Reviewerly/exHarmony huggingface dataset:
An investigation on the automatic generation of music and its application into video games

This paper presents a description of the author's PhD research plan and its progress to date. By way of introduction, some gaps and challenges are pointed out concerning algorithmic composition and its literature. Motivated by these, a set of research questions are given, which explore the possibility of generating music matching tension and its applications in video games. To give a brief overview of the background, the most relevant models of tension in music are introduced, as well as the most recent pieces of related work. The research approach is then presented as a summary of the scope of the problem, according to the gaps motivating the project and the challenges that come from the related work, and the appropriate methodology to explore the research questions. A brief review of the work to date is included, emphasising the design of an automatic music generator and the empirical study carried out to test its capabilities. To conclude, the steps presented in the methodology are transformed into a future plan and some research contributions.
Single Image Super-Resolution Based on Cascaded Recursive Residual Convolutional Neural Network

The accurate and automatic reconstruction of a super-resolution image from one single low-resolution image is an important challenging problem for image analysis. In this paper, we have optimized the problems of existing methods and presented an automated super-resolution image reconstruction system from a single low-resolution image based on cascaded recursive residual convolutional neural network. In this method, we extracted the characteristics of images firstly, then used recursive networks to connect the feature information of each layer to obtain three residual images. Finally, the high-resolution image was obtained by applying the deconvolution at the end of network. we used the recursive structure applying to the residual learning, this network differed from traditional residual learning in that the structure and overlay of the remaining learning network are different, it can solved the problem of gradient explosion, overfitting and insufficient transmission of high frequency component. Compared with present four methods, our algorithm had good performance in experiment of super-resolution testing. Experimental results showed that the proposed method could recover the fine details and textures, simultaneously, it has satisfactory performance in PSNR values.
Generating Anime from Real Human Image with Adversarial Training

With growing interest in animation movie, the demand for building an automated system to convert the real video into animation is higher than ever. The frame-by-frame editing of animation generation process is very costly and time-consuming. To help to generate animation quickly and easily in an automated process we proposed a generative model that converts real-world images into corresponding-animation image without losing important details of the source image. We used a variation of the generative adversarial network as underlying architecture with custom loss function to preserve the content of source image which converting to animation image.
Minimal Specifications for Detecting Security Vulnerabilities

Computers are nearly ubiquitous in modern society with uses from maintaining friendships and monitoring homes to managing money and coordinating health care. As the roles of a computer continue to expand, so to does the threat posed by cyberattacks. An important challenge for today's software engineers is to build secure software and help neutralize these threats. Formal methods have long been suggested as an excellent way to build secure software but have not been widely adopted for this purpose. The "conventional wisdom" has suggested several reasons for this slow adoption, including a steep learning curve, difficulty in augmenting existing systems, and lack of tools with security-specific abstractions. Our hypothesis, however, is that applying a small and easy to learn subset of the techniques available today could significantly decrease software vulnerabilities and reduce the risk of cyberattacks. In this paper, we discuss the motivation for our hypothesis and discuss our ongoing experiment to test it.
Verification and Validation of Semantic Annotations

In this paper, we propose a framework to perform verification and validation of semantically annotated data. The annotations, extracted from websites, are verified against the schema.org vocabulary and Domain Specifications to ensure the syntactic correctness and completeness of the annotations. The Domain Specifications allow for checking of the compliance of annotations against corresponding domain-specific constraints. The validation mechanism will detect errors and inconsistencies between the content of the analyzed schema.org annotations and the content of the web pages where the annotations were found.
The Research of the Relationship of Preferred Musical Styles and Personal Features of Music Teachers

The present article researches the relationship of musical preferences of adolescence (pop, rock and rap) and modernity (classical, pop, rock, music lovers) with the display of personal features, such as empathy, emotional exhaustion, state and trait anxiety, pedagogical style and coping strategies. The musical genres and trends are considered as musical preferences and precedential musical phenomenon revealed empirically during the self-analysis by the subjects. The experimental study was carried out on the basis of Moscow Pedagogical State University and consisted of 4-5th-year students of Music Department. The connections between personal features and preferred musical styles of modernity were detected. The students considering classical music to be their favorite musical direction having a high level of personal resources. These results display the positive influence of classical music to the personal resources of future music teachers. This phenomenon demands further investigations and using in the field of musical education psychology. These results highlight necessity of detailed consideration on classical music relationship and personal features of bachelor students musical department during the educational process.
Formation of Career Choice Readiness in Senior Pupils

