Reviewerly/exHarmony huggingface dataset:
The socio-cultural competence development of future primary school teachers in the process of studying language cycle disciplines

Nakonechna Larysa.The socio-cultural competence development of future primary school teachers in the process of studying language cycle disciplines.This article is devoted to an extremely important problem of educational activity in modern pedagogical higher educational institutions the socio-cultural competence development in the process of studying the disciplines of the language cycle in students studying the specialty «Primary Education».The relevance of the topic is due to several factors.Since the time of Russia's open aggression against Ukraine, the issue of national selfidentification, national values, civic position, and patriotism has become particularly important.In this regard, the teachers of pedagogical high schools were faced with the need to find more effective ways for forming the socio-cultural competence of students as citizens and future teachers.The upbringing of a nationally conscious and at the same time socio-culturally competent person is entrusted primarily to the teacher, educator, and mentor.In this regard, the role of the teacher in the process of educating the younger generation and, accordingly, the preparation of future teachers in the context of professional activities.The author focused on the review of the concept of «sociocultural competence» in the context of preparing future teachers for professional activities, as well as outlining the tasks and ways of forming sociocultural competence on the base of national ideas for future teachers of primary school during their studying the linguistics disciplines.The author is convinced that the formation of socio-cultural competence of future primary school teachers contributes not only to the study of language but also to acquaintance with the history of language, monuments of written culture along with the study of history, culture of the native country.Language as a socio-cultural phenomenon is not only a means of creating a linguistic picture of the world but also an extremely important marker of national identification, a factor in the formation of a nationally conscious personality.
The Need for Internet of Things (IoT) Forensics

With the advent of Internet of Things (IoT), the devices are becoming more and more smarter. The innovations in IoT technologies have tremendously increased the communication over wireless channels. The wireless channels are proven to be more insecure and hence become prone to many attacks. Limited processing power and storage restrict the capabilities of security measures to be installed in the IoT devices. The chapter starts with little introduction to digital forensics and as IoT forensics as its extension. Key features of IoT forensics are explained in nutshell. Major issues which may arise due to limited IoT capabilities are described. Various challenges and opportunities which an IoT devices have created are examined and analyzed. The lifecycle of the evidence collected during the process is then discussed along with its technical and legal complexities. An example of IoT forensics has been provided with the help of case study. The chapter also sheds some light on a few investigations frameworks followed by a high-level comparison. Finally, the chapter concludes by giving few ideas for further research and investigations.
Depth maps correction based on neighboring frames

Monocular depth estimation allows to extract the depth map from a single image. State-of-the-art algorithm MiDaS allows to extract the depth map from a single image, however its results are not accurate for video sequences. We propose a new monocular depth estimation method based on MiDaS. Quality improvement is obtained by utilizing information not only from the current video frame but also from neighboring frames. Alternative ways to extract information from neighboring frames are presented and their quality compared. Presented method works in real-time and produces more accurate predictions than alternative methods in the majority of cases.
Exploring the Spatio‐Temporal Aware Graph for video captioning

Describing video content automatically in natural language sentences is a long-standing challenge in computer vision. Although existing methods that capture relational information among objects have made significant strides in the past years, the detailed geometrical and temporal information of objects remains to be further explored. To address this problem, a novel Spatio-Temporal Aware Graph is proposed to capture more elaborate visual representations, which are able to exploit the detailed spatio-temporal clues of the extracted object features. By performing graph-structured aggregation, the proposed model is capable of capturing not only the interactions among objects but also the detailed spatio-temporal relations. Meanwhile, a Frame Similarity Graph is constructed on frame features to learn comprehensive representations, which aim to extract the global information that the object feature lacks. Moreover, to capture rich video semantics from different perspectives, multiple video representations, that is appearance and motion information, are utilised to learn discriminative representations. Experiments on the prevalent benchmarks: Microsoft Video Description Corpus and Microsoft Research Video to Text demonstrate that the proposed approach achieves state-of-the-art performance in several widely used evaluation metrics: BLEU-4, METEOR, ROUGE, and CIDEr.
Analysis of Ensemble Classification of Twitter Sentiments Using New Dependency Tree Based Approach

The vast amount of data available on social media and microblogs can be a valuable resource for mining opinions or for analyzing the overall mood of the public. This helps in identifying potential customers, exploring market trends and predicting events. Analyzing twitter data is comparatively difficult due to the large amount of irregularities present in tweets. Many approaches that use sentiment dictionaries and machine learning have been proposed until now. In this paper, we present a new feature that is extracted using dependency parsing and an emotion lexicon. This feature, along with n-grams, syntactic n-grams and lexicon-based features, is used to classify the tweets. We also use custom dictionaries to identify slang words, SMS short forms, emoticons and word contractions. The performance of various classification algorithms and ensemble techniques is compared. Our results show that the new feature along with the ensemble framework improves sentiment classification.
The Read-Aloud Method to Develop Reading Literacy at School’s Educational Park

