Reviewerly/exHarmony huggingface dataset:
Sign annotation generation to alphabets via integrating visual data with somatosensory data from flexible strain sensor-based data glove

Sign language forms a significant way of communication for deaf-mute people. There are different types of sign languages of different countries in different regions. The sign language recognition system translates the gestures into the alphabet, which solves a barrier in comprehension of conversation. Visual techniques and somatosensory strain sensors are both useful modalities to detect sign gesture. In order to improve the recognition accuracy and wear resistance, this paper proposes a real-time alphabets conversion system, which combines the visual data with the data glove based on flexible strain sensor and the somatosensory data of the camera, supplemented by deep learning. In this learning structure, convolutional neural network is used to process the visual data, and then the sparse neural network for sensor data fusion and recognition is realized in the feature layer. A module is proposed to train the linking of hand motions primitives to alphabets (A–Z). The proposed framework is tested on a dataset of sign and descriptive alphabets. The proposed system would recognize the 26 letters with a recognition rate of up to 99.50% and a recognition time of less than 1 s, which helps to understand observations of sign as words and texts.
The role of virtual reality in enhancing tourists happiness in the context of special-interest tourism

As the COVID-19 pandemic has restricted the physical movement of individuals, all kinds of special-interest tourism (SIT) activities tourists have enjoyed in the past have stopped. In this situation, virtual reality (VR) tourism is regarded as the only way to cheer people up by providing a substitute for a real field trip. VR tourism is a means to stage constructive authenticity, which does not exist in individuals' daily life, and allows them to experience feelings of immersion, novelty, presence, and nostalgia. Moreover, VR journeys to digitally preserved or restored sites can satisfy the numerous desires of SIT tourists, albeit such visits take place in a virtual environment. Such VR journeys also allow people to think they are contributing to preservation. All of these benefits of VR tourism may increase the positive emotions derived only from tourism and prolong an individual's happiness for a long time during the COVID-19 pandemic.
CURRENT TRENDS OF DIGITAL TRANSFORMATION OF EDUCATION

Digital resources today are reliably included in the practice of managerial and educational activities of the education system at all levels. The digital transformation of education is considered among the strategic directions for the development of the institution of education, which involves the development of priority ways to expand the scale and improve the quality of the use of information technologies in the educational space. Digital service in education is assigned the role of changing the quality of life by opening up new opportunities for the provision of educational services in the context of globalization. This circumstance actualizes the expediency of the research. The problem of the research is to identify the current directions of the digital transformation of education in the context of globalization. The purpose of the study: to identify current areas of digital transformation of education as a means of improving the quality of educational services. The achievement of this goal was facilitated by the use of a set of research methods: analysis of scientific research and information sources on the Internet, generalization, methods of visual presentation of the data obtained. The results and key conclusions: the prerequisites for the digital transformation of education in modern conditions are substantiated; the concept of "digital transformation of education" has been considered; current areas of digital transformation of education and examples of their use in the educational process have been reviewed; attention is focused on the positive effect of the practice of integrating traditional learning with innovative digital solutions; the conclusion has been formulated about the prospects for the active introduction of digital services in education as an integral component of the technological and structural modernization of the digital society.
An efficient and user-friendly software tool for ordered multi-class receiver operating characteristic analysis based on python

Receiver Operating Characteristic (ROC) analysis is a prevalent tool for two-class problems in a wide range of science and engineering areas. The relevant scalar area under the ROC curve (AUC) has been proven to be a robust evaluation index for characterizing the performance of binary statistical models. However, tasks with more than two categories are frequently encountered in practice. As an extension of the ROC curve and AUC in parallel, the hyper ROC surface and the corresponding hyper-volume under the surface (HVUS) have attracted extensive interest. The current mainstream analysis tools of AUC and HVUS are based on the R program language. To provide researchers with an alternative tool for multi-class ROC analysis, in this work, we develop a python-based package and software tool, which allows the users to unbiasedly estimate HVUS, confidently compare HVUSs, and flexibly visualize 2D-ROC curve and 3D-ROC surface, etc.
Experimental demonstration of Gaussian boson sampling with displacement

We inject squeezed vacuum and weak coherent light into a multiport interferometer and measure the output photon statistics. Our work explores the capabilities of a displacement field in Gaussian boson sampling.
A SHA-512 Hardware Implementation Based on Block RAM Storage Structure

