Reviewerly/exHarmony huggingface dataset:
Virtual Reality in Object Location

Currently, virtual reality (VR) is a computer technology that is growing in terms of developments and discoveries. Virtual reality has been introduced in different areas due to the growing interest it has caused in people. The development of applications with virtual reality is increasingly varied, covering activities, tasks, or processes of everyday life in the fields of industry, education, medicine, tourism, art, entertainment, design, and modeling of objects, among others. This chapter will focus on describing the latest advances and developments in virtual reality within the scope of representing reality in the process of locating objects. With the support of virtual environments and intelligent virtual agents, the author has managed to develop a computational model that generates indications in natural language, for the location of objects considering spatial and cognitive aspects of the users.
Modification of Valiant’s Parsing Algorithm for the String-Searching Problem

Some string-matching problems can be reduced to parsing: verification whether some sequence can be derived in the given grammar. To apply parser-based solutions to such area as bioinformatics, one needs to improve parsing techniques so that the processing of large amounts of data was possible. The most asymptotically efficient parsing algorithm that can be applied to any context-free grammar is a matrix-based algorithm proposed by Valiant. This paper presents a modification of the Valiant’s algorithm, which facilitates efficient utilization of modern hardware in highly-parallel implementation. Moreover, the modified version significantly decreases the number of excessive computations, accelerating the search of substrings.
Entanglement-Enhanced Physical-Layer Classifier Using Supervised Machine Learning

We introduce physical-layer classifiers enhanced by multipartite entanglement learned through a support-vector machine. The required entangled states are practical and give error probability advantage over classical schemes in presence of loss.
The Trend of lmage Segmentation 3 Research: An Overview Based on Traditional, Supervised and Semi-supervised Methods

Deep convolutional networks are powerful visual models in image segmentation. Traditional methods like Edge Detection and Grab-cut have a large amount of computation and slow processing speed. However, our research into the existing body of literature reveals that supervised learning and semi-supervised learning increasingly become the mainstream algorithms. In order to predict the trend of the image segmentation algorithm, we firstly review the traditional semantic segmentation methods and DeepLab methods. Then summarize the semi-supervised network methods and discuss their advantages and disadvantages. At the end of the paper, we compare DeepLab method with semi-supervised method, and our analysis find that the performance of semi-supervised networks is good, but it still needs some improvement when dealing with a large amount of datasets. Thus, we highlight the value of semi-supervised networks by combining with DeepLab for higher prediction accuracy. It is also a novel direction for image segmentation algorithms.
Ecological parks educational environment usage at Biology lessons in schools (on the example of N.A. Avrorin Polar-Alpine Botanical Garden-Institute)

The paper is devoted to the search of alternative ways to update school Biology curriculum. Creation of educational environment at the premises of ecological parks is considered to be an innovative and promising prospective approach that meets contemporary demands and follows central developmental trends of society. Based on the results of practicing Biology teachers poll, we give an expediency analysis of engaging ecological parks educational opportunities for school lessons. According to available data as well as based on the structure and the content of biological education prescribed for secondary school, the authors program Kola Land nature has been developed. The content of our program is an addition to the General program line Biology for 6-9 classes created by V.V. Pasetchnik, it matches the themes of a school Biology course: Biology as a science of vital nature, Cellular structure of organisms, Plant kingdom, Diversity of fauna, Ecosystems. Effectiveness evaluation of the authors educational program Kola Land nature was realized in the ecological park of N.A. Avrorin Polar Alpine Botanical Garden Institute. The obtained data confirm that the program usage as an addition to the basic school Biology course improves the level of its mastering by students.
Cognitive Man-Machine Communications in the Training of Specialists for the Digital Economy

Specialists in the digital economy must constantly improve their skills, for which it is advisable to use computer-based training. The purpose of the article is to consider the problems of personalized education in the training of specialists for the digital economy and methods of providing cognitive man-machine communications. Research methods include analyzing the competencies of a digital economy specialist and experimentally confirmed methods for designing cognitive man-machine communications. Results. The need to ensure the formation of the cognitive structures of the student under the leadership of man-machine dialogue is associated with a large flow of information coming from the Internet and the complexity for the student to independently form own scientific knowledge and skills required for the digital economy. Formation of scientific, integral knowledge simultaneously with the solution of creative problems, work on group projects provides an opportunity to get a specialist who is ready to work productively in the digital economy. The dialogue forms the cognitive structures of the student and allow the student to solve correctly the problems of professional activity. Conclusion. The use of the competence approach and the principles of cognitive man-machine communications allows us to create a high-quality educational product for the training of specialists in the digital economy.
Algorithm for Evaluating the Promotions Effectiveness Based on Time Series Analysis