The research featured pedagogical conditions of formation of career choice readiness in high school students. The research objective was to identify, justify, and verify the effectiveness of the pedagogical conditions of formation of career choice readiness in senior pupils. The paper describes specifics of career choice formation and various approaches to the process. It also features structural components of readiness and introduces its authentic interpretation. The essential characteristics of career choice readiness in senior pupils include the ability to analyze alternatives and make decisions based on life experience. The authors propose innovative methods of teaching educational organization to senior pupils and formation of career choice readiness. In addition, the paper justifies involvement of students in the activities of school self-government.
A novel geometric fuzzy membership functions for mouth and eye brows to recognize emotions

Summary In this article, the novel geometrical fuzzy membership functions are proposed to accurately recognize the emotions. The four corner features are taken from both the eyebrows and mouth regions of the expressive static image without considering reference face. The quadrilateral shape is defined from the features and it failed to match with the basic geometric shapes. This quadrilateral is given to the proposed novel fuzzy membership functions to calculate the degree of impreciseness and vagueness exists in the defined quadrilateral. As the result, the 12 fuzzy features are obtained and evaluated by the various classifiers. Both CK and JAFFE datasets are used in the experiment to prove the performance of the proposed method compared to some of the recent methods. It is observed the proposed method gives the highest accuracy rate of 98.4% and 99.15% against contemporary methods. The proposed work achieves the statistical measures of 97.8% of sensitivity, 99.2% of specificity, 97.9% of positive predictive value, and 97.8% of F‐measure. Moreover, the proposed work achieves encouraging results compared to the recent Google API using publicly available datasets. This achievement is due to the fact of exactly identified the impreciseness and vagueness in the defined quadrilateral. The proposed approach avoids reference face and uses the least number of fuzzy features compared to state‐of‐the‐art approaches that reduces the pre‐processing, computational, and learning times.
Video-based salient object detection using hybrid optimisation strategy and contourlet mapping

The advancements in salient object detection have attracted many researchers and are significant in several computer vision applications. However, efficient salient object detection using still images is a major challenge. This paper proposes salient object detection technique using the proposed Spider-Gray Wolf Optimiser (S-GWO) algorithm that is designed by combining Gray Wolf Optimiser (GWO) and Spider Monkey Optimisation (SMO). The technique undergoes three steps, which involves keyframe extraction, saliency mapping, contourlet mapping, and then, fusion of obtained outputs using optimal coefficients. Initially, the extracted frames are subjected to saliency mapping and contourlet mapping simultaneously in order to determine the quality of each pixel. Then, the outputs obtained from the saliency mapping and contourlet mapping is fused using the arbitrary coefficients for obtaining the final result that is employed for detecting the salient objects. Here, the proposed S-GWO is employed for selecting the optimal coefficients for detecting the salient objects. The experimental evaluation of the proposed S-GWO based on the performance metrics reveals that the proposed S-GWO attained a maximal accuracy, sensitivity and specificity with 0.914, 0.861 and 0.929, respectively.
PARTICIPATIVE TEACHER TRAINING IN PEDAGOGICAL UNIVERSITIES

The relevance of the problem of participative training of students in pedagogical universities arises from the socio-economic changes that required a revision of the main positions of higher pedagogical education. Based on the conceptual and terminological analysis, a terminological set of basic concepts of the studied issue was created. These terms include participative, participativity, pedagogical participativity, participative teacher training. Following the analysis of the authors’ understanding of participative training of students in pedagogical universities, the main characteristics of participative teacher training were identified. The article presents the authors’ definition of the concept of participative teacher training, which is understood as a training system (theoretical, methodological, practical) that pursues the formation of participative competence, reflecting the new subject-subject relations of a teacher and a student, characterized by equality, cooperation, co-management, common interests, views and values, creativity and pedagogical empathy. As a result of the theoretical comprehension of the studied issue, a competence-based andprocessmodel of participative teacher training was created. The model reflects the essence and the dynamics of the process under investigation. Theprocess model consists of value-based motivational, activity-contensive and reflective-corrective units. The value-based motivational unit determines the activity that ensures the adoption of pedagogical and universal values forming the internal, emotional orientation of the behavior and the activities of a future teacher. The activity-contensiveunitdetermines the formation of participative competence and the features of this process. The capability for critical introspection and awareness of one’s activity on the basis of reflection characterizes the reflective-corrective unit. The competence-based model represents the formation of participative competence on the part of the formation of participatory competencies: basic and special ones. The basic competencies show in a most general way the participative activities of future teachers while thespecial competencies reflect its specific characteristics. The methodological analysis of the essence of the studied issue made it possible to identify the methodological foundations: system-activity, participative and competence-based approaches.
Pronominal Anaphora Resolution in Hindi Language Using Number Agreement and Animistic Knowledge

