Reviewerly/exHarmony huggingface dataset:
Survey on face recognition

Face detection and recognition have been explored with various supervised and unsupervised learning techniques. The foundation of all the algorithms is to detect the feature vectors from the face (eye height & width, lip width, ratio of distance between eye to nose etc..).Various classification algorithms are compared with different combination to analyze and decide which combination operates best. This paper, explains the various existing methods based on their accuracy on different kinds of datasets.
Current Issues of Higher Education Pedagogy and University Teachers’ Skills Development

The article addresses the current issues of pedagogy of higher education that should be presented in skills development programs in consideration of aerospace education specifics. According to the contemporary Russian and foreign researches, current issues of pedagogy are concentrated in such areas as the development of education in connection with the formation of learning and teaching processes. It is feasible to discuss systematically these problems in teacher training and professional development courses and formulate scientific and practical recommendations. The article dwells on the three research areas in pedagogy of higher education: 1) features of the modern educational process; 2) student as a subject of educational activities; 3) university teacher as a subject of pedagogical activities. A student as a subject of learning is characterized not only by criteria of his cognitive and motivational development, but also in the context of mental health risks in the conditions of challenges and threats of the digital world. The analysis of modern student’s value orientations is an important aspect of higher education pedagogy in order to influence teaching and educational processes. A teacher as a subject of pedagogical activity is studied from the standpoint of his professional success. Such factors of his professionalization as motivation, pedagogical communication, stress resistance in various working conditions are considered.
THE CONSTRUCTION OF PUBLIC SPORTS SERVICE GUARANTEE SYSTEM IN A HEALTHY COMMUNITY IN THE BACKGROUND OF PUBLIC HEALTH

ABSTRACT Community sports are a very important part of urban public service. It directly affects the quality of life of residents and plays an important role in improving the health level of the masses. However, there are still many problems in the current public sports service system, which hinders the provision of public health. In order to ensure the effectiveness of the indicators of community sports service guarantee system, this study uses the fuzzy analytic hierarchy process to construct the community public sports service guarantee system, and verifies the effectiveness of the system constructed by this method through an example application. The results show that the weight of each index from large to small is service effectiveness &gt; residents' demand &gt; service capacity &gt; service content; the weight of service effectiveness is 48.46%, which is an important indicator of service guarantee system. In addition, the effectiveness of the community sports service system was verified, and the CR value of the evaluation index system was greater than 0.1, which showed that the consistency of the evaluation index system met the requirements. The public sports service guarantee system constructed by the research institute has good applicability and high practical value. I hope that the research results can provide some reference for the improvement of grass-roots sports service construction, and provide some theoretical support for the construction of grass-roots service evaluation system.
Internet of Things-Based Solar Tracker System

Internet of Things (IoT) technologies, along with economies of scale and advances in hardware, software, and network technologies, have accelerated the explosion of connected objects across the Internet. A connected object can be controlled online from an IoT platform and can send, receive, and process various and varied data. In this chapter, we leverage some of the IoT technologies to propose a simple and low-cost IoT solution to monitor and control a smart dual-axis solar tracker system for performance evaluation. The solution also includes alert notifications to inform a remote user through phone or mail (or both) when a sensor has reached a certain predefined event. The solution is designed based on low-cost and easy-to-use hardware and software and an online open-source IoT platform. The design aspects of the IoT-based solar tracker are extensively described in this chapter. Moreover, a prototype of the IoT-based solar tracker has been manufactured and tested. Test results demonstrate that solar tracker data can be sent easily and properly and can be directly monitored online, as well as the solar tracker, can take commands from the IoT monitoring application.
Scene Text Detection Based On Fusion Network

Due to the robustness resulted from scale transformation and unbalanced distribution of training samples in scene text detection task, a new fusion framework TSFnet is proposed in this paper. This framework is composed of Detection Stream, Judge Stream and Fusion Stream. In the Detection Stream, loss balance factor (LBF) is raised to improve the region proposal network (RPN). To predict the global text segmentation map, the algorithm combines regression strategy and case segmentation method. In the Judge Stream, a classification of the samples is proposed based on the Judge Map and the corresponding tags to calculate the overlap rate. As a support of Detection Stream, feature pyramid network is utilized in the algorithm to extract Judge Map and calculate LBF. In the Fusion Stream, a new fusion algorithm is raised. By fusing the output of the two streams, we can position the text area in the natural scene accurately. Finally, the algorithm is experimented on the standard data sets ICDAR 2015 and ICDAR2017-MLT. The test results show that the [Formula: see text] values are 87.8% and 67.57%, respectively, superior to the state-of-the art models. This proves that the algorithm can solve the robustness issues under the unbalance between scale transformation and training data.
Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects

Side effects during neural network tuning are typically measured by overall accuracy changes.However, we find that even with similar overall accuracy, existing tuning methods result in non-negligible instance-wise side effects.Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus propose to conduct neural network surgery by only modifying a limited number of parameters.Neural network surgery can be realized using diverse techniques, and we investigate three lines of methods.Experimental results on representative tuning problems validate the effectiveness of the surgery approach.The dynamic selecting method achieves the best overall performance that not only satisfies the tuning goal but also induces fewer instance-wise side effects by changing only 10 -5 of the parameters.
Matrix analysis for fast learning of neural networks with application to the classification of acoustic spectra

Neural networks are increasingly being applied to problems in acoustics and audio signal processing. Large audio datasets are being generated for use in training machine learning algorithms, and the reduction of training times is of increasing relevance. The work presented here begins by reformulating the analysis of the classical multilayer perceptron to show the explicit dependence of network parameters on the properties of the weight matrices in the network. This analysis then allows the application of the singular value decomposition (SVD) to the weight matrices. An algorithm is presented that makes use of regular applications of the SVD to progressively reduce the dimensionality of the network. This results in significant reductions in network training times of up to 50% with very little or no loss in accuracy. The use of the algorithm is demonstrated by applying it to a number of acoustical classification problems that help quantify the extent to which closely related spectra can be distinguished by machine learning.
Supporting Spontaneous Museum Visits by Deaf People: An Augmented Reality Application and a Case Study

This chapter presents research that aims to identify accessibility characteristics necessary for a satisfactory interaction of prelingual deaf users with the contents of a museum exhibition. To achieve this goal an application was developed and a case study carried out at the UFRJ (Federal University of Rio de Janeiro) Geodiversity Museum. Augmented reality was chosen in order to provide different types of information about one of the museum’s exhibition rooms. The application was submitted to an accessibility evaluation with experts and, subsequently, to tests with users, involving five prelingual deaf volunteers. The users’ observations occurred in the real context and identified the demands of the deaf and the effects of providing accessibility with the help of augmented reality.
Pengembangan buku ajar digital Geografi SMA berplatform aplikasi 3D Page Flip dengan pendekatan STEAM (Science, Technology, Engineering, Art, Mathematic) materi keragaman budaya di Indonesia

Today students live in a digital era. Such conditions affect the changing needs of textbooks as a source of learning for students. The purpose of this development research is to produce a digital textbook on cultural diversity in Indonesia with a 3D page flip application platform with a STEAM approach (Science, technology, engineering, art, mathematics). The design of this study uses the ADDIE model. Product trial results show that the percentage of the feasibility of textbooks is as much as 87 percent, which means it is very feasible to use. While the results of students' understanding test scores in the range of 76-85 with a percentage of 53 percent. These results indicate that students have a high level of understanding criteria.
Deep Learning Method for Handwriting Recognition

The advancement of technology nowadays resulted into documents, such as forms and petitions, being filled out in computer and digital environment. Yet in some cases, documents are still preserved in traditional style, on print. Due to its distinct proportions, however, its storage, sharing and filing has become a complication. The relocation of these written documents to digital environment is therefore of great significance. In this view, this study aims to explore methodologies of digitizing handwritten documents. In this study, the documents converted to image format were pre-processed using image processing methods. These operations include dividing lines of the document into image format, dividing into words which then divided into characters, and finally, a classification operation on the characters. As classification phase, one of the deep learning methods is the Convolution Neural Network method is used in image recognition. The model was trained using the EMNIST dataset, and in the character, dataset created from the documents at hand. The dataset created had a success rate of 87.81%. Characters classified as finishers are sequentially combined and the document is transferred to the computer afterwards.
Professional and personal development of university students as an axiological resource

The article deals with the conceptual provisions of the professional and personal development of university students as an axiological resource. The review and analysis of the pedagogical theory and practice of scientific literature is carried out. The features of modern professional training of university students are revealed. The definition of the category “professional and personal development of university students” is given. The developed model of the value attitude of university students to professional activity, contributing to professional and personal development, is proposed.
CNN-DMA