The article considers the problem of evaluating the effect of promotions held in a company. This problem remains relevant in our time, as evidenced by various studies on this topic. The authors of the article providesan algorithm for evaluating the promotions effectiveness based on the sales analysis of time series. In this case, promotions are considered as interventions that cause deviations from the main trend of the time series, which can be included in the model as a separate fictitious regressor. The paper also considers mathematical models for representing the interventionand the main characteristics of interventions. To evaluate the systematic component of the time series, it is proposed to use the Prophet forecasting package, since it has a number of advantages that allow one to more accurately isolate the effect of the ongoing campaign. The evaluation algorithm was tested on historical data for a specific product of the product line of stores at gas stations. The results of evaluating the effectiveness of the evaluated product.
Target Detection Algorithm of Optimized Convolutional Neural Network under Computer Vision

The objectives are to recognize and detect image information such as face contours, to promote the development of convolutional neural network (CNN) algorithms in the field of detection technology, and to expand the application of neural network technology in computer vision, thereby achieving the effective fusion of deep learning and computer vision technology. Based on the problem of low accuracy and meticulousness in traditional contour detection, first, by improving the traditional cross-entropy cost function and Dice coefficient, an adaptive fusion cost function is established. An improved deep reinforcement face contour detection algorithm with mixed training of multi-scale model and adaptive fusion function IDRC-FA-MS is proposed. Second, the AlexNet network is modified by fine-tuning the classifier, and the fully connected layer of CNN is changed to a full convolution layer. A multi-scale face detection algorithm is proposed to analyze the performance indicators related to face detection. Finally, an end-to-end multi-scale face detection algorithm DL-CNN is proposed by connecting the convolution kernel and the convolution layer generated by the feature map to extract the overall features including the detailed features. The results show that after training in the BSDS500 dataset, comparing the IDRC-FA-MS face contour detection algorithm with other algorithms, it is found that the algorithm shows the optimal performance and the highest accuracy reaches 0.99. The average accuracy of the multi-scale face detection algorithm training based on network fine-tuning is 43.4%, the corresponding area of the receiver operating characteristic (ROC) curve is 0.92, and the classification accuracy rate is 99.11%. Also, the detection time is short, and the performance is significantly better than the situation when there is no scale change. Compared with other algorithms, the average accuracy of DL-DNN face detection algorithm is the highest. The corresponding value of continuous score is 0.72 and the corresponding value of discrete score is 0.95. The performance is better than most face detection algorithms.
Privacy concerns over mobile tracing app

Legal bodies have sounded alarm bells over the federal government's plan to release a mobile-tracing app that would help track the contacts and movement of people infected with COVID-19.
Conceptual Models for Counter-Terrorism and Intelligence Knowledge Bases

Building database and knowledge bases in support of Counter-Terrorism and Intelligence analysis presents some complex challenges. We overview some past efforts and conclude that typical conceptual models fall short of what is needed; that is, the types of data that can be represented in the knowledge base may not be all that is needed for the analysis. A proposal for an extended conceptual model is introduced, and its value for Counter-Terrorism efforts is discussed.
Cyber Safety Awareness Framework for South African Schools

Almost every facet of a cyber user’s personal and business life is impacted by Information Communication Technologies (ICT) within cyberspace. In the twenty-first century, cyber users have to be ICT literate to be able to keep themselves safe from cybercrime and cyber risks. It is important to start from an early age to educate cyber users about cyber safety in order to ensure that they are cyber safety aware. It is of the utmost importance that South Africa, as a developing country, should start creating and implementing cyber safety awareness policies, procedures, measures and initiatives for the education of all cyber users within its borders. The aim of this research is to explore and identify relevant cyber safety literacy (knowledge), skills and components needed from school management, and then to develop a theoretical framework to support educators and school management to enhance cyber safety awareness in a school environment.
Pedagogical Aspects of Formation of Cognitive Interest in Students as a Technology of Interactive Learning in Higher Educational Institutions

The article reviews the method of activating cognitive activity in students. The characteristic is given to methods of stimulating interest in learning. Examples of using methods to stimulate interest in learning are given. The pedagogical significance of interactive teaching methods is justified, the need for continuous improvement of the system and practice of education is due to social changes in society. The issues of improving the quality of training and the level of upbringing of the students personality were considered and remain priorities in the modern methodology of teaching technology.Keywords: Interactive Learning Methods, Interest Stimulation Method, medical, Technology, Discipline, Pedagogy, Method.
An Integral Field Unit Based on Parabolic Mirror

