Reviewerly/exHarmony huggingface dataset:
Digital Drawing and Painting in the Training of Bachelors of Professional Education: Experience of Blended Learning

The aim of the study is to solve the problem of insufficient training of specialists who are able to meet the growing demand for projects in the digital design and computer games industry. Based on the analysis of the content of digital drawing and painting, two elective courses were implemented. They are: “Digital drawing with the basics of composition” and “Digital painting with the basics of color” for Bachelors of Professional Education majoring in digital technologies. In order to properly organize the process of blended learning, which was caused by quarantine restrictions due to the COVID-19 pandemic, appropriate tools to be used in the process of studying these courses, were selected. The Moodle distance learning platform and cloud technologies (Google Docs, Google Drive) were used to present theoretical material and set tasks for practical study. For operative communication with students VoIP program Discord was used. To develop students’ skills of drawing from life and high-speed drawing we offered such Internet resources as Line of action, Character designs, Bodies in Motion, Human anatomy for artist. Approbation of the developed courses in the institution of higher education in the conditions of blended learning and the results of the survey of students proved the effectiveness of the developed courses.
Pixel Corrected Adaptive Conductance Function based Diffusion Filter and Image Denoising using Bi-dimensional Empirical Mode Decomposition

In this paper, a method is proposed to denoise image based on semi-adaptive conductance function in anisotropic diffusion filter and bi-dimensional empirical mode decomposition. Here, the color image is utilized in the work to split the image into red, green and blue channels. To each channel component, the local difference value method is implemented where the noise contaminated pixels of the respective channel are replaced with the processed ones in which a Gaussian filter is used for smoothing. The bi-dimensional empirical mode decomposition decomposes the channel into its constituent intrinsic mode functions and the diffusion filter is applied to them. The various function parameters define the extent of diffusion to the channel like connectivity, conductance function, number of iterations and gradient threshold. Here, the conductance function is made semi-adaptive by introducing the gradient value of the image to the threshold parameter along with a preset constant term. The processed intrinsic mode functions, obtained by applying the modified diffusion filter in the conductance function, of each channel are combined and the final image is reconstructed by merging all the three channels. The experimented results thus obtained are evaluated and compared with other existing techniques based on the performance parameters like peak signal-to-noise ratio, mean square error and, structural similarity index and concluded that the proposed method is superior and efficient in both image denoising and feature retention.
Beyond a binary of (non)racist tweets: A four-dimensional categorical detection and analysis of racist and xenophobic opinions on Twitter in early Covid-19

Transcending the binary categorization of racist and xenophobic texts, this research takes cues from social science theories to develop a four-dimensional category for racism and xenophobia detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of deep learning techniques, this categorical detection enables insights into the nuances of emergent topics reflected in racist and xenophobic expression on Twitter. Moreover, a stage wise analysis is applied to capture the dynamic changes of the topics across the stages of early development of Covid-19 from a domestic epidemic to an international public health emergency, and later to a global pandemic. The main contributions of this research include, first the methodological advancement. By bridging the state-of-the-art computational methods with social science perspective, this research provides a meaningful approach for future research to gain insight into the underlying subtlety of racist and xenophobic discussion on digital platforms. Second, by enabling a more accurate comprehension and even prediction of public opinions and actions, this research paves the way for the enactment of effective intervention policies to combat racist crimes and social exclusion under Covid-19.
Research on online medical community doctor recommendation based on information fusion

At present, more and more methods are used to solve the problem of doctor recommendation in online medical community. LDA topic model, vector space model, AHP method, knowledge map and other methods have shown good characteristics and recommendation effect. However, different recommendation methods often get different results, which often leads to inconsistent recommendation, so there are some deficiencies. This paper introduces information fusion technology to fuse the recommendation results obtained by different recommendation methods, obtains further optimization results, and solves the problem of inconsistent results obtained by different recommendation methods. Based on LDA model and word2vec model, this paper established a doctor recommendation model based on information fusion. In the empirical research, Haodaifu (www.haodf.com) has been selected as the research object. The empirical results show that the doctor recommendation model based on information fusion is better than the separate LDA topic model and word2vec model in accuracy and effectiveness.
Research on Search Engine Optimization Method of Innovation and Entrepreneurship Platform

