Reviewerly/exHarmony huggingface dataset:
From Music to Image a Computational Creativity Approach

In this paper we propose a possible approach for a cross-domain association between the musical and visual domains. We present a system that generates abstract images having as inspiration music files as the basis for the creative process. The system extracts available features from a MIDI music file given as input, associating them to visual characteristics, thus generating three different outputs. First, the Random and Associated Images - that result from the application of our approach considering different shape's distribution - and second, the Genetic Image, that is the result of the application of one Genetic Algorithm that considers music and color theory while searching for better results. The results of our evaluation conducted through online surveys demonstrate that our system is capable of generating abstract images from music, since a majority of users consider the images to be abstract, and that they have a relation with the music that served as the basis for the association process. Moreover, the majority of the participants ranked highest the Genetic Image.
Food Pairing Based on Generative Adversarial Networks

The Generative Adversarial Networks (GAN) has received great attention and achieved great successes in many applications. It is still being intensively developed and get many different variants of GAN. GAN was proposed to generate similar-looking samples to those in the training data sets. The emergence of GAN and its variants also provide new ideas for food pairing. In this paper, we have tried to invent a novel technique for food pairing using GAN and its variants. Specifically, we adopted the Long Short Term-Memory (LSTM) as the generator and the Convolutional Neural Network (CNN) as the discriminator. The sequences of recipes as the input will be encoded by LSTM into target sequences, which were finally identified by CNN to compute the differences between the generated recipes and their original input. The CNN will give a feedback to LSTM to optimize its parameters until the end of training process. As different customers have different food tastes, we have improved our method and invented new model using Conditional GAN (CGAN) to incorporate the personal demands in food pairing. We have conducted extensive experiments on real data sets to evaluate the efficiency of our proposed methods. The experimental results proved that our methods can generate better food pairings.
The improvement of visual object tracking using a combination between particle and correlation filter

Abstract One of the many challenging problems from computer vision is constructing robust object tracking from various problems and object conditions. Difficulties in tracking are more complex when an image sequence has many types of tracking problems such as background clutter, fast motion, occlusion, illumination variance, scale variance, motion blur, non-rigid object pattern, and another. Meanwhile, the task to build a novel approach which efficiently handle all tracking problems still in development step, especially supported by low computing time. We combine adaptive particle filters (APF) and discriminative correlation filters (DCF) to overcome more complex tracking problems so it can improve overall tracking accuracy. Our combination employs APF as the main framework and embeds DCF in it to estimate motion vector. However, this will make a double-edged knife where the utilization of two approaches together will make enlarged computing time. So, we also utilize leap-tracking as an alternative solution to reduce high computation time. Our testing uses 15 sets of benchmarks tracking datasets and 3 sets by ourselves with various tracking problems. The results show that our combination significantly improves tracking accuracy. Thus, the reducing of computational procedures efficiently will increases tracker speed without reduces accuracy and avoids identification errors.
A Novel and Efficient Approach for Content- Based Image Retrieval

Abstract In content-based image retrieval (CBIR) means the quest of an image continues at the real content material of picture to a positive volume to its metadata. The CBIR System has utilized for the remove the features, indexing individual’s features by inappropriate formation and capably given a response to user’s uncertainty. To provide the significant response to client question, CBIR gave some run of work. A novel scheme is proposed in this research work for color picture indexing by introducing the ease of the SVM system. A novel method is introduced for color picture indexing by exploit for it easy of the SVM system. This feature Extraction was seen as the binary classification problem and SVM was used for the problem’s solution. It is concluded that to attain the extraction at the high speed as well as for making it so flexible that it can also adjust with the images of large size. The objective of this paper is to stand for the importance of maintaining vector machine in the well-organized retrieval of an image in SVM is utilizes as classifier that performing task of classification the image and this process of classification are providing to all picture that are mined later by feature extraction process
A Recommender System for Insurance Packages Based on Item-Attribute-Value Prediction

Finding a proper insurance package becomes a challenging issue for new customers due to the variety of insurance packages and many factors from both insurance packages’ policies and users’ profiles for considering. This paper introduces a recommender model named INSUREX that attempts to analyze historical data of application forms and contact documents. Then, machine learning techniques based on item-attribute-value prediction are adopted to find out the pattern between attributes of insurance packages. Next, our recommender model suggests several relevant packages to users. The measurement of the model results in high performance in terms of HR@K and F1-score. In addition, a web-based proof-of-concept application has been developed by utilizing the INSUREX model in order to recommend insurance packages and riders based on a profile from the user input. The evaluation against users demonstrates that the recommender model helps users get start in choosing right insurance plans.
Role of entrepreneurial orientation in firm performance through project success; Moderating role of absorptive capacity in SMEs of KSA