Recently many modern instruments and systems have been developed to study the Sun. For that, spectral instruments with high spectral resolution are most often used. It is relevant to achieve high spatial resolution along with spectral one for many scientific tasks. In practice, the achievement of both high spectral and spatial resolution can be done by the use of integral field spectroscopy. Current paper is devoted to searching for a system solution for an integral field unit (IFU), which will be implemented to the optical system of solar telescopecoronagraph. The diameter of the main mirror is D = 3 m. Telescope’s working spectral range is ∆λ = 390 − 1600 nm. The integral field unit is based on reflective elements. It divides the input field of a rectangular shape with a size of 0.7500 ×1200(0.145 mm×2.327 mm) into 8 parts with a size of 0.09400×9600(0.018 mm×18.617 mm) each. The possibility of creating an IFU optical system using a parabolic mirror for all (eight) channels is shown. The quality of the optical system was evaluated, as well as the effect of vignetting on the slicing mirrors.
Development Of Mobile Phone Box Detection Modul Using Convolutional Neural Network for Identification Of Stock Smartphone

Determining the percentage of product distribution of a company compared to the products of its competitors is an important thing to know by the company. Through this research, the company can conduct market research on the products it sells. With the development of the existing artificial intelligence system, it is possible to determine the distribution of these products can using Deep Learning. On this occasion will be tried using Convolutional Neural Network-based Object Detection to detect cell phone boxes that scattered on a cellphone counter. Through the dropping of the mobile phone, the box will also know the number of each object or cell phone box that is detected, both from its products and competitors' products, so that the company's product distribution compared with other competitors. The model architecture used in this study is EfficientDet with EffciientNet-B0 Extractor, EffciientNet-B1, EffciientNet-B2, and EffciientNet-B3 features. The evaluation results are EfficientDet-D0 with mAP of 66%, EfficientDetD1 with mAP of 72.5%, EffcientDet-D2 with mAP of 73.8%, EffcientDet-D3 with mAP of 72.7%. Judging from the results of the evaluation. The model and architecture chosen for use in this system are EffcientDet-D0, because the accuracy provided is not too far from other architectures, besides EfficientDet-D0 also has the fastest inference time compared to the others, i.e., 7 ms on GPU RTX 2080 Ti, suitable for use in production with many users.
A Vision-Based Pathfinder using MOEA with Low-Cost Gene Encoding for Navigating a Mobile Robot

The pathfinding is generally regarded as a multi-objective optimization problem that simultaneously optimizes the shortest path and the least collision-free distance to obstacles. This work develops an MOEA-based algorithm of pathfinding to navigate a mobile robot to avoid the obstacles in a given finite environment. A grid-based method is also introduced for mapping of the environment to efficiently encode the robot's position to be chromosomes. The hardware architecture is built by a mobile robot and its working environment, as well as the Aruco system and a computer running the algorithms of pathfinding and image processing. Both simulations and experimental results are presented to verify the feasibility of the proposed method. In applications, this work can be employed in a commercial ball collecting or object-carrying robot.
PRISME: An interaction model linking domain activities and mixed and tangible interactors in virtual environments

The PRISME model introduced in this article is part of ongoing research On VR and AR for ergonomics and the design of industrial operator platforms. In this context, PRISME is an innovative solution by proving an automatic link (removing the need for domain-specific adaptations) between operator tasks and interactions with their platform. This research will be presented in two stages: a generic topology of interactors in Mixed and Tangible reality followed by an interaction model based on MASCARET's activity description meta-model syntax. Finally, an aeronautical use case will validate the model by simulating the standard operations performed by an airplane controller.
A comparison of Vietnamese Statistical Parametric Speech Synthesis Systems

In recent years, statistical parametric speech synthesis (SPSS) systems have been widely utilized in many interactive speech-based systems (e.g. Amazon’s Alexa, Bose’s headphones). To select a suitable SPSS system, both speech quality and performance efficiency (e.g. decoding time) must be taken into account. In the paper, we compared four popular Vietnamese SPSS techniques using: 1) hidden Markov models (HMM), 2) deep neural networks (DNN), 3) generative adversarial networks (GAN), and 4) end-to-end (E2E) architectures, which consists of Tacontron 2 and WaveGlow vocoder in terms of speech quality and performance efficiency. We showed that the E2E systems accomplished the best quality, but required the power of GPU to achieve real-time performance. We also showed that the HMM- based system had inferior speech quality, but it was the most efficient system. Surprisingly, the E2E systems were more efficient than the DNN and GAN in inference on GPU. Surprisingly, the GAN-based system did not outperform the DNN in term of quality.
Gaussian Processes Regression with Joint Learning of Precision Matrix

