Abstract: Analysis of training data has become an inseparable part of sports preparation not only for professional athletes but also for sports enthusiasts and sports amateurs. Nowadays, smart wearables and IoT devices allow monitoring of various parameters of our physiology and activity. The intensity and effectiveness of the activity and values of some physiology parameters may depend on weather conditions in particular days. Therefore, for efficient analysis of training data, it is important to align training data to weather sensor data. In this paper, we show how this process can be performed with the use of the fuzzy join technique, which allows to combine data points shifted in time.

Abstract: The statistical significance for machine learning (ML) and artificial intelligence (AI) applications improves due purely to the increasing big data size. This positive impact can be a great advantage. However, other challenges arise for processing and learning from big data. Traditional data sciences, ML and AI used in small- or moderate-sized analysis typically require tight coupling of the computations, where such an algorithm often executes in a single machine or job and reads all the data at once. Making a generic case of parallel and distributed computing for a ML/AI algorithm using big data proves a difficult task. In this paper, we described a novel infrastructure, namely collaborative learning agents (CLA) and the application in an operational environment, namely swarm intelligence, where a swarm agent is implemented using a CLA. This infrastructure enables a collection of swarms working together for fusing heterogeneous big data sources in a parallel and distributed fashion as if they are as in a single agent. As a use case, we described a data set from the Hack the Machine event, where data sciences and ML/AI work together to better understand Navy's engines, ships and system of systems. The sensors installed in a distributed environment collect heterogeneous big data. We showed how CLA and swarm intelligence used to analyze data from system of systems and quickly examine the health and maintenance issues across multiple sensors. The methodology can be applied to a wide range of applications that leverage collaborative, distributed learning agents and AI for automation.

Abstract: In this work, a computationally efficient technique for acoustic events classification is presented. The approach is based on cochleagram structure by identification of dominant time-frequency units. The input signal is splitting into frames, then cochleagram is calculated and masked by the set of masks to determine the most probable audio class. The mask for the given class is calculated using a training set of time aligned events by selecting dominant energy parts in the time--frequency plane. The process of binary mask estimation exploits the thresholding of consecutive cochleagrams, computing the sum, and then final thresholding is applied to the result giving the representation for a particular class. All available masks for all classes are checked in sequence to determine the highest probability of the considered audio event. The proposed technique was verified on a small database of acoustic events specific to the surveillance systems. The results show that such an approach can be used in systems with limited computational resources giving satisfying classification results.