The Secure Hash Algorithms (SHAs) are essential building blocks of modern cryptographic systems. The imple-mentation dimensions of secure hash algorithms are explored for different application scenarios. Cloud servers may favor an implementation with considerable throughput, while a compact implementation with acceptable speed and sustainable power is crucial for the Internet of Things (IoT). In this paper, we present an implementation of SHA-512 for FPGA platform based on Block RAM (BRAM) storage structure. Three implementation techniques are proposed to facilitate the usage of BRAMs as replacements for Look-Up Tables (LUTs) and Flip-Flops (FFs) to achieve a balanced FPGA utilization. Compared to other FPGA implementations of SHA-512, our design has one of the smallest slice consumption while maintaining a moderate but sufficient throughput for cryptographic applications like the post-processing of true random number generators (TRNGs).
Arm meets Cloud: A Case Study of MPI Library Performance on AWS Arm-based HPC Cloud with Elastic Fabric Adapter

Recent advances in HPC Cloud field has made multi-core high performance VM services more accessible. Emerging Arm based HPC systems are also receiving more attention. Amazon Web Service recently announced new c6gn instances with Gravition 2 Arm CPU on each node and support of Elastic Fabric Adapter, which make them the leading high performance Arm-based cloud system vendor. In this paper, we characterize the performance and capability of the AWS Arm architecture. We explore the performance optimization of current MPI libraries based on features of Arm-based cloud systems and Scalable Reliable Datagram protocol of Elastic Fabric Adapter and evaluate the impact of our optimization of high-performance MPI libraries. Our study shows that the performance optimization for MPI library on AWS Arm systems significantly improves the performance of MPI communication for both benchmark and application level. We gain up to 86% performance improvement in micro-benchmark level col-lective communication operations and up to 9% improvement in Weather Research and Forecasting application level. This paper provides a comprehensive performance evaluation for several popular MPI libraries on AWS Arm-based Cloud systems with EFA support. HPC application developers and users are able to get insights from our study to achieve better performance of their applications on Arm-based cloud systems with EFA support.
Glottal instants extraction from speech signal using Deep Feature Loss

Electroglottograph (EGG) is a device used to measure the conductance between the vocal folds. The analysis of EGG signal has many applications in the literature such as speech-to-text synthesis, voice disorder analysis, emotion recognition, speaker verification, etc. Therefore, the EGG device is essential to record the vocal folds activity. Alternatively, a new method is proposed in this work to synthesize the EGG waveform from speech signal using a context aggregation convolutional neural network. The synthesis network is trained by accounting the deep feature losses obtained by comparing it with another network called the EGG classification network. The synthesized EGG signal needs to be characterized. During the voiced speech production, the instants at which the vocal folds attain complete closure are called glottal closure instants (GCIs). Likewise, the opening instants are called glottal opening instants (GOIs). Such instants are reliably measured using the EGG signal. The performance of the proposed method is compared with other state-of-the-art techniques. The CMU-Arctic database has a parallel corpus of speech and EGG signal recorded simultaneously. This database is used for training the synthesis network and for comparison purposes. It is found that the performance of extracting glottal instants from synthesized EGG signals is comparable to other methods.
Image Segmentation Technology Based on Attention Mechanism and ENet

With the development of today's society, medical technology is becoming more and more important in people's daily diagnosis and treatment and the number of computed tomography (CT) images and MRI images is also increasing. It is difficult to meet today's needs for segmentation and recognition of medical images by manpower alone. Therefore, the use of computer technology for automatic segmentation has received extensive attention from researchers. We design a tooth CT image segmentation method combining attention mechanism and ENet. First, dilated convolution is used with the spatial information path, with a small downsampling factor to preserve the resolution of the image. Second, an attention mechanism is added to the segmentation network based on CT image features to improve the accuracy of segmentation. Then, the designed feature fusion module obtains the segmentation result of the tooth CT image. It was verified on tooth CT image dataset published by West China Hospital, and the average intersection ratio and accuracy were used as the metric. The results show that, on the dataset of West China Hospital, Mean Intersection over Union (MIOU) and accuracy are 83.47% and 95.28%, respectively, which are 3.3% and 8.09% higher than the traditional model. Compared with the multiple watershed algorithm, the Chan-Vese segmentation algorithm, and the graph cut segmentation algorithm, our algorithm increases the calculation time by 56.52%, 91.52%, and 62.96%, respectively. It can be seen that our algorithm has obvious advantages in MIOU, accuracy, and calculation time.
Introduction to Educational Media