This study aims to improve students' reading literacy through the Read-Aloud method. Students' reading literacy has decreased due to the implementation of Learning-From-Home activities as an effect of the Covid-19 pandemic. Learning in this study was done outdoor that concerned literacy activities in a School’s Education Park. This research used the Classroom Action Research (CAR). The subjects of this study were 18 graders of elementary school. Data were collected through observation, test questions, and documentation. The research instrument consisted of student observation sheets in reading activities, reading performance test items, and documentation guidelines. The data were analyzed following the descriptive qualitative techniques. The results indicated that the implementation of Read-Aloud activities at the Educational Park could improve students' reading literacy, particularly in the aspects of fluency and comprehension. For the reading fluency, the students were able to read fluently in a clear intonation with correct pronunciation and use punctuation marks appropriately. While for the comprehension, the students showed the ability to find the main idea, answer questions based on the reading, and conclude the reading content. The increase in the students' reading literacy was also supported by the assessment results, which climbed from the 'low' to the 'excellent' category.
A Blind Video Copyright Protection Technique in Maximum and Minimum Energy Frames Based on The Fast Walsh Hadamard Transform (FWHT) and Discrete Wavelet Transform (DWT) and Arnold Map

Video copyright protection is the most generally acknowledged method of preventing data piracy. This paper proposes a blind video copyright protection technique based on the Fast Walsh Hadamard Transform (FWHT), Discrete Wavelet Transform (DWT), and Arnold Map. The proposed method chooses only frames with maximum and minimum energy features to host the watermark. It also exploits the advantages of both the fast Walsh Hadamard transform (FWHT) and discrete wavelet transforms (DWT) for watermark embedding. The Arnold map encrypts watermarks before the embedding process and decrypts watermarks after extraction. The results show that the proposed method can achieve a fast embedding time, good transparency, and robustness against various attacks.
Prediksi Nilai Inflasi Post Covid 19 di Indonesia

The COVID-19 pandemic has positively impacted inflation in Indonesia, where inflation decreased to 1.33 in June 2021. It would be better if Indonesia could maintain this figure, but in July 2021, the inflation rate began to rise again; even in April 2022, the inflation rate reached 3.47. This study will predict the inflation value after the covid-19 pandemic using Autoregressive Moving Average (ARIMA) analysis. Using monthly time-series data, we study inflation in Indonesia during the period 2010 – 2022, then predict the inflation value in the following months using Autoregressive Moving Average (ARIMA) analysis. The data is stationary at the first difference. Several possible ARIMA models analyzed show that the ARIMA (1,1,1) model was chosen as the best model. The results of this study indicate that monthly inflation in Indonesia is likely to continue to rise. The government is expected to make appropriate policies to reduce inflation based on these results.
INSURANCE OPPORTUNITIES AND CHALLENGES IN A CRYPTO CURRENCY WORLD

Purpose: The study analysed the insurance opportunities and challenges in a cryptocurrency world.&#x0D; Materials and Methods: The research methodology used in this study consisted of a systematic literature review of previous studies from different scholars, that have discussed on insurance opportunities and challenges in the cryptocurrency world or research topic related to the study topic. This research method was preferred since it is quite reliable, cost-saving and time-saving as compared to primary research methods. This study relied more on online journals, articles and published work from libraries since they provide easy access to secondary information/data.&#x0D; Results: The results indicated that the reviewed studies had conceptual and contextual gaps. This is because most of them discussed on blockchain technology and smart contracts as the only components in the cryptocurrency market that insurance firms have tapped into. In addition, some of these studies have discussed on the opportunities and challenges in the real estate market and healthcare systems or a specific type of insurance firm rather than the bigger picture of the insurance industry.&#x0D; Unique contribution to the theory, practice and policy: The theories discussed in this study will be beneficial to the researchers and scholars since it gives a better understanding on the application of these theories to insurance and the cryptocurrency world. The researchers and scholars will also find this study relevant considering that studies on insurance opportunities and challenges in a cryptocurrency world are quite scarce. This study will provide a good literature base for their studies. Moreover, the identified research gaps in this study will act as a motivation to the different researchers and scholars to conduct more studies on related topics, thus bridging the research gaps on insurance opportunities and challenges in a cryptocurrency world. The study will provide a better understanding to the management of the insurance companies, on how they can invest on the various opportunities available in the cryptocurrency market. Further, the policymakers in the insurance industry will also benefit from the discussion on challenges that arise from adoption of blockchain technology and smart contract, as they implement various policies that promote growth of the insurance industry.
Transformation relation between coherence and entanglement for two-qubit states

Entanglement and coherence are two important resources in quantum information theory. A question naturally arises: Is there some connection between them? We prove that the entanglement of formation and the first-order coherence of two-qubit states satisfy an inequality relation. Two-qubit pure state reaches the upper bound of this inequality. A large number of randomly generated states are used to intuitively verify the complementarity between the entanglement of formation and the first-order coherence. We give the maximum accessible coherence of two-qubit states. Our research results will provide a reliable theoretical basis for conversion of the two quantum resources.
Simulating global misalignment of a binocular HMD during a flying and target discrimination task

