Reviewerly/exHarmony huggingface dataset:
Ecology of the Digital Environment as an Attempt to Respond to the Civilizational Challenges of the Digital Age

The article is devoted to the analysis of the impact of digital technologies on the brain, consciousness and human nature as a whole. According to the author, this kind of impact is one of the most serious civilizational challenges of the dig­ital age. In her analysis, the author relies on the ecological approach as a holistic interdisciplinary methodological principle. The most important part of this ap­proach is the ecology of the digital environment. The digital environment in the article is considered as a new environment for a person, a new Umvelt, that is, the immediate environment that affects his inner world. Therefore, signs and stimuli that arise in the process of digital communication have such a signif­icant impact on cognitive processes. The communication situation of modern man is paradoxical in a certain sense: despite the ever-increasing complexity of technology, the communication process itself is impoverished and standard­ized. This negatively affects the evolution of the brain, as the emergence of new neural connections is not stimulated. The way out of this situation can be a pur­poseful stimulation of the development of critical thinking. Within the frame­work of an integrated ecological approach, the author relies on an ecological ap­proach to the visual perception of J. Gibson and the concept of Umwelt J. von Uexküll, substantiating a much broader theoretical applicability of environmental science than it is accepted in classical biological ecology. The author comes to the conclusion that the ecology of the digital environment contributes to find­ing answers to the civilizational challenges of the digital era and becomes the main weapon in the fight against the information and environmental crisis.
Modelling Subscription‑Based Streaming Services for Books

By departing from the Nordic countries, the purpose is to explain and conceptualize how subscription-based streaming services for books operate in the contemporary book trade, and furthermore to discuss what effects these new business models are causing regarding the production, distribution, and consumption of books. A model is proposed that outlines the aspects of streaming services and the relationships to other actors involved. The model illustrates how streaming services affect the entire chain of publishing, and influence the constitution of books in contemporary digital culture. Since audiobooks are by far most popular, the production get altered to suit this format. Streaming services themselves also act as publishers, which makes them powerful. Distributional aspects are both more complex and more important in streaming services than in traditional book retailing. When consumption is transferred to digital platforms, the distributional frame and the final product converge, and consumer data becomes a key source of information.
Decision Making Model for Strategy Choice in Higher Education Under Neutrosophic Environment

The importance of studying the relationships between colleges, businesses, and governments has grown as a result of the recent explosion of technical advancements. The potential for national economic growth is enhanced by the dissemination of new information discovered via research. When it comes to the generation of new information that can be used by established economies, universities play a crucial role. So, in this study we proposed a framework for select best strategy in higher education. This process contains many conflicting criteria, so the concept of multi-criteria decision making (MCDM) is used. The MCDM is integrated with the triangular neutrosophic sets to overcome the vague information. The COCOSO technique is proposed to rank the alternatives. Higher education officials, including government bureaucrats and academic administrators, may put the recommended approach to use.
Scale-invariant Mexican Hat wavelet descriptor for non-rigid shape similarity measurement

The Mexican Hat wavelet (MHW) is strictly derived from the heat kernel by taking its negative first-order derivative with respect to time t. As a solution to the heat equation that the heat kernel has a clear initial condition, the Laplace-Beltrami operator. Although the MHW descriptor can effectively characterize the model information, but it has poor robustness to the model with scale transformation, and the feature description performance is affected to some extent. Following a popular mathematical method, in this paper, we bases on the MHW to study scaling invariance and proposes a new shape descriptor, the scale-invariant Mexican Hat wavelet (SIMHW), which by logarithmic sampling and Fourier transform that obtains the expression of SIMHW in Fourier domain. The experimental results show that SIMHW has finer information description ability and stronger recognition ability, and has better robustness to various non-rigid transformations. It can correctly calculate the similarity between 3D shapes and realize the effective shape retrieval.
Analysis of the Dynamic Characteristics of Bitcoin and Ethereum

Represented by two digital currencies, Bitcoin and Ethereum, the dynamic characteristics of the currency value of digital currencies are analyzed.First, we conduct a horizontal analysis of the relationship between these two digital currencies through co-integration and Granger causality test.Secondly, the Sup ADF and GSADF test methods are used to longitudinally analyze the bubbles of the three digital currencies.The results show that there are Granger causality and price bubbles in Bitcoin and Ethereum.Through analysis, it is found that the high volatility of currency value is not only related to the relationship between digital currencies but also related to factors such as digital currency market fluctuations and regulatory policies.
Graduation Thesis Topic Recommendation Based on Neural Network