In this paper, the workflow of search engines is introduced, and the optimization strategies of search engines are discussed and researched. For the innovation and entrepreneurship platform, we use the "comprehensive index evaluation method" in search engine optimization, and on this basis, an improvement plan was proposed. Through the comparison before and after the improvement, the keyword selection is proposed as the further optimization direction. After determining the search engine optimization strategy, this paper proposes a method for constructing an innovation and entrepreneurship platform based on the MVC model.
Importing Eye Tracking Regarding with Human Aspects in Software Quality

Eye-tracking technology can positively give support to the need for investigation on user behavior when working with a computer, which allows obtaining analysis in the form fixation point (location of a user's eye gaze), scan-path (gaze trajectory), heat maps (areas of interest), salient picture components. Eye-tracking research has been increasingly adopted to improve the design and usability of human-computer interactions and explore a comprehension of how they guide users. Therefore, this technology has a potential prospect to be used in software design and development. In this paper, we are considering the possibility of using eye-tracking technology regarding human aspects in software quality. It is expected that this novel technique will have an effective impact in this field.
ExamChain: A Privacy-Preserving Onscreen Marking System Based on Consortium Blockchain

The development of educational informatization makes data privacy particularly important in education. With society's development, the education system is complicated, and the result of education evaluation becomes more and more critical to students. The evaluation process of education must be justice and transparent. In recent years, the Onscreen Marking (OSM) system based on traditional cloud platforms has been widely used in various large-scale public examinations. However, due to the excessive concentration of power in the existing scheme, the mainstream marking process is not transparent, and there are hidden dangers of black-box operation, which will damage the fairness of the examination. In addition, issues related to data security and privacy are still considered to be severe challenges. This paper deals with the above problems by providing secure and private transactions in a distributed OSM assuming the semi-trusted examination center. We have implemented a proof-of-concept for a consortium blockchain-based OSM in a privacy-preserving and auditable manner, enabling markers to mark on the distributed ledger anonymously. We have proposed a distributed OSM system in high-level, which provides theoretical support for the fair evaluation process of education informatization. It has particular theoretical and application value for education combined with blockchain.
Quasi‐min‐max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

Abstract This paper proposes a new visual servoing quasi‐min‐max MPC algorithm for stabilization control of an omnidirectional wheeled mobile robot subject to physical and visual constraints. The visual servoing dynamics of the robot are modeled as the state‐dependent linear error system with nonlinear control inputs of rotation and deflection velocities of wheels. The state‐dependent linear error system is covered as linear parameters‐varying models which is used to design the visual servoing quasi‐min‐max MPC controller. The actual control inputs of the robot are then computed by the solution of an inverse algebraic equation of the MPC actions. The recursive feasibility and stability of the new visual servoing MPC are ensured by some LMIs conditions. The performance and practicability of the visual servoing MPC are verified by some simulation and experiment results.
The need to provide the priority of spiritual and educational processes in the modern education system

Thanks to gaining independence, our people began to realize a new stage in their development -the acquisition of inheritance rights in the conditions of national revival and cultural renewal. At the same time, it is no secret to anyone that the interaction of global events with global civilization to some extent affects our spirituality, our worldview, our way of thinking. Indeed, today's world is not limited to one nation, one country living on its own territory. On the contrary, they are united by the common interests of the peoples of the world. Any particular problem is equally important for everyone. The number of issues of global importance is growing. It has a profound effect on human history and destiny, present and future. The deeper we understand the nature and essence of these problems, the deeper we feel that mental pain, mental discomfort is a big threat. This article discusses the issue of the priority of spiritual and educational processes in the modern education system.
The Application of Connotative Teaching in Private Colleges Based on Kano Model

Connotative development of higher education is of great significance in China. To promote this endeavor, this paper has established a connotative teaching system based on the Kano model and has applied it in the teaching of statistics. Guided by the fundamental demands of students, this system is built from the four perspectives, including students, third-party evaluation institutions, schools, and morality and law. It also takes the advantages of the Kano model, featuring the three dimensions of essential demand quality, expected demand quality, and charismatic demand quality. The paper also describes a two-year experiment to test the effect of the Kano-model-based connotative teaching system. It has collected more than 200 effective samples and compared the performance of students in private colleges. The results are as follows: the connotative teaching of statistics guided by the Kano model can meet the demands of students in private colleges and improve their comprehensive scores. This model shows remarkable performance and will promote the reform on connotative teaching.
Use of Augmented Reality Technology in Marketing