In the traditional Gaussian process regression (GPR), covariance matrix is modeled by a kernel function, which is dominated by a set of hyper-parameters. However, the estimation of such hyper-parameters are generally a highly nonconvex optimization problem, which imposes computational difficulties and undermines the practical performance. To improve the prediction accuracy, we propose in this paper a novel GPR algorithm that introduces the estimate of precision matrix of target values. Covariance and precision matrices are coupled by a regularized approximation error term. In practice, the precision matrix and hyper-parameters are trained by the alternating optimization. Experimental results demonstrate that the performance of the joint-learning formulation is superior to traditional GPR.
Sports Big Data Simulation and Analysis: from CUDA to the AI Processors

Sports big data simulation and the further analysis ranging from CUDA to the AI processors has been studied in this paper. In the use of CUDA, the data structure and memory access have a great impact on the performance of the GPU, and sometimes they are decisive factors. For these two aspects, the research can be carried out from a single level or multiple levels of the multiple levels such as operating system level, memory controller, and also DRAM. The SoC chip security architecture research platform uses a combination of high-speed AHB bus and low-speed APB bus, and selects the more common peripheral devices to simulate modern general-purpose SoC chips. The proposed model is simulated through the data sets. The experiment compared witht the latest methods have proven the effectiveness.
Multilabel Classification of Nursing Activities in a Realistic Scenario

Nursing activities are complex in nature: in the real scenario, when nurses perform caregiving activities, they can perform concurrent activities or multiple activities at a time. With traditional supervised learning, it is very difficult to classify them, especially when there is no proper information on sequence and duration of activities performed. In this paper, we used a new nursing dataset collected in a complete natural setting. As our observer could not enter the room of patient to record the duration of each activity, we utilized multilabel classification in this scenario. We considered our work both as multiclass classification and multilabel classification. We applied (1) Random Forest for multiclass classification, (2) Binary Relevance, and (3) Random Forest SRC (Survival, Regression, and Classification) for multilabel classification. By evaluating the result, we found out that Random Forest SRC achieved the highest performance that is around 86% precision, 62% recall, and 63% F1 score, whereas Random Forest achieved around 10% precision, 11% recall, and 10% F1 score and Binary Relevance achieved around 28% precision, 13% recall, and 17% F1 score. We also explained why multilabel classification is necessary to recognize activities in real scenario problems.
Latent Walking Techniques for Conditioning GAN-Generated Music

Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. As computer-generated music improves in quality, it has potential to revolutionize the multi-billion dollar music industry by providing additional tools to musicians as well as creating new music for consumers. Beyond simply generating music lies the challenge of controlling or conditioning that generation. Conditional generation can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques for conditional image generation, but its effectiveness on music-domain generation is largely unexplored, especially for generative adversarial networks (GANs). In this paper, latent walking is implemented with the MuseGAN generator to successfully control two semantic values: note count and polyphonicity (when more than one note is played at a time). This shows that latent walking is a viable technique for GANs in the music domain and can be used to improve the quality, among other features, of the generated music.
Cascade single stage Detector using full convolution network

Abstract For object detection, the two-stage approach always achieves better performance than one-stage. But one-stage is more effective. The main shortcomings of one-stage approach are: class imbalance problem during the training phase and sometimes the correct object score is lower. To address those problems, we propose a novel single-shot detector, called Cascade detection. It is composed of several stages, and each stage is composed of several detection layers. The main point of our method is: we design an anchors to anchors module (A2AM), which gives one-stage approach the ability of training stage by stage. It can not only address the class imbalance problem in the training phase, but also increase the object scores and the Iou between prediction and ground truth in the inference phase. The multitask loss function enables us to train the whole network in an end-to-end way. Experiments on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO prove that our method achieves state-of-art performance detection accuracy and computation efficiency.
Graph Grouping Loss for Metric Learning of Face Image Representations

This paper proposes Graph Grouping (GG) loss for metric learning and its application to face verification. GG loss predisposes image embeddings of the same identity to be close to each other, and those of different identities to be far from each other by constructing and optimizing graphs representing the relation between images. Further, to reduce the computational cost, we propose an efficient way to compute GG loss for cases where embeddings are L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> normalized. In experiments, we demonstrate the effectiveness of the proposed method for face verification on the VoxCeleb dataset. The results show that the proposed GG loss outperforms conventional losses for metric learning.