Rapidly developing artificial intelligence technologies are expected to help us in various sectors of life, but their applications also entail certain risks.

The paper depicts some negative consequences of the attempts of history comprehension. In the light of the settlements of the contemporary psychology such attempts lead to the biases in historical cognition results.

The paper depicts the relations between historian person and history cognition, especially the influence of his mind inclinations to cognitive biases on narrative.

The paper deals with the problem of bias randomization in evaluation of the measuring instrument capability. The bias plays a significant role in assessment of the measuring instrument quality. Because the measurement uncertainty is a comfortable parameter for evaluation in metrology, the bias may be treated as a component of the uncertainty associated with the measuring instrument. The basic method for calculation of the uncertainty in modern metrology is propagation of distributions. Any component of the uncertainty budget should be expressed as a distribution. Usually, in the case of a systematic effect being a bias, the rectangular distribution is assumed. In the paper an alternative randomization method using the Flatten-Gaussian distribution is proposed.

Low-cost Micro-Electromechanical System (MEMS) gyroscopes are known to have a smaller size, lower weight, and less power consumption than their more technologically advanced counterparts. However, current low-grade MEMS gyroscopes have poor performance and cannot compete with quality sensors in high accuracy navigational and guidance applications. The main focus of this paper is to investigate performance improvements by fusing multiple homogeneous MEMS gyroscopes. These gyros are transformed into a virtual gyro using a feedback weighted fusion algorithm with dynamic sensor bias correction. The gyroscope array combines eight homogeneous gyroscope units on each axis and divides them into two layers of differential configuration. The algorithm uses the gyroscope array estimation value to remove the gyroscope bias and then correct the gyroscope array measurement value. Then the gyroscope variance is recalculated in real time according to the revised measurement value and the weighted coefficients and state estimation of each gyroscope are deduced according to the least square principle. The simulations and experiments showed that the proposed algorithm could further reduce the drift and improve the overall accuracy beyond the performance limitations of individual gyroscopes. The maximum cumulative angle error was - 2:09 degrees after 2000 seconds in the static test, and the standard deviation (STD) of the output fusion value of the proposed algorithm was 0.006 degrees/s in the dynamic test, which was only 1.7% of the STD value of an individual gyroscope.

The Theory of Bounded Ethicality postulates that in situations involving consequences for self and/or others people make ethical decisions that bring self- -oriented motivational forces to bear on decision-making. These ethical decisions are biased by a stubborn view of oneself, as moral, competent, and deserving and thus, not susceptible to conflicts of interest. People unconsciously favor this particular vision of the self being unaware of data that may contradict it. This conception of ethical decision making seems to imply that self-oriented motivation plays a dominant role in regulation of human behavior. But there are good theoretical reasons for questioning this view. In the paper, I describe three different research programs that may illustrate operation of the three different motivational systems – only one of them seems to corresponds with the Bounded Ethicality model. It means that that “bounded ethicality“ may appear in pure form in specific situations – when self system is activated by specific egxogenous and endogenous factors while other systems are not remain in the latent state.

The paper makes a comparison of the results of the application of two-sided and one-sided versions of the Hodrick-Prescott filter on GDP data concerning 27 EU Member States. Based on the results, the overall finding is that, contrary to its assumed advantages, the one-sided filter does not help overcome endpoint unbiasedness. Quite the opposite, it rather spreads and consolidates the endpoint bias that plagues the two-sided version over the entire filtered data. In addition, regression-based results on the influence of the second, third, and fourth moments of the GDP acceleration rates on the differences between onesided and two-sided HP trends are presented.

Monitoring of permanent stations that make up the reference frame is an integral part of the geodesists work. Selection of reference stations is based on analysis of parameters characterizing them (hardware, coordinates’ stability, mounting, location). In this paper, we took into account phase residual as an indicator of unmodelled signal. Phase residuals were computed based on ASG-EUPOS and EPN observation processing. The results show the connection between the method of mounting the antenna and the residuals. We have reviewed multipath effect at ASG-EUPOS stations, and chosen those which are characterized by the highest value of phase residual. The results show that LC phase residual is a good factor to characterize site’s solutions’ reliability. For majority of sites RMS values were less than 10 mm. Modulations associated with multipath effect were observed for few ASG-EUPOS sites only. Phase residuals are distributed specifically for sites, which antennas are mounted on pillars (more common for EPN sites). For majority of analysed sites phase residual distribution was similar for different days and did not depend directly on atmosphere condition.

The error reduction technique, based on inverse transformation, for a shunt active resistance measurement using an ammeter and voltmeter is considered. When computing a corrected reading only multiplicative operations on two measurement results are used, namely squaring and division. The proposed method allows to increase re-sistance measurement accuracy by about two orders of magnitude what has been validated by both theoretical and experimental outcomes.

This paper is devoted to a detailed experimentally based analysis of applicability of vector network analyzers for measuring impedance of surface mount inductors with and without DC bias. The measurements are made using custommade bias tees and a test fixture with an ordinary vector network analyzer. The main attention in the analysis is focused on measurement accuracy of an impedance of surface mount inductors. Measurement results obtained with a vector network analyzer will also be compared to those obtained by using an impedance analyzer based on auto-balancing bridge method.

The impact of complexity within government and societal systems is considered relative to the limitations of human cognitive bandwidth, and the resulting reliance on cognitive biases and systems of automation when that bandwidth is exceeded. Examples of how humans and societies have attempted to cope with the growing difference between the rate at which the complexity of systems and human cognitive capacities increase respectively are considered. The potential of and urgent need for systems capable of handling the existing and future complexity of systems, utilizing greater cognitive bandwidth through scalable AGI, are also considered, along with the practical limitations and considerations in how those systems may be deployed in real-world conditions. Several paradoxes resulting from the influence of prolific Narrow Tool AI systems manipulating large portions of the population are also noted

The Histogram Test method is a popular technique in analog-to-digital converter (ADC) testing. The presence of additive noise in the test setup or in the ADC itself can potentially affect the accuracy of the test results. In this study, we demonstrate that additive noise causes a bias in the terminal based estimation of the gain but not in the estimation of the offset. The estimation error is determined analytically as a function of the sinusoidal stimulus signal amplitude and the noise standard deviation. We derive an exact but computationally difficult expression as well as a simpler closed form approximation that provides an upper bound of the bias of the terminal based gain. The estimators are validated numerically using a Monte Carlo procedure with simulated and experimental data.

Determination of the phase difference between two sinusoidal signals with noise components using samples of these signals is of interest in many measurement systems. The samples of signals are processed by one of many algorithms, such as 7PSF, UQDE and MSAL, to determine the phase difference. The phase difference result must be accompanied with estimation of the measurement uncertainty. The following issues are covered in this paper: the MSAL algorithm background, the ways of treating the bias influence on the phase difference result, comparison of results obtained by applying MSAL and the other mentioned algorithms to the same real signal samples, and evaluation of the uncertainty of the phase difference.