This study aimed to examine the impact of entrepreneurial orientation on firm performance through project success and moderating role of absorptive capacity. A deductive approach was used to test the model empirically. Primary data were collected at one point in time from employees of small and medium enterprises in the Kingdom of Saudi Arabia. 354 responses were used for final data analysis. The structure Equation Modeling (SEM) technique was used to analyze the hypothesized relationships by using the Smart PLS3. The findings indicate that entrepreneurial orientation has a significant impact on firm performance and project success also significantly mediates this relationship. Moreover, absorptive capacity moderates the entrepreneurial orientation impact on project success this relation becomes stronger in the presence of absorptive capacity. This study adds value to the literature and proves the mediating role of project success and moderating role of absorptive capacity. Practically, it highlights the importance of entrepreneurial activities for firm performance and project success.
Maps Across the Triangle

Various types of maps are explored and located in the triangular-plot graphic introduced in the previous chapter. Reference maps tend to the left side of this triangular plot while thematic maps to the right side, whereas land-use maps, and others, where both locational and thematic information are of fairly equal importance are in a vertical wedge along the medial height. Large-scale topographic maps, regional maps, world maps, topological maps (e.g., subway maps), choropleth maps, cartograms, weather maps, geologic maps, and maps of airline routes – all these and more – can be positioned on this triangular-plot visualization.
A Novel Algorithm for Path Planning of the Mobile Robot in Obstacle Environment

—In this paper, a design method of smoothing the path generated by a novel algorithm is proposed, which makes the mobile robot can more rapidly and smoothly follow the path and reach the target point. No matter the attitude vector angle is an acute angle or obtuse angle, there is no doubt that we can find the right curve, including polar polynomial curves and piecewise polynomial functions, which makes the path length and the circular arc tend to be similar and guarantees the shorter path length. In the condition of meeting the dynamic characteristics of the mobile robot, the tracking speed and quality are improved. Therefore, the symmetric polynomial curve and the piecewise polynomial function curve are used to generate a smooth path. This novel algorithm improves the path tracking accuracy and the flexibility of the mobile robot. At the same time, it expands the application range of mobile robot in structured environment.
Effective Nonparametric Distribution Modeling for Distribution Approximation Applications

Many fields of science rely on the collection of samples and estimation of true population distributions from those samples. There are several effective nonparametric methods for approximating a true distribution from empirical data, however it is unclear which methods produce the best approximations in practice. This work presents a case study on the effectiveness of various distribution approximations. Results show that piecewise linear approximations produce the smallest maximum absolute error, while the classic empirical distribution function (EDF) produces the smallest median absolute error as well as the smallest first quartile and minimum absolute error when approximating a distribution from a sample. When building distribution prediction models, the piecewise quintic and cubic approximations produce the lowest absolute error at most error percentiles. This case study encourages more research on the best methods of fitting empirical data with smooth functions to generate accurate distribution approximations.
Profile Solar Geyser Heating with Standby Element including Usage utilizing prediction for Optimal Heating

Solar geyser's standby element is controlled by a time-based controller, heating water to preset value if required. In this investigation the time-based controller will be replaced with a controller profiling weather conditions and warm water usage adjusting pre-set temperatures accordingly without human intervention. Multiple sensors recordings are used recording warm water usage and heating patterns. Official weather predictions replace physical sensors to record environmental conditions. Fuzzy logic and Hidden Markov Model are used, profiling and training the algorithm. Simulations with historical data are used evaluating and comparing the algorithm efficiency against time-based heating, but its functioning in such environment needs to be assessed.
Wonders of the World Simulation Program by Virtual Reality

Wonders of the World Simulation Program by Virtual Reality was created to design and build up four 3D tourist destination models, namely, Eiffel Tower, Petra, Great Pyramid of Giza, and Stonehenge. Unreal Engine 4 was used to simulate these locations and their surroundings in order to help tourists select a destination for their vacation. Twenty samples were recruited to participate in this study, based on a voluntary basis. The results demonstrated that the participants were very satisfied with the appropriateness of the models in the scenes (mean = 4.6/SD = 0.50). The simple operating system was highly rated among the experts (mean = 4.5/SD = 0.75). The atmospheric features, sound effects, and simulated scenes of these four Wonders, in general, provided users novel experiences and realistic feelings.
Parasitic Network: Zero-Shot Relation Extraction for Knowledge Graph Populating