Memory bandwidth utilization has become the key performance bottleneck for state-of-the-art variants of neural network kernels. Current structures such as depth-wise, point-wise and atrous convolutions have already introduced diverse and discontinuous memory access patterns, which impact efficient activation supply due to more frequent cache misses and consequently high-penalty DRAM pre-charging. To handle this, GPU achieves efficient parallelization with sophisticated optimization of CUDA program to reduce memory footprints, which demands high engineering efforts. In this work, we in contrast propose a programmable direct memory access engine for convolutional neural networks (CNN-DMA) supporting a fast supply of activation for independent and scalable computing units. The CNN-DMA favours a predictable activation streaming approach which completely avoids penalties by bus contention, cache misses and less carefully designed low-level programs. Furthermore, we enhance the baseline DMA with the capability of out-of-order data supply to filter out unique sliding-windows to boost the performance of the computing infrastructure. Experiments on state-of-the-art neural networks show that CNN-DMA achieves optimal DRAM access efficiency for point-wise convolution layers, while reduces 30% to 70% rounds of computation with sliding-window filtering.
Implementation of the Principle of Decent Wages in Russian Education: Economic and Statistical Study

The article presents the results of economic and statistical study assessing the implementation of the principle of decent wages for employees in general education. According to the author, at present, determining the amount of funding for teachers’ salaries is caught in the trap of using the basic criterion defined as the «average salary in the region», which does not allow a significant part of specialists to receive decent remuneration for their work and provokes a high staff turnover. In order to eliminate negative trends, the author proposed adjusting the principle for payment of salaries for school teachers, with an emphasis on increasing the minimum wage rather than the average wage. The study proposes a modified method for calculating the leading coefficient. This indicator makes it possible to correlate the growth rates of teachers' salaries with the basic (starting) conditions prevailing in the regions at the beginning of the implementation of the Decree of the President of the Russian Federation No. 597 of 7 May 2012 «On Measures for the Implementation of State Social Policy» and also takes into account the uneven distribution of the salary fund for school teachers (by comparing the achievement of the May decree target in regions with the number of specialists receiving a minimum salary or less). The modified methodology was tested on statistical data for 83 constituent entities of Russia. The approbation showed that in 2019 11 constituent entities of the Russian Federation could not provide even the minimum conditions for a decent salary for all school teachers (in 2015, they included 3 territories, in 2017 – 13). All the necessary conditions for the implementation of the principle of decent salaries are created only in three constituent entities of the Russian Federation. The conclusion summarizes possibilities of practical use of regional experience in the implementation of the «All included» model (based on the establishment of a single salary for all professional activities in the school) to improve the planning of teachers' salaries in the constituent entities of the Russian Federation.
Model to Form Professionally Oriented Foreign-Language Communicative Competence of Students in the Field of Training “Foreign Regional Studies”

The research objective includes developing a model to form professionally oriented foreign-language communicative competence of future bachelors in the field of training “Foreign Regional Studies”. The article analyses requirements of the Federal State Educational Standard of Higher Education 3++ for professional competence of students - future specialists in the field of foreign regional studies. Within the framework of the social demand approach, the authors examine requirements of the labour market for a specialist in the field of foreign regional studies. The researchers reveal the structure of professionally oriented foreign-language communicative competence taking into account specificity of training in the field of foreign regional studies; propose a flexible system to assess the level of professionally oriented foreign-language communicative competence, which constitutes scientific originality of the paper. The research findings are as follows: the authors develop a model to form professionally oriented foreign-language communicative competence of students in the field of training “Foreign Regional Studies”, describe the model structure taking into account specificity of training in the field of foreign regional studies.
Principles and Patterns of Interaction Design under Concept of Calm Technology

Calm technology is a design concept based on ubiquitous computing. It advocates that products should fully consider the natural human-computer interaction environment and should not be designed for the purpose of occupying users' attention. Instead, products can be calmly integrated into the environment through “ubiquitous computing”. As informatization progresses in nowadays industrial products, the parallel, mobile and multi-device environment makes attention become a scarce resource. Occupation of attention is often the essence of product interaction design. Calm technology advocates that the law of human attention should be fully considered, and the principle of minimizing attention input should be taken as the principle, and “peripheral interaction” should be used for interaction design. According to the different classes of activities in everyday life and the goal of leveraging the periphery of our attention, the approach among, low-resolution information display and switching in horizon activities, unconscious peripheral operation in secondary activities, and supporting and informing in primary activities, can be applied as interaction design principles.
‘Thy Algorithm Shalt Not Bear False Witness’: An Evaluation of Multiclass Debiasing Methods on Word Embeddings