Augmented reality technology is accepted in different fields today. Marketing is one of the areas where this new generation technology is widely used. This technology, which enables customers to gain experience between the virtual world and the real world, regardless of time and place, in order to ensure sustainable purchasing behavior, should be considered as a gateway to the changing world of marketing. In addition to its use in the fields of augmented reality, health, defense, education, engineering, architecture, media, it has also been effective in the acceptance of institutions/organizations, brands, and social media by wider customers/users. Provided that this technology is implemented in all marketing strategies, it contributes to gaining competitive advantage in the market. In this chapter, augmented reality technology will be discussed first. In the rest of the chapter, the application of this technology to marketing strategies will be explained with examples.
Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data

This paper proposes an effective and optimal sentiment classification method named Penguin Rider optimization algorithm-based Deep Recurrent Neural Network (PeROA-based Deep RNN) to perform sentiment classification using political reviews. However, the proposed PeROA is developed by incorporating the Penguins Search Optimization Algorithm (PeSOA) with the Rider Optimization Algorithm (ROA). The sentiment classification process is progressed using the Deep RNN classifier, which in turn generate the optimal solution based on the fitness measure. Accordingly, the function with the minimal error value is accepted as the best solution. The sentiment-based features enable the classifier to perform better classification result with respect to the sentiment tweets. However, the proposed PeROA-based Deep RNN obtained better performance using the metrics, like accuracy, sensitivity, specificity, recall, F-measure, thread score, NPV, FPR,FNR and FDR with the values of 92.030%, 92.030%, 92.235%, 92.030%, 92.030%, 92.030%, 92.030%, 3.105%, 3.11%, and 3.105%, respectively.
Machine Learning-Based Sentiment Analysis Towards Indian Ministry

This paper presents the performance of Twitter sentiment analysis on the Agricultural Ministry of India and also determines the optimal AI model between random forest (RF) and k nearest neighbours (KNN) algorithms. 'Twitter and Reddit Sentimental Analysis' dataset from Kaggle is used to train and test the RF and KNN AI models. Term frequency-inverse document frequency (TF-IDF) vectorizer is used to extract features from the obtained dataset. The optimal AI model is determined using the library functions of scikit-learn-2.0 which perform the training and testing of the dataset. Twitter authenticated API called Tweepy is used to create the dataset required to determine the public sentiment towards the Agriculture Ministry by us. This dataset includes 6000 tweets related to the Agriculture Ministry. The Twitter sentiment analysis performed determines what percentage of public opinion towards the Agriculture Ministry is positive, negative and neutral.
Medical image Denoising using Scaled Dual Convolutional Neural network (SD-CNN)

In medical image analysis, image denoising is a critical pre-processing step. Various algorithms with various denoising performances have been proposed during the last three decades. Deep learning-based models have recently showed enormous potential, outperforming all conventional methods. However, these methods are hampered by the need for a large training sample size and high computing costs. We show in this research a novel method built using convolutional layers which can be employed for efficient denoising of medical images with limited sample sizes. We provide a novel method for achieving the following goals by combining the qualities of various algorithms in this research. (1) Improved performance (2) Faster processing (3) Reduced denoiser complexity (4) Extracting complementary features to increase the denoiser's total capacity. We tested our method on retinal images (STARE dataset) in terms of qualitative and quantitative analysis. According on simulation results, the proposed technique Scaled Dual CNN (SD-CNN) outperforms earlier methods.
Directed Undersampling Using Active Learning for Particle Identification

Accurate particle identification is an ongoing task in the European organization for nuclear research, known as CERN where the challenge remains that targeted particles/events represent tiny minorities in front of the overwhelming presence of common particles such as protons. This paper presents a directed undersampling using an active learning method named DUAL to handle the high imbalance problem present in the particle identification dataset. The proposed approach was used to reduce the training set size while maintaining classifiers' performance. Compared against various imbalance learning approaches, the experimental results show that using DUAL as a data reduction technique with a random forest classifier enhances classification performance in terms of Macro- $$F_1$$ score and decreases the training time needed to train the models, which is very relevant while dealing with large-scale datasets. Despite being experimented only with particle identification dataset, we believe that DUAL could be adopted as a generic method for multi-class imbalanced classification problems with big data scale difficulties.
Study of Context-based Personalized Recommendations for Points of Interest