The relation tuple is the basic unit of the knowledge graph. Conventional relation extraction methods can only identify limited relation classes and not recognize the unseen relation types that have no pre-labeled training data. In this paper, we explore the zero-shot relation extraction to overcome the challenge. The only requisite information about an unseen type is the label name. We propose a Parasitic Neural Network (PNN), where unseen types are parasitic on seen types to get automatic annotation and training. The model learns a mapping between the feature representations of text samples and the distributions of unseen types in a shared semantic space. Experiment results show that our model significantly outperforms others on the unseen relation extraction task and achieves effect improvement of more than 20% when there are not any manual annotations or additional resources. This model, with good performance and fast implementation, can support the industrial knowledge graph populating.
Hierarchical probabilistic task recognition based on spatial memory for care support

We propose a method for recognizing tasks performed by care workers. Time-series sample data for each feature amount during tasks are defined as a task history, which is used as a basis for creating spatiotemporal task information in reference to Niitsuma's spatial memory. We perform simulated care tasks in an environment that recreates an actual care site, and measure time-series data of worker feature amounts by a motion capture system. We divide this data into learning and evaluation data, and verify the recognition accuracy. Recognition accuracy for seven defined elemental tasks is close to 80% on average, demonstrating the effectiveness of this method.
THE SOCIO-PSYCHOLOGICAL NATURE OF THE FACTORS AFFECTING THE SYSTEM OF NATIONAL FAMILY VALUES

Getting married and raising family values is a difficult task. This requires great loyalty and many years of hard work. In the family, a person learns such qualities as loyalty to his parents, friends and relatives, loyalty to the motherland, love and trust, kindness and generosity, responsibility and mutual assistance, respect not only for adults, but for everyone around him. The simple rules and moral principles formed within the boundaries of the native home are then passed on to society. These then manifest themselves in the behavior of children in kindergarten, school, institute, work and public places. Family values shape human culture and make society more humane.The whole burden of the proper formation of our society, the preservation of values falls mainly on the families. Despite the influence of socio-psychological and economic factors that affect family values in our time, we must properly protect the values and innovate in accordance with our national values. Social values are very diverse, have a historical character, and include principles of different nature, depending on human qualities, character, upbringing, and the environment in which they live. The essence and forms of values are formed depending on the mode of operation of social institutions in the society we live in, and are expressed in the form of social norms in human behavior. So, the ways in which values are expressed are norms, and in one place they express the essence of a social institution. Social institutions are constantly improving and adapting to the conditions, which means that social values do not remain the same historically, they adapt to the environment, conditions and human needs.
“Bodiliness” of thought in N.F. Fedorov’s system of views on language

The article discusses the system of N.F. Fedorov's views on language, as it was immanently formulated in his various articles and notes, laconic, aphoristic and fragmentary. Architecture and scale of Fedorov's ideas along with fundamental incompleteness as a huge project open to the future are considered in the article. An important dominant of Fedorov's thought is ‘bodiliness’, on the one hand, opposed to abstraction that destroys a phenomenon, including reality of human speech, and, on the other, hypostatizing thought and language as organic being. The dominant is essential for understanding culture at the turn of the XIX–XX centuries and the first half of the XX century (for instance, to understand Vladimir Solovyov’s views on the nature of art, the work of Andrei Platonov). ‘Bodiliness’, corporality, materiality of language and thought, according to Fedorov, is the guarantee of immortality of material life. This is the explanation of Fedorov's special attraction to ideographic writing, what embodies thought in matter as “much in little”. Fedorov’s thinking is in a special way consonant with the search for a new style of thinking in Western philosophy at the turn of the 20th century. Fedorov returns to humanitarian knowledge bodily, material; humanitarian knowledge lacks this in order to break away from the surface and move on to the things themselves, which have preserved in their original being the seeds of immortality. The article emphasizes that thought and its linguistic expression are a symbolic designation of the common cause of resurrection in Fedorov’s system, and in this it is material and has concrete outlines. The organic being of thought, belonging to life itself, has no authorship, has no place in a specific work that legitimizes it. The main conclusion of the article is about the central position of the concepts of corporality, ‘bodiliness’ in Fedorov’s project, that at the same time represent, although an integral, but, like conceptual relations in language, incomplete system, where each thought appears as a fragment, a fragment of the world that needs to be recreated.
Criptografía basada en curvas elípticas

