In the ever-growing realm of the Internet of Things (IoT), ensuring the security of interconnected devices is of paramount importance.This paper discusses the process of spotting cyber breaches in IoT devices, a significant concern that needs urgent attention due to the susceptibility of these devices to hacking and other cyber threats.With billions of IoT devices worldwide, the detection and prevention of cybersecurity breaches are critical for maintaining the integrity and functionality of networks and systems.In this paper, we showcase the outcomes achieved by employing the LightGBM technique for a cyberattack prediction challenge, which was a part of the FedCSIS 2023 conference.

Assigning data to one of two predefined classes is called a classification problem in machine learning (ML). This problem is very important and has become very popular, which has resulted in many methods designed to solve it. One of the problems we may encounter in the construction of this model is that the classes are not balanced – the classes have significantly different sizes. Of course, methods have been proposed to deal with this problem, one of which is to enlarge the minority class to the size of the larger class. In this paper, we present results obtained using the generative adversarial network (GAN) model to generate samples to increase the quality of binary classifier detecting malicious programs which was the goal of one of the competitions organized as part of the IEEE BigData 2022 conference. The GAN model we chose is the Wasserstein GAN (with gradient penalty), and the classifier is the LightGBM model. We reduced the RMSE on the public part of the test data from 0.19088 to 0.13497, and on the entire test set the RMSE difference was 0.11276. Due to some randomness in this solution, we showed that the presented method improves the quality of the classifier on average.

In this work, we propose the P3 Learning to Rank (P3LTR) model, a generalization of the RP3Beta graphbased recommendation method.In our approach, we learn the importance of user-item relations based on features that are usually available in online recommendations (such as types of user-item past interactions and timestamps).We keep the simplicity and explainability of RP3Beta predictions.We report the improvements of P3LTR over RP3Beta on the OLX Jobs Interactions dataset, which we published.

When selling goods abroad or bringing them into the country from foreign partners, we face the problem of delivery.The division of responsibilities related to this between the manufacturer and the recipient sometimes varies.In such a situation, it is reasonable to use the services of a forwarding company.Then a forwarding contract is concluded, which specifies the details of the service, but the most important issue remains the selection of its price.In this paper, we present results obtained using the LightGBM method on the forwarding contracts pricing challenge held as part of the FedCSIS 2022 conference.

Many tinnitus subjects report problems with communication, in particular, difficulties with the intelligibility of speech when it is presented in the background of noise. The type of tinnitus (tone-like, noise, etc.), its location and range in the frequency domain, and the type and degree of accompanying hearing loss can affect speech intelligibility in noise in different ways. The main purpose of this study was to determine the effects of tinnitus and degree of hearing loss on the intelligibility of speech when it is presented in a background noise. A group of 128 patients participated in the study. There were persons with tinnitus and sensorineural hearing loss as well persons with tinnitus without hearing loss. All participants were patients of the Laryngological Centre of Rehabilitation, Medical University in Poznań. The age of patients ranged from 31 to 84 years. An additional group of 10 subjects (24–50 years) with normal hearing and without tinnitus took part in this study as a control group. An initial experiment was concerned with the determination of the tinnitus type. A band of noise of different widths (Q-factor, goodness), with varying centre frequency (f) was used to match the perceived tinnitus type. The Q factor is the ratio of the centre frequency to the noise bandwidth. In the main experiment, the Speech Reception Threshold (SRT) was measured using The Polish Sentence Test (PST). In this test, short sentences were presented in a background of a so-called babble-noise reflecting the averaged spectrum of Polish speech. among the different types of signal used by patients to match their tinnitus, the ones most often used were broadband noise in the medium frequency range (BM) and tone-like high-frequency noise (TH). The average Speech Reception Threshold (SRT) for tinnitus patients with normal hearing was 3 dB higher than that for the control group. The highest deterioration in speech intelligibility was observed for broadband tinnitus, located in the mid-frequency band (1.5–5 kHz). Tinnitus patients with normal hearing threshold have significantly higher speech reception thresholds (SRT, on average by 3 dB) than normally hearing subjects (control group, without tinnitus), when the speech is presented in a background of babble-noise. Patients with flat audiograms had SRT values on average about 2.5 dB lower than patients with sloping type audiograms (with greater loss at higher frequencies). On average patients in the age group up to 60 years had 4 dB higher SRT values than the control group, and in the older group of patients (above 61 years) - values were 5 dB higher than in the control group. A significant effect of tinnitus located in the medium and high frequency band on the increase of SRT was noted in normally hearing patients and also in patients with mild hearing loss. For higher degrees of hearing loss, the factor that determines the deterioration of speech intelligibility is the hearing loss (moderate and severe), not tinnitus.

Over recent years the popularity of time series has soared. Given the widespread use of modern information technology, a large number of time series may be collected during business, medical or biological operations, for example. As a consequence there has been a dramatic increase in the amount of interest in querying and mining such data, which in turn has resulted in a large number of works introducing new methodologies for indexing, classification, clustering and approximation of time series. In particular, many new distance measures between time series have been introduced. In this paper, we propose a new distance function based on a derivative. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than point-to-point function comparison. The new distance is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 20 time series datasets from a wide variety of application domains. Our experiments show that our method provides a higher quality of classification on most of the examined datasets.