Undergraduate graduation design thesis is not only the goal of higher education, but also an important link.It plays an important role in training college students to carry out scientific research, basic training, and improve their ability and comprehensive quality.This paper takes the engineering class of the College of Electronic and Electrical Engineering as the research object, the paper uses text mining algorithm to know the main research topic and the simulation software used.In order to solve the problem of how to guide the students to choose the suitable graduation project theme, this paper puts forward some suggestions to the students through the method of neural network.
Variability of Solar Radiation Received on Tilted Planes in Adrar Region in the South of Algeria

The solar radiation is one of the renewable energy sources that is gaining importance day by day. To make the most effective use of solar energy, the tilt angle of the solar panel that collects solar radiation must be properly adjusted. In this study, experimental and analytical investigations have been conducted in order to identify the solar panels’ optimum tilt angles for the first time in Adrar site, located on the southwestern region of Algeria, and to recommend a general approach for any location in country. Calculations were made with both selected isotropic and anisotropic models. 11 years (2010–2020) historical data in Renewable Energy Research Unit in Saharan Medium (URER-MS) was used. Monthly, seasonal, and Annual solar panels’ optimum tilt angles were determined. It is found that the annual optimum solar panel tilt angle should be set to 28°. These findings might be generalized to locations with different coordinates at the same latitude the annual variability of solar radiation on different planes of inclination is between 6 kWh/m2/day and 8 kWh/ m2/day (2010–2020) all inclinations combined from horizontal to near 60° and 2 kWh/m2 to 6 kWh/m2 for a vertical plane facing plain south.
A Weight Based Hierarchical Vector Quantization Algorithm for Space Environment Volume Data

空间环境数据可视化是空间环境预报和服务的重要手段.目前基于矢量量化的压缩体绘制算法均只考虑了数据特点和压缩效果，并未结合具体应用需求.为适应空间环境体数据可视化具体应用需求，提出了一种应用驱动的压缩体绘制算法——基于权重的层次矢量量化算法（WHVQ）.算法将体数据划分为4<sup>3</sup>的数据块，并为数据块设置权重，重点关注区域的数据块赋予相对较大的权重值.然后，对各数据块用三层结构表示.最后，对次高层和最高层分别采用基于权重的主成分分析分裂法产生初始码书，并采用基于权重的LBG算法进行码书优化和量化.实验结果表明，该算法能够在保证整体压缩效果的同时，提升局部重点关注区域的重构质量.
Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Spam and emails have always been intrinsically linked since the creation of the Advanced Research Projects Agency Network, otherwise known as (ARPANET).The latter witnessed, on May 3rd, 1978, the first known spam email to date.Today, spam emails negatively affect the users' productivity and private lives.A significant number of approaches emerged in the past two decades that deal with the spam detection problem, with limited success.Therefore, the current paper presents an intelligent and automated solution to spam email detection using a logistic regression model trained by a teaching-learning-based optimization algorithm.The proposed solution has been tested on two benchmark spam email datasets (CSDMC2010 and TurkishEmail), and evaluated against seven other contending cutting-edge metaheuristics utilized in the same experimental setup.The simulation outcomes without a doubt indicate the superior level of accuracy achieved by the proposed solution.
How Well Do Vision Transformers (VTs) Transfer to the Non-natural Image Domain? An Empirical Study Involving Art Classification

Vision Transformers (VTs) are becoming a valuable alternative to Convolutional Neural Networks (CNNs) when it comes to problems involving high-dimensional and spatially organized inputs such as images. However, their Transfer Learning (TL) properties are not yet well studied, and it is not fully known whether these neural architectures can transfer across different domains as well as CNNs. In this paper we study whether VTs that are pre-trained on the popular ImageNet dataset learn representations that are transferable to the non-natural image domain. To do so we consider three well-studied art classification problems and use them as a surrogate for studying the TL potential of four popular VTs. Their performance is extensively compared against that of four common CNNs across several TL experiments. Our results show that VTs exhibit strong generalization properties and that these networks are more powerful feature extractors than CNNs.
A Pruning Regularization Stochastic Configuration Network Algorithm Based on Node Contribution and Its Application in Soft Sensor Development