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural language processing domain, it has been shown that social biases persist in word embeddings and are thus in danger of amplifying these biases when used. As an example of social bias, religious biases are shown to persist in word embeddings and the need for its removal is highlighted. This paper investigates the state-of-the-art multiclass debiasing techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It evaluates their performance when removing religious bias on a common basis by quantifying bias removal via the Word Embedding Association Test (WEAT), Mean Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias (RNSB). By investigating the religious bias removal on three widely used word embeddings, namely: Word2Vec, GloVe, and ConceptNet, it is shown that the preferred method is ConceptorDebiasing. Specifically, this technique manages to decrease the measured religious bias on average by 82.42%, 96.78% and 54.76% for the three word embedding sets respectively.
Potensi Laboratorium Alam Sumbermanjing Wetan dalam pembelajaran Geografi berbasis kerja lapangan (fieldwork)

This research is a qualitative descriptive study using secondary data as the main data. Data were collected through literature studies from the results of previous research in Sumbermanjing Wetan District. The results showed that Sumbermanjing Wetan had the potential to be used as a natural laboratory in the context of geography learning. This is supported by physical conditions in the form of diverse landscape features, quite complex social and cultural conditions. This potential includes the geological and geomorphological conditions of Sumbermanjing Wetan, the geodiversity and biodiversity of Sempu Island, social and cultural conditions, disasters, and tourism. In addition, the existence of natural resources in the area can also be used as one of the studies in fieldwork. Fieldwork-based learning can increase the skills of students in making observations, identifying, experimental, doing teamwork, and applying the use of technology. Geography learning topics at Sumbermanjing Wetan include physical, social, tourism, disaster, environmental, and other areas of geography. Indeed, implementing fieldwork needs to be independent, especially in managing data. In addition, fieldwork-based learning will continue to develop in line with the convenience of technology and increasingly advanced research studies with challenges faced including security, finance, health and safety, access to the field, and damage to the surrounding area used for fieldwork due to sampling exaggerated.
OPriv: Optimizing Privacy Protection for Network Traffic

Statistical traffic analysis has absolutely exposed the privacy of supposedly secure network traffic, proving that encryption is not effective anymore. In this work, we present an optimal countermeasure to prevent an adversary from inferring users’ online activities, using traffic analysis. First, we formulate analytically a constrained optimization problem to maximize network traffic obfuscation while minimizing overhead costs. Then, we provide OPriv, a practical and efficient algorithm to solve dynamically the non-linear programming (NLP) problem, using Cplex optimization. Our heuristic algorithm selects target applications to mutate to and the corresponding packet length, and subsequently decreases the security risks of statistical traffic analysis attacks. Furthermore, we develop an analytical model to measure the obfuscation system’s resilience to traffic analysis attacks. We suggest information theoretic metrics for quantitative privacy measurement, using entropy. The full privacy protection of OPriv is assessed through our new metrics, and then through extensive simulations on real-world data traces. We show that our algorithm achieves strong privacy protection in terms of traffic flow information without impacting the network performance. We are able to reduce the accuracy of a classifier from 91.1% to 1.42% with only 0.17% padding overhead.
End-to-end pattern optimization technology for 3D shape measurement

The nonlinear errors caused by the gamma effect seriously affect the measurement accuracy of phase-measuring profilometry systems. We present a novel robust end-to-end pattern optimization technology for phase-height mapping. This method generates standard sinusoidal patterns on the reference plane on a camera image instead of on a projection image by optimizing the projection image, which can avoid phase errors due to phase conversion from projector to camera. The pixel set mapping is achieved by chessboard calibration; then the projection image is optimized with the multiscale optimization method and the dislocation optimization method based on proportional integral derivative control. The experiments show that the proposed method can effectively avoid the influence of nonlinear effect and achieve high-quality three-dimensional measurement.
US-China relations: Towards strategic partnerships

The influential Aspen Strategy Group’s framing of US-China relations suffers from a groupthink mentality. More meaningfully, the US strategy ought to reflect an immediate end and reversal of Trumpi...