The global COVID-19 pandemic revealed obstacles to conducting consistent online education. Even though we live in the digital age, distance learning revealed inequality and exposed the fact that children with no appropriate resources and study environments could show lower efficiency. Although school children are accustomed to using apps, the pandemic led to a revival of television education media. In many countries, governments intervened and put emphasis on television media by creating partnerships with public broadcasters. Specific programs were created to follow the curriculum for each grade and to make it not only instructive but also appealing. European Broadcasting Union report on education during the COVID-19 pandemic presents various European public service media’s good practices on the educational programs produced by teams of engaging teachers as well as artists and actors presenting the topics included in the curriculum and also developing creativity through assignments. The content was available also on online platforms, which show that only by combining online and traditional media, society can respond to the challenges of sociodemographic and economic differences.
Traditional intellectual properties and Yoruba (Ifa) philosophy in South‐West Nigeria: Intellectual property versus traditional protection approach

Abstract The claim that Ifa is not merely a form of worship has been made by many scholars and remains a controversy. According to them, it is the core of Yoruba philosophy and scientific as well. Nevertheless, there exists art and science of an intellectual orientation regarding the development of Yoruba people. There are artworks, sculptures, incantations (poetry), and performances in Ifa. Divination is based on the binary system linked to science, as well as the process of traditional healing. Indeed, where you have art and science, intellectual property rights (IPRs) are in pursuit. In this case, copyright, patents, and trade secrets. The aim of this article is to examine IPRs in the spiritual world of Ifa and Yoruba philosophy; proposing that spiritual intellectual properties are for an entirely different world with a protection mechanism outside the modern approach of protection, while other forms of traditional intellectual properties outside the realm of the spiritual world requires modern intellectual property protection approach, subsequent to the understanding of the creator or inventor.
Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018

Background: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are located in or around the Amazon rainforest. Despite declining cases and deaths, malaria remains a major public health issue in Brazil. Accurate spatiotemporal prediction of malaria propagation may enable improved resource allocation to support efforts to eradicate the disease. Methods: In response to calls for novel research on malaria elimination strategies that suit local conditions, in this study, we propose machine learning (ML) and deep learning (DL) models to predict the probability of malaria cases in the state of Amazonas. Using a dataset of approximately 6 million records (January 2003 to December 2018), we applied k-means clustering to group cities based on their similarity of malaria incidence. We evaluated random forest, long-short term memory (LSTM) and dated recurrent unit (GRU) models and compared their performance. Results: The LSTM architecture achieved better performance in clusters with less variability in the number of cases, whereas the GRU presents better results in clusters with high variability. Although Diebold-Mariano testing suggested that both the LSTM and GRU performed comparably, GRU can be trained significantly faster, which could prove advantageous in practice. Conclusions: All models showed satisfactory accuracy and strong performance in predicting new cases of malaria, and each could serve as a supplemental tool to support regional policies and strategies.
Study on relationship between composition and prediction of photo aesthetics using CNN

The purpose of image aesthetics assessment is to automatically predict the perceived quality of an image. Convolutional neural network (CNN) based on deep learning has been used for aesthetics assessment and has displayed potential results. Our final objective is to identify features that contribute to the estimation of aesthetic quality by using explainable AI. In this study, we focused on composition as the first step. By applying clustering to the attention maps obtained by CNN and Grad-CAM++, it was experimentally verified whether the CNN model considers composition for aesthetics assessment. In addition, we verified whether the aesthetic quality features that humans pay attention to differ according to photographic categories such as landscape or portrait.
Large scale programmable photonic circuits using silicon photonic MEMS

We demonstrate low-power and non-volatile MEMS actuators on an industrially established silicon photonics platform. The compact electrostatically actuated phase shifters and tunable couplers enable large-scale programmable photonic integrated circuits.
Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