Anaphora resolution is always been one of the most active research areas in the domain of Natural Language Processing (NLP). Anaphora resolution refers to the act of referring, it is commonly used for pronoun resolution in discourse. It tends to figure out who or what the referent is used for. Much of the work is been carried out and still going on in this field. A number of algorithms has been proposed to solve and to find out the referent. Anaphora resolution approaches rely on domain knowledge and linguistic. The disadvantage of such approach is that it takes lot of effort and hence is laborious. This paper focuses only on pronominal anaphora resolution for Hindi language sentences that are preprocessed by a part-of-speech tagger. Inputs are checked using number agreement and animistic of the noun phrase. The paper defines resolving anaphora on the basis of limited knowledge of the language. A method called transitive anaphora resolution is also proposed, where the antecedent may not be present in the same or the previous sentence.
Designing Software to Introduce the Musical Instruments

Abstract Identify music equipment is an early stage in the process of learning music, both for theoretical and practical purposes. How the instructor introduces musical instruments is shows the instrument physically. But the way to introduce this is very limited to the tools that are available and relatively inexpensive, such as guitars, violins, and the like. Multimedia computers are now widely used to introduce a particular topic of discussion. One of them is a program that can introduce and teach various types of musical instruments. This software usually contains theory and information about a musical instrument and to test user knowledge, it can be tested through quizzes in this program where all the questions displayed are related to musical instruments. The implementation of this paper is a program that can display musical instruments, sounds, history, and information about this instrument. In addition, there is usually an interactivity like a quiz that can display quizzes about musical instruments by playing their voices and asking where these musical instruments originated.
Enhancing Train Travel with Augmented Reality for Smartphones: The “Tales on Rails” Project

The project “Tales on Rails” describes the research and development of an augmented reality game that can be played on trains with smartphones. The project addresses tourism changes in the digital age; the ubiquitous use of smartphones alters a tourist’s travel experience, from accessing timetables to conveying cultural and historic information or transforming traditional souvenirs. “Tales on Rails” tested a smartphone application allowing tourists to play a game about a crime story on the route between the cities of Lucerne and Interlaken, which is operated by the Swiss railways company zb Zentralbahn AG. The game directly references the environment outside the train on this route as well as cultural themes about the region. The game’s progress is linked to the position of the train, successively unlocking its content on the route. The game is tailored for use by 20 to 30-year-old leisure travellers, and optimized for a group experience in which two to four players collaborate on solving the game; thus, group dynamics during the gaming experience could be investigated. The game features a technologically ambitious program with augmented reality game elements, while minimizing negative impacts on playability by environmental influences such as seasonal and weather conditions. The technical complexity of the concept could be expanded in future iterations. The project “Tales on Rails” was released in July 2019 as fully functional game application in the Google Play Store and the Apple Store.
Implementation Mirror Technique 3D Objects for Interactive Learning Media “Circulatory System” Virtual Reality-Based

Abstract The quality of the education can be improve with the use of learning media. Learning media can be used as the development of teaching methods with conventional techniques into teaching methodes using information technology. One of them is the learning media with utilization of virtual reality technology in the field education, especially in science lesson. Human circulatory system is one of science lesson which is considered diffucult by student. This is because the subject matter have the microscopic organ and process that can not be seen directly. The use of learning media with virtual reality based can increase students curiosity about what is being learned. Application of learning media based on virtual reality called “Bloodyou” is a learning media application about the human circulatory system is equipped with 2D animated content 360 degrees, visualization of organs in 3D and simulation of the circulatory process. The creation of 2D and 3D assets is done using Luther’s multimedia software development method. Testing on making 3D and 2D asset is done by Likert scale calculation on each answer. From the test results obtained the average value of answers that show the results strongly agree with the results of the making of 2D and 3D assets that exist in the application of interactive learning media of the human blood circulation system based on virtual reality.
Deep Learning Aided Friendly Coexistence of WiFi and LTE in Unlicensed Bands

