systems based on machine learning in the so-called critical systems (systems with a very high cost of erroneous decisions and actions), the demand for confirmation of the stability of such systems is growing. How will the built machine learning system perform on data that is different from the set on which it was trained? Is it possible to somehow verify or even prove that the behavior of the system, which was demonstrated on the initial dataset, will always remain so? There are different ways to try to do this. The article provides an overview of existing approaches to formal verification. All the considered approaches already have practical applications, but the main question that remains open is scaling. How applicable are these approaches to modern networks with millions and even billions of parameters? - from our new paper

Sunday, May 01, 2022

A Survey of Adversarial Attacks and Defenses for image data on Deep Learning

This article provides a detailed survey of the so-called adversarial attacks and defenses. These are special modifications to the input data of machine learning systems that are designed to cause machine learning systems to work incorrectly. The article discusses traditional approaches when the problem of constructing adversarial examples is considered as an optimization problem - the search for the minimum possible modifications of correlative data that ”deceive” the machine learning system. As tasks (goals) for adversarial attacks, classification systems are almost always considered. This corresponds, in practice, to the so-called critical systems (driverless vehicles, avionics, special applications, etc.). Attacks on such systems are obviously the most dangerous. In general, sensitivity to attacks means the lack of robustness of the machine (deep) learning system. It is robustness problems that are the main obstacle to the introduction of machine learning in the management of critical systems. - from our new paper

Friday, March 25, 2022

Inaugural Issue JoSCaS

Journal of Smart Cities and Society

All articles are Open Access, the publisher is considering making the whole year Open Access for free to promote the newly created journal. Another incentive to submit reports on your research.

Thursday, February 24, 2022

Analysis and Traffic Management in Smart Cities

This article examines the issues of traffic management in the city. It is well known that cars are a significant contributor to urban air pollution. Accordingly, the management of transport in the city is one of the imperative tasks in terms of supporting the environment and ensuring urban development. Such management is impossible without collecting information on traffic flows in the city. It is logical to assume that there should be a single data source (information store) that contains information about all shipments. Urban governance, of course, involves not only collecting and analyzing data, but also managing this process. This article is devoted to the discussion of such systems. - from our paper

Thursday, December 16, 2021

On Trajectories in Moscow Subway

Along with the continuous growth of megacities, their transportation systems have become increasingly large and complex. The use of transportation systems by passengers directly reflects the changes that occur in the urban environment - for this reason, the study of urban mobility is an important task of digital urbanism. In particular, this paper is devoted to the study of spatial patterns (repetitive routes) in transportation systems with the case study on the Moscow subway. A brief review of data mining approaches to transportation systems data in general and to the task of spatial patterns extraction, in particular, is presented. A simple method for pattern extraction is proposed and applied to the Moscow subway data. As a result of the deployment of the proposed method the list of patterns was obtained - the graph of spatial patterns of the transport system under study was constructed based on it.

from our paper The Analysis of Trajectories in Moscow Subway