Stochastic configuration network (SCN) has emerged as an efficient nonlinear regression modeling method. However, the traditional SCN has to set the model training stop criterion (e.g. the maximal hidden layer node number, or the expected error tolerance) in advance. The unreasonable parameter setting may lead to the over-fitting SCN model. To solve this problem, an adaptive pruning regularization SCN (PRSCN) algorithm based on the node contribution is proposed and its application to soft sensor modeling is demonstrated. First of all, this method builds the definition of node contribution and then assigns different weights to the hidden nodes according to the corresponding contributions. Secondly, an adaptive pruning strategy is presented to delete the redundant nodes so that an optimal network structure is obtained. Finally, the L2 regularization method is used to calculate the output weights for ensuring the good model generalization. The proposed method is applied to develop the soft sensor from one real sulfur recovery unit system, and the testing results show that PRSCN has a better predictive performance than the basic SCN method.
A novel text sentiment analysis system using improved depthwise separable convolution neural networks

Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people’s emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification technology can produce huge social and economic benefits. However, due to the rapid development of the Internet, the text information generated on the Internet increases rapidly at an unimaginable speed, which makes the previous method of manually classifying texts one-by-one more and more unable to meet the actual needs. In the subject of sentiment analysis, one of the most pressing problems is how to make better use of computer technology to extract emotional tendencies from text data in a way that is both more efficient and accurate. In the realm of text-based sentiment analysis, the currently available deep learning algorithms have two primary issues to contend with. The first is the high level of complexity involved in training the model, and the second is that the model does not take into account all of the aspects of language and does not make use of word vector information. This research employs an upgraded convolutional neural network (CNN) model as a response to these challenges. The goal of this model is to improve the downsides caused by the problems described above. First, the text separable convolution algorithm is used to perform hierarchical convolution on text features to achieve the refined extraction of word vector information and context information. Doing so avoids semantic confusion and reduces the complexity of convolutional networks. Secondly, the text separable convolution algorithm is applied to text sentiment analysis, and an improved CNN is further proposed. Compared with other models, the proposed model shows better performance in text-based sentiment analysis tasks. This study provides great value for text-based sentiment analysis tasks.
A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem

W HEN the control rules of traditional fuzzy controller are determined, it comes to be time-consuming and laborious to adjust for different usage conditions.Therefore, the timeliness cannot be guaranteed to solve the timeliness problem, and a fuzzy controller with modifiable factors is designed.While the entire control table is affected by the modifiable factors selection table, all previous control parameters need to be reset.In light of above problems, this paper firstly proposes new fuzzy controller design methods, which retain strengths of traditional controller and controller with modifiable factors.It effectively overcomes the shortcomings of the two controllers mentioned above, and only need to adjust the compromise factor for different working conditions of proposed controller, which is more convenient and efficient.Secondly, the proposed fuzzy controller also adopts a four-layer neural network to optimize the control rules of compromise to improve control precision and system robustness.Finally, the excellent characteristics of proposed controller are verified through simulation research, and the simulation result proves the proposed fuzzy controller has the advantages of higher control precision and smaller transition.
Image-Based Visual Servoing of Quadrotors to Arbitrary Flight Targets

Visual servoing of Unmanned Aerial Vehicles (UAVs) has achieved satisfactory performance in fixed and planar motion targets. Due to highly coupled system dynamics and the sensitivity of the target image to aircraft attitude, the problem for chasing free-flying targets remains challenging. In this paper, a vision-based algorithm is designed for controlling an UAV while tracking an intruder flying arbitrarily in 3D space. Image-based visual servoing is used to design controllers that depend directly on errors in image plane. Specifically, a virtual camera approach is adopted to decouple the UAV dynamics by compensating the pitch and yaw, and an improved image error term is proposed to reduce the impact of the UAV rotation on error signals in the process of tracking, thus a simplified control design is achieved, and the stability of the visual servo system is guaranteed. Comparison and ablation experiments in both simulated and real environments are provided to verify the effectiveness of the proposed method.
The need to centre imperialism in studies of uneven development

The key argument of this chapter is that to grasp the global and uneven character of capitalist development, it is crucial to place imperialism at the core of our analysis. This is of particular importance at a time when the study of development has been increasingly characterised by methodological individualism. Centring imperialism, in contrast, allows us to see capitalism as a global system of exploitation, which is essential to shed light on the drivers and manifestations of contemporary global phenomena such as financialisation. To what extent imperialism plays a role in shaping uneven development today is a subject of much disagreement. There has been a wave of scholars arguing that imperialism is no longer relevant, or at least not for all developing countries. Given the plethora of theories of and approaches to imperialism that exist, this is a highly controversial topic even among radical scholars. This chapter discusses what it means to centre imperialism, drawing on some key scholarly contributions. It does not rehash the many rich debates about specific theories of imperialism, but draws in some key contributions from the past two centuries. The chapter then goes on to discuss what centring imperialism does for analyses of finance in uneven development, before demonstrating how centring imperialism is helpful for understanding the evolution of the financial systems of Ghana and Senegal in particular.
ANALYTICAL AND INTEGRATED FRAMEWORK FOR PRIVACY PRESRVATION OF IMAGES IN CLOUD ECOSYSTEM