To solve the abovementioned problem, we propose a collaborative filtering recommendation algorithm that incorporates singular value decomposition (SVD) and Chebyshev truncation in spectral domain convolution. Firstly, the SVD algorithm is used to optimize the adjacency matrix, mine the potential association information between users and items, and expand the user-item adjacency matrix. Finally, based on the MovieLens-1M public dataset, the proposed algorithm (CBSVD-SCF) is compared with other commonly used algorithms. The results show that the article optimizes the recommendation effect of the algorithm based on the traditional collaborative filtering algorithm by combining the temporal order and sequence of user interaction information, as well as the popularity of items and the activity of users; the experimental results on MovieLens show that the optimized collaborative filtering recommendation algorithm can effectively improve the recommendation effect.
Turning the Page

The term metaverse signifies various things to various people. While it broadly refers to a virtual shared space at the confluence of Web 3.0, augmented reality, virtual reality, and enabling technologies such as blockchain, there is a fair bit of divergence when it comes to the implications of metaverse on important aspects of day-to-day life, be it finance, entertainment, or education. Preteens today may find it perfectly natural to spend large chunks of their playtime inside their co-created virtual ecosystems within Roblox or Minecraft, hanging out with their friends and showing off their creative sides, even accumulating virtual currencies. This behaviour, when internalised by larger groups, can potentially pave the way for other physical world interactions to be moved to the virtual arena, notably those related with education. With the elements of on-demand availability, interactive feedback, and customised delivery mechanisms, the metaverse can emerge as the go-to destination for many learners and educators alike.
Health Care Intervention Measures for the Elderly under the Smart Sports Rehabilitation Mode under the Background of Big Data

With the aging of the population becoming more and more serious, the health care intervention mechanism for the elderly has also attracted the attention of many scholars. With the rapid development of social economy, intelligent medical treatment, intelligent sports rehabilitation, and other security means have become the main ways to help the elderly improve their health. This paper studies the health assessment and health care intervention measures of the elderly based on the intelligent sports rehabilitation model under the background of big data. First of all, from the security problems faced by the elderly medical care, this paper analyzes the impact of intelligent sports rehabilitation on the elderly medical care. Adopt big data analysis and data mining technology to calculate and evaluate the health index of the elderly and establish a multidimensional machine evaluation system to provide data sources for the intelligent sports rehabilitation model. Secondly, combine the Internet of Things with intelligent sports medicine to build a rehabilitation assistant robot model. Finally, from the perspective of physical function, this paper analyzes the degree of health medical intervention of the intelligent sports model on the elderly and puts forward targeted optimization measures. Research shows that in the intelligent sports rehabilitation model, the health index of the elderly is closely related to sports activities. The sports rehabilitation auxiliary machine constructed with big data technology can effectively improve the medical intervention ability and has obvious effect in practical application.
Moroccan's Arabic Speech Training And Deploying Machine Learning Models with Teachable Machine

Speech recognition is the field of research concerning the ability of machines to accept human speech as input and interpret it with the highest probability. There are several techniques for implementing speech recognition models. One of the emerging techniques is to use neural networks with Deep-Learning for speech recognition. Arabic dialect is one of the most widely spoken and least emphasised languages in terms of speech recognition. There has been relatively little research on speech recognition of Arabic dialect compared to other languages. Dialect Arabic has several regional forms and is used for everyday spoken communication in non-formal contexts. With the advent of social media networks, dialectal Arabic is also written. In Morocco, we use Darija dialect as a language of communication. This form of language results in lexical forms of morphological and grammatical differences which results in great difficulty in developing speech recognition. We develop here a deep-learning model based on TensorFlow to recognise this language. This model has been successfully tested in a car driving simulator (Robot Operating System, ROS) with the robot HUSKY with success rates up to 90%[1].
Optimization of English Grammar Expression Based on the Discrete Dynamic Analysis Algorithm

The dynamic changes of grammatical functions in English teaching in different language environments are different. Based on this background, this paper studies the discrete dynamic modeling technology in the big data complex system. By analyzing the current situation of the English language, this paper studies the dynamic path of the development of English functional grammar. Different from traditional modeling algorithms, dynamic modeling of complex systems can accurately process the relevant data provided by big data. This paper discusses the influence of functional grammar on English listening teaching through dynamic modeling and predictive analysis. This study reduces the error rate of the English prediction model and determines that the change of English achievement is closely related to functional grammar. The results show that the dynamic modeling of complex systems can promote the rapid development of functional grammar in English teaching and provide an effective basis for grammar research. At the same time, the dynamic prediction model based on complex system modeling can accurately predict the actual effect of English grammatical functions on improving English proficiency.