Location-based services are essential to delivering information for users in the context of travel, leisure, and sports application. Nevertheless, these services are often implemented as recommendations and suggestions that may overwhelm users, or fail to adapt to their goals, behavior, and context. To address these limitations, this paper presents NearMe, an application that provides tailored recommendations of Points of Interest surrounding the user. Beyond existing approaches, NearMeallows the generation of dynamic recommendations from heterogeneous service providers, and the definition of regions to which notifications are related. Moreover, it allows to fine-tune notifications, thus preventing over-information and noise. A preliminary study has been conducted involving a heterogeneous group of potential users and service providers that elaborates on their vision, expectations, features desiderata, and possible interfaces.
An Intelligent Robustness Optimization Method for Internet of Things Using Graph Neural Networks

research-article Share on An Intelligent Robustness Optimization Method for Internet of Things Using Graph Neural Networks Authors: Yabin Peng Institute of Information Technology, PLA Strategic Support Force Information Engineering University, China Institute of Information Technology, PLA Strategic Support Force Information Engineering University, ChinaView Profile , Caixia Liu Institute of Information Technology, PLA Strategic Support Force Information Engineering University, China Institute of Information Technology, PLA Strategic Support Force Information Engineering University, ChinaView Profile , Shuxin Liu Institute of Information Technology, PLA Strategic Support Force Information Engineering University, China Institute of Information Technology, PLA Strategic Support Force Information Engineering University, ChinaView Profile , Kai Wang Institute of Information Technology, PLA Strategic Support Force Information Engineering University, China Institute of Information Technology, PLA Strategic Support Force Information Engineering University, ChinaView Profile Authors Info & Claims ICCIP '21: Proceedings of the 7th International Conference on Communication and Information ProcessingDecember 2021 Pages 171–175https://doi.org/10.1145/3507971.3508000Published:07 March 2022Publication History 1citation32DownloadsMetricsTotal Citations1Total Downloads32Last 12 Months32Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Technological University Faculty ICT Barriers During the Pandemic

The current study aims to identify the ICT barriers that educators faced after the outbreak of the pandemic. To achieve these objectives, the authors surveyed 62 faculty members at the Kazan National Research Technological University. Attitudes towards thirty-three barriers were evaluated by Fisher angular transformation co-efficient tests depending on respondents’ faculty position, scientific degree, teaching experience, age, gender, and group of taught disciplines. As a whole, the conducted research revealed that the issue of insufficient technical support at Kazan National Research Technological University is so urgent that it requires an immediate solution from the university administration. The urgent nature of the problem is confirmed by the fact that educators evaluated this barrier similarly regardless of position, length of service, age or gender. We concluded that special attention and providing support as regards ICT barriers might be important for younger faculty members, educators with smaller teaching experience and those delivering social and humanitarian subjects.
The concept of dynamicity of martial arts office building design with environmentally friendly

Abstract The demand for office building for the sports branch of martial arts in Indonesia has increasingly risen in line with the rapid development of the international arena. For the administrative region, including the province of East Java, a martial arts head office is essential the administrative duties and management of the sports organisation. A martial arts office is an office to accommodate administrative and management tasks. It also requires facilities for users who mostly have an athletic background and are physically fit and well-being. Therefore, this paper aims to explore and propose architectural designs of martial arts’ administrative office from finding the idea, determining the design’s parameters and concept to formal design transformation. Presenting big ideas by translating the character and value of sport is applied during the design process and considering the green design approach to provide building’s energy-efficient. The result proposes a building design that expresses the dynamicity of sport’s value and characters. The building is also designed by reducing negative impacts on the environment and maximise energy saving.
A New Measure of Similarity in Textual Analysis: Vector Similarity Metric versus Cosine Similarity Metric

ABSTRACT This paper proposes a new similarity metric, Vector Similarity Metric (VSM), which is as simple as the popular Cosine Similarity Metric (CSM). The CSM has a major deficiency. It yields the same value, irrespective of how different the two vectors are in their sizes so long as the angle between them is the same. This deficiency remains intact even when Natural Language Processing is used to associate semantic meanings to the words/phrases and when the term frequency is modified using Inverse Document Frequency. This deficiency becomes a serious concern when one is comparing the risk profile of one company with the risk profile of another company or investigating the changes in the risk profile of a company from one year to another. The VSM is based on the difference of the two vectors. The paper demonstrates the superiority of VSM over CSM analytically and through real-world examples.