Privacy preservation is always a potential concern while storing or transmitting images with sensitive information within them.A review of existing privacy preservation techniques shows the potential usage of deep learning, adoption of highly sophisticated encryption, lack of packetization scheme, and focus on a singular attack.Therefore, the proposed study presents a novel analytical framework for the privacy preservation of image data in the cloud environment.A novel and simplified mathematical model with layer-based operation is constructed, which facilitates appending image chunks with digital security code and encryption token using a hash to generate a hash vector of an image with a higher degree of secrecy.An extensive analysis is carried out over different datasets of images to find that the proposed scheme offers consistency in its performance and a significant balance between security and computational performance in contrast to frequently exercised privacy preservation schemes.The study outcome exhibited 59% overhead reduction, 25% enhanced signal quality, 65% better resolution, and 40% faster execution time in contrast to the existing scheme.
Welfare Program Recommendation by Conditional Variational Autoencoder and Collaborative Filtering

Recently, the government of South Korea has offered a variety of welfare programs that are customized to diverse demands, such as diabetes management, alcohol addiction rehabilitation, living condition improvement, etc. These welfare programs have become too diverse to be remembered and recommended by individuals, and the government now has a list matching program recipients and programs for further studies. This research investigates such welfare program recommendation with a conditional variational autoencoder merged with collaborative filtering, a.k.a. CVAE-CF. We use a natural language description to provide the program information, or item in the context, and we utilize the demographic information from potential recipients as the user information. Our results show agreeable performance for future application to recommendation tasks showing 63% recall and 13.1% precision on average.
COGNITIVE ELEMENTS OF INFORMATION ENVIRONMENTS

The most important problem of the automated construction of ontologies based on the semantic analysis of natural language texts is intellectualization and related data integration and qualitative information search. However, these technologies require the availability of quality semantic data sources, and the data sources are not sufficiently sophisticated to be conveniently used or integrated. In connection with this problem, the task of automated formation of ontologies based on natural language text analysis in construction is relevant. A common criterion of the quality of an ontology is based on the assessment of the convenience and efficiency of working with it, as well as the presence of a connection between cognitive and semantic processes for its construction and actualization in the field of construction. The main difference of the conducted research, the results of which are presented in the proposed work, is a cognitive-semantic analysis based on the theory of categories, mathematical logic and universal algebra, algebra of sets, algebra of tuples and relational algebra, namely, the construction of an ontological dictionary, ontological constructions corresponding to the text in open languages, presentation of knowledge. In the work, the classification and definition of information units, their sources, as well as the features of the information resource are determined.
Economic openness, institutional quality and per capita income: Evidence from the Economic Community of West African States (ECOWAS)

&#x0D; &#x0D; &#x0D; &#x0D; The controversy surrounding the actual impact of institutional quality and economic openness on economic growth is among the motivating factors for this study. The study seeks to investi- gate this relationship in the Economic Community of West African States (ECOWAS) by using the panel autoregressive distributed lag (ARDL) test with annual series covering the period from 2000 to 2020. Findings indicate that in the short-run, regulatory quality and FDI outflows had an adverse impact on the economic performance of the ECOWAS bloc. Furthermore, the long-run results show that trade openness, political stability and FDI outflows had an adverse impact on the economy of the bloc, while regulatory quality positively affected the economy. Consequently, the paper recommends that member countries in the ECOWAS bloc should put in place effective regulatory framework in the short and medium term to attract FDI inflows, while building a strong and stable political environment in the long term.&#x0D; &#x0D; &#x0D; &#x0D;
Criminal Face Detection System

Criminal record generally contains all the information both personal and criminal with the photograph of the person. In order to recognize Criminal, identification of some sort is required, designated by eyewitnesses. In most cases the resolution or/and quality of the recorded image sections is unsatisfactory and is difficult to recognize the face. Recognition can be achieved in various different ways like DNA, eyes, finger print, etc. One of the ways is face identification. In our proposed methodology, a database is created by storing both full and sliced images of the criminals along with all the personal and criminal details. Then one more database is created in order to identify the criminal; eyewitnesses with the help of a professional will try to build the criminal face using the stored slices stored in the database. Then with the help of the Amazon Rekognition, it predicts the criminal by matching the created image with the existing database, if the result is 70-80% of then that face is declared as a criminal.