The Order & Complexity Toolbox for Aesthetics (OCTA): A systematic approach to study the relations between order, complexity, and aesthetic appreciation

Do individuals prefer stimuli that are ordered or chaotic, simple or complex, or that strike the right balance of order and complexity? Earlier research mainly focused on the separate influence of order and complexity on aesthetic appreciation. When order and complexity were studied in combination, stimulus manipulations were often not parametrically controlled, only rather specific types of order (i.e., balance or symmetry) were studied, and/or the multidimensionality of order and complexity was ignored. Progress has also been limited by the lack of an easy way to create reproducible and expandible stimulus sets, including both order and complexity manipulations. The Order & Complexity Toolbox for Aesthetics (OCTA), a Python toolbox that is also available as a point-and-click Shiny application, aims to fill this gap. OCTA provides researchers with a free and easy way to create multi-element displays varying qualitatively (i.e., different types) and quantitatively (i.e., different levels) in order and complexity, based on regularity and variety along multiple element features (e.g., shape, size, color, orientation). The standard vector-based output is ideal for experiments on the web and the creation of dynamic interfaces and stimuli. OCTA will not only facilitate reproducible stimulus construction and experimental design in research on order, complexity, and aesthetics. In addition, OCTA can be a very useful tool in any type of research using visual stimuli, or even to create digital art. To illustrate OCTA’s potential, we will propose several possible applications and diverse questions that can be addressed using OCTA.

Government domestic debt arrears and private investment in Sub-Saharan Africa

PurposeThis study investigates the effect of government domestic payment arrears on private investment. The authors argue that an increase in government domestic arrears can reduce private sector investment owing to the competition for credit.Design/methodology/approachThe prediction is empirically tested using data for 33 Sub-Saharan Africa (SSA) countries for the period 2007–2018 using a panel general methods of moment estimation technique. This is also complemented with impulse responses derived from the standard vector autoregressive model.FindingsThe results show that an increase in government domestic arrears adversely affects private investment in SSA and most subregional communities within SSA. It also revealed that private investment negatively responds to shocks in government domestic arrears.Originality/valueThis is the first study that attempts to investigate the effect of government domestic borrowing arrears on private investment. It seeks to serve as a guide to governments in their domestic borrowing decisions to ensure timely servicing.

Frequency Domain Repercussions of Instantaneous Granger Causality

Using directed transfer function (DTF) and partial directed coherence (PDC) in the information version, this paper extends the theoretical framework to incorporate the instantaneous Granger causality (iGC) frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger connectivity, where interactions mediated without delay between time series can be easily detected.

Farm wage and Rice Price dynamics in Bangladesh

Empirical findings explaining the wage-price nexus in Bangladesh are diverse and conflicting. A proper understanding of the relationship between food prices and farm wages is essential for planning policies in support of the wellbeing and food security of the rural poor. In exploring the link between food prices and rising agricultural wages, this study analyzes the dynamic relations between those two by using monthly data from 1994 to 2014. A standard vector error correction model (VECM) is implemented to determine the short-run and long-run relationships between wages and food prices in eight divisions in Bangladesh. In addition, we use autoregressive distributed lag (ARDL) models to estimate the pass-through coefficients and to compare the short-run effects of rice price and urban wage shocks on agricultural wages. We find statistical evidence for a structural break between January 2007 and January 2009 in the relationships of the variables in all divisions. Different to the period until 2007/2009, after the structural break, in six out of eight divisions, rice price shocks do not transmit to the farm wages in the short-run. Moreover, our findings show that in the long-run food prices have become less influential in explaining the changes in farm wages while the influence of urban wages has become stronger in some divisions.

Frequency Domain Repercussions of Instantaneous Granger Causality

Using Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) in their information version, this paper extends their theoretical framework to incorporate instantaneous Granger Causality (iGC)’s frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger Connectivity where interactions mediated without delay between time series can be easily detected.

Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine

herein, we have compared the performance of SVM and MLP in emotion recognition using speech and song channels of the RAVDESS dataset. We have undertaken a journey to extract various audio features, identify optimal scaling strategy and hyperparameter for our models. To increase sample size, we have performed audio data augmentation and addressed data imbalance using SMOTE. Our data indicate that optimised SVM outperforms MLP with an accuracy of 82 compared to 75%. Following data augmentation, the performance of both algorithms was identical at ~79%, however, overfitting was evident for the SVM. Our final exploration indicated that the performance of both SVM and MLP were similar in which both resulted in lower accuracy for the speech channel compared to the song channel. Our findings suggest that both SVM and MLP are powerful classifiers for emotion recognition in a vocal-dependent manner.

Quantum semantics of text perception

The paper presents quantum model of subjective text perception based on binary cognitive distinctions corresponding to words of natural language. The result of perception is quantum cognitive state represented by vector in the qubit Hilbert space. Complex-valued structure of the quantum state space extends the standard vector-based approach to semantics, allowing to account for subjective dimension of human perception in which the result is constrained, but not fully predetermined by input information. In the case of two distinctions, the perception model generates a two-qubit state, entanglement of which quantifies semantic connection between the corresponding words. This two-distinction perception case is realized in the algorithm for detection and measurement of semantic connectivity between pairs of words. The algorithm is experimentally tested with positive results. The developed approach to cognitive modeling unifies neurophysiological, linguistic, and psychological descriptions in a mathematical and conceptual structure of quantum theory, extending horizons of machine intelligence.

Analysis and Forecast of Marketing Strategies and PR in the Condition of the Coronavirus Pandemic

The current coronavirus crisis is forcing many companies to adjust their strategies and marketing plans. It would be a big mistake to abandon promotion altogether, because for the market, a crisis is not a destruction, but a changing environment. Who will be able to adapt to change and most importantly — to prepare for the next stage, success awaits. But we must act carefully and with a view to the future. Thus, the aim of the article is to identify the role of marketing strategies and PR during coronavirus, analyze the advantages and disadvantages of existing marketing strategies, as well as to develop a mechanism for selecting the most successful of them. It is investigated that the increase of online purchases leads to an increase in the impact of in-app advertising, more developed logistics in the regions; consumers also prefer remote methods of product selection more often than personal shopping. It is determined that non-standard vector for improving the brand reputation are educational or commercials. It is also investigated that socially-oriented marketing successfully replaces part of traditional communications; however, the proportion of routine interactions still requires routine audience work practices. Socially-oriented topics should include: communication about the internal structure of the company and measures to adapt to the conditions, assistance and special conditions for its employees; publications on external prosocial, such as donations; use of the platform as a training platform, dissemination of educational material; manifestation of sectoral solidarity. It is considered that thematic strategies optimally fit into anti-crisis procedures. It is determined that a more tolerant attitude to the private sector allows to level the negative with less effort and produce an organic positive through communication with people; the population spends more time and effort on consumption and content creation, but is oversaturated with dry and pragmatic formats. It has been investigated that search engine promotion supports the position of the site for the sake of filling the marketing funnel, not just conversions; the combination of channels reduces the cost of context and targeting, allowing you to clearly specify the audience and spend organic resources only on the target segment.

Affect and Personality

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches – GIMME, uSEM, and LASSO gVAR – in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three dynamic network approaches provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches’ respective strengths and limitations.

Estimating Macroeconomic Uncertainty and Discord

We apply generalized beta and triangular distributions to histograms from the Survey of Professional Forecasters (SPF) to estimate forecast uncertainty, shocks. and discord using an information framework, and we compare these with moment-based estimates. We find that these two approaches produce analogous results, except in cases where the underlying densities deviate significantly from normality. Even though the Shannon entropy is more inclusive of different facets of a forecast density, we find that with SPF forecasts it is largely driven by the variance of the densities. We use Jenson–Shannon Information to measure ex ante “news” or “uncertainty shocks” in real time, and we find that this “news” is closely related to revisions in forecast means, is countercyclical, and raises uncertainty. Using standard vector autoregression analysis, we confirm that uncertainty affects the real sector of the economy negatively.