Cryptography incorporates the techniques with which it seeks to guarantee protection of information, in front of unauthorized persons. This way of protecting information has existed since ancient times, where there were elements that only certain people were able to understand and interpret. In the beginning, cryptography was used for the purposes of war and power, but thanks to the great technological advances developed at the end of the last century, we have seen the need to safeguard the information that everyone manages and shares through the internet. That is why cryptography takes on greater importance. Current cryptography is based on two types of protocols, one of symmetric cryptography and the other corresponding to asymmetric cryptography. In this paper, an asymmetric type protocol based on elliptic curves on the finite field GF(p) was analyzed, proposing a library developed in PHP that allows to encrypt and decrypt information, which aims to provide security services, authentication, integrity and confidentiality of the information.
Characteristics of the temporal behavior of quantum Fisher information and entanglement between radiation field and two atoms under atomic motion effect

The influence of time dependence on the model which consists of two qubits interacting with a two-mode electromagnetic field of the parametric amplifier type is investigated. The analytical solution of the wave function is obtained. The quantum Fisher information, entanglement and population inversion for a time-dependent system are analyzed. The photon statistics of a single-mode are quantified by the evolution of the Mandel parameter. Our results showed that there exists a positive relationship between the time-dependent parameter and entanglement. In other words, the time-dependent parameter due to the degree of entanglement is increased. Also, the quantum quantifier is strongly affected by the time-dependent coupling parameter in the absence and presence of the detuning parameter. This enables new parameters to control the degree of entanglement and quantum Fisher information, especially in quantum communication.
The Use of Think-Aloud in Assisting Reading Comprehension among Primary School Students

Reading strategies are seldom taught to students despite their importance in aiding reading comprehension. This study aimed to examine the effectiveness of the think-aloud strategy and investigate primary school students' perception of using the think-aloud strategy in assisting their reading comprehension. Twenty-seven primary school students were introduced and trained to use the think-aloud strategy through teacher-modelling and reciprocal think-aloud with comprehension passages related to the relevant English curriculum. Questionnaires and interview sessions were conducted to gather information on how the students perceive the use of think-aloud in reading comprehension. Findings revealed that students have positive perceptions towards using think-aloud as a reading strategy in their reading comprehension and were keen on using the strategy in their future reading.&#x0D;
Digital Video Encryption by Quasigroup on System on Chip (SoC)

Video is the relied means of visual communication in a contactless manner. With personal, official and educational communication moving online, there arises a strong need of video encryption schemes, to maintain privacy and confidentiality. The current manuscript presents a novel Quasigroup Video Encryption Scheme (QuVench). Achieving the near real time encryption/decryption rate with very little latency while maintaining a small footprint is a challenge. Near real time secure processing of video data is achieved with the proposed encryption algorithm seamlessly, maintaining a small footprint. This algorithm is suitable for UAV and drone applications. The present work details the algorithm, its hardware implementation as well as the security analysis on Quasigroup Video Encryption Scheme (QuVench).
Downside Risk in Cryptocurrency Market

I look at the cryptocurrency market through the prism of standard multi-factor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important forth component in the multi-factor cryptocurrency pricing model.
Evaluation of the European Union’s Policy of Cryptocurrencies‘ Regulation

In 2008, a global financial crisis happened. It led to strong currency price volatility. Because of that, discussions on the need for an alternative, institution-independent currency occurred. Due to this reason the first decentralized cryptocurrency Bitcoin was created. The new and not yet explored concept of cryptocurrency changed the previously strictly defined role of money. Currently, with the growth of the cryptocurrency market, the most important regional institutions (e.g. FED, EBA) provide regulatory guidelines of a recommendatory nature. The regulations of these institutions remain significant, reflecting the dominant approach to digital money. Because of this reason, the aim of the study is to identify the factors that determine the difficulties in the legalization process of cryptocurrencies and to investigate the features of the European Union's cryptocurrency regulatory policy. Methods used: analysis of scientific literature and legal documents, systematization, comparison, interpretation and generalization of information. The results of the study show that the European Union has taken active regulatory action with the growing importance of cryptocurrencies in the world. To date, a document regulating the definition of cryptocurrencies has not yet been adopted at Union level, but the adoption of the cryptocurrency regulation proposal presented in 2020 would mean greater clarity and security for cryptocurrency issuers, intermediaries and users.