Unlicensed long term evolution (LTE) technology is considered one of promising solutions to address the problem of limited spectrum resources with the booming of big data and internet of things (IoT). However, the unlicensed band is rich resource, which is mainly occupied by wireless fidelity (WiFi). The biggest challenge for the unlicensed LTE technology is how to friendly coexistent with WiFi. Many Researchers in academia and industry have proposed various solutions to deal with this problem. For example, the mLTE-U scheme needs dynamic environmental information to keep it running. In this paper, we propose a classification method based on convolutional neural networks (CNN) algorithm in order to distinguish unlicensed LTE and WiFi. We collect the real data about unlicensed LTE and WiFi. Simulation results show that the identification of two incumbent technology performs well under high signal-to-noise ratios (SNRs).
Threat Identifying Cyber Physical Systems Security

Critical infrastructures are vital assets for the public safety, economic welfare and national security of countries.Cyber systems are used extensively to monitor and control critical infrastructures.A number of infrastructures are connected to the internet via.corporate networks.Cyber security is therefore, an important item of the national security agenda of a country.These systems will empower our critical infrastructure and have the potential to significantly impact our daily lives as they form the basis for emerging and future smart services.On the other hand, the increased use of CPS brings more threats that could have major consequences for users but advances in technology make it necessary to develop new threats will continue to be exploited and cyber attacks will continue to emerge, hence, the need for new methods to protect CPS.This study introduces a novel framework for understanding cyber attacks and the related risks of multi elements, multi-layered to cyber-physical systems.The longer-term goal is to use the framework as a means to reduce cyber-physical system security properties and to enumerate the principles for designing systems that are resilient to cyber attacks and analysis of the security issues at the various layers of CPS architecture, risk assessment and techniques for securing CPS.Finally, challenges and possible solutions are presented and discussed.
Storytelling and event concepting

This chapter will thoroughly discuss the application of storytelling principles while creating the concept for a new event. The advantages of storytelling will be presented by comparing it to a conventional 5Ws approach. The basic principles and elements of storytelling will be explained and discussed. The second part of the chapter will focus on the application of several design techniques to organise and visualise event concepts and design ideas. Firstly, brainstorming methods will be explored. Secondly, creative tools such as mind mapping, mood boards and sketching will be introduced and explained.
Gaussian Mixture Reduction Methods Using Support Vector Machine

Gaussian mixture model is a useful distribution for statistical methods such as statistical static timing analysis, but the number of components of Gaussian mixture model increases exponentially by repeating statistical operations. Hence, the number of components must be reduced to around 2 in order to repeat operations efficiently and effectively. Although several methods for reducing the number of components have been proposed, each of them has strength and weakness in accuracy and CPU time. Therefore, selecting an appropriate method for an input distribution is a practical way for reducing the number of components. In this paper, we construct selection methods by using support-vector machine and evaluates their performances. Since the input distribution is represented by non-linear functions, it is not easy to select an appropriate method, but support vector machine achieved 94.8% correct selection rate.
Coarse-to-Fine Deformable Model-Based Kidney 3D Segmentation

We present a 3D segmentation algorithm which can extract kidney from CT images. The contribution of this paper is twofold: one is a deformable model-based kidney segmentation algorithm which directly provides users with a surface mesh model of kidney; the other is a coarse-to-fine update method of deformable model by adding vertices which decreases computing resource consumption of the segmentation algorithm. This algorithm is based on a deformable mesh model that interacts with intensity value of CT images. The mesh model is firstly initialized as a tessellated sphere with whose center lying inside the kidney. After initialization, the mesh model evolves to fit the actual kidney surface over iterations. At the same time, as a coarse-to-fine process, the total of vertices in the mesh model gradually increases. The algorithm has been tested on data sets from a wide variety of scan parameters. Evaluation against manual segmentation was carried out. Jaccard similarity index and sensitivity are used to measure the performance of the algorithm. Experimental results show that the algorithm is accurate, yielding an average Jaccard index of 90%.
A clustering algorithm for ipsative variables

The aim of this study is to introduce a new clustering method for ipsatives variables. This method can be used for nominals or ordinals variables for which responses must be mutually exclusive, and it is independent of data distribution. The proposed method is applied to outline motivational profiles for individuals based on a declared preferences set. A case study is used to analyze the performance of the proposed algorithm by comparing proposed method results versus the PAM method. Results show that proposed method generate a better segmentation and differentiated groups. An extensive study was conducted to validate the performance clustering method against a set of random groups by clustering measures.