Binocular head-mounted displays (HMD) utilizing augmented reality (AR) strategies can greatly increase the information that reaches the visual system of the user. For example, binocular presentation allows for elements to appear in stereoscopic depth and with a higher perceived resolution and AR can improve the quality of a low visibility scene. But with two independent optical channels, a binocular HMD can easily become misaligned, which can potentially be detrimental to both performance and comfort (Gavrilescu et al., 2019; SPIE DCS). Here, we quantify the effect that global binocular misalignment in an HMD has on both operational and visual performance during a simulated flying task. Using a platform consisting of 3 85-inch displays providing out-the-window imagery and head-tracked AR overlay (e.g., DAS) within the HMD (Posselt et al., 2021; SPIE DCS), subjects were instructed to adhere to flight commands while periodically discriminating the orientation of a target aircraft. In different blocks the two optics of the HMD were either well-aligned, misaligned vertically by 0.67&deg;, or rolled in opposite directions by 4&deg;. In the well-aligned condition, subjects could discriminate the orientation of the target plane on average nearly 1000 ft farther than in either of the misaligned conditions. Curiously, adherence to the flight commands was affected only by the vertical misalignment, which may represent a strategy of selectively ignoring grossly misaligned imagery in one eye. These results obviate the need to quantify and maintain well-aligned visual channels in binocular HMDs that utilize AR imagery.
Learning secure modulation using complex neural networks

Growing interest in utilizing wireless spectrum makes spectrum access congested, competitive, and often contested making wireless signals vulnerable to various attacks. This compels us to design a secure waveform that solves encryption and modulation as a joint problem. We propose a novel end-to-end symmetric key encryption algorithm where the transmitter encodes the confidential data bits using a shared secret key to generate a secure waveform and the legitimate receiver decrypts the waveform to retrieve the transmitted bits. The trusted pairs are trained adversarially to learn secure data communication by introducing an adversarial NN, that helps to separate the mutual information between secret bits and secured waveform. Cooperative learning takes place between the trusted pair to defeat the adversary and learn encryption and modulation jointly. Complex neural networks are used to build encryption/decryption networks to improve the secrecy-reliability trade-off compared to prior works. Extensive simulated data set is used to train the trusted pair to learn secure data transmission. Our results demonstrate that the trusted pair succeeds in achieving secure data transmission over wireless links while the adversary can not decode or recognize the received waveform.
Real Time Face Mask Recognition

Abstract: This project depicts the use of Real Time Face Mask Recognition in the global pandemic. During COVID-19, WHO made wearing face masks a necessity so that people could protect themselves from the deadly virus. Real Time Face Mask Recognition is used to identify if a user is not wearing a face mask or not. Real Time Face Mask Recognition had seen crucial progress in the fields of Image processing and Computer vision, since the rise of the deadly pandemic. Many face mask detection models have come up which were made using various algorithms and techniques. The suggested approach in this paper uses Keras, Deep Learning, TensorFlow and OpenCV to do Real Time Face Mask Detection. This model can be used to take precautions in various public places as it can help in restricting the spread of virus. Face masks have become an essential element of our daily lives, hence it is important to wear one for the safety of ourselves and others. Keywords: Deep Learning, Face Recognition, Neural Networks, Image Processing, Open CV, Keras, Tensor Flow
Intelligent Development of Enterprise Management Innovation Based on Artificial Neural Network

Artificial intelligence technology is gradually entering all aspects of our lives while also promoting the development of various fields. Artificial intelligence can also solve problems that could not be solved by computing before. This paper combines artificial intelligence and network function virtualization, two very advanced and popular technologies in modern society, and uses neural networks to solve and develop the service chain problem in network function virtualization. The simulation shows that all algorithms almost decrease linearly with the change of VNR. This decrease means that there is no noise. The optimal mapping of each VNE problem can be achieved within the scope of the existing solutions, and the heuristic method is to try to find possible mappings. In terms of enterprise management innovation, this article points out the need to strengthen in-depth cooperation and exchanges between universities and enterprises. At the same time, the government, as a third party in cooperation, should play an active leadership role and an intermediary coordination role. In terms of innovation management cooperation, the establishment and improvement of government plans and policy support mechanisms are important external driving factors.
Deep Global-Local Gazing: Including Global Scene Properties in Local Saliency Computation

Visual saliency models imitate the attentive mechanism of the human visual system (HVS) to detect the objects that stand out from their neighbors in the scene. Some biological phenomena in HVS, such as contextual cueing effects, suggest that the contextual information of the whole scene does guide the attentive mechanism. The saliency value of each image patch is influenced by its visual (local) features as well as the contextual information of the whole scene. Modern saliency models are based on deep convolutional neural networks. Because the convolutional operators operate locally and use weight sharing, such networks inherently have difficulty capturing global and location-dependent features. In addition, these models calculate the saliency value pixel-wise using local features. Therefore, it is necessary to provide global features along with local features. In this regard, we propose two approaches for capturing the contextual information from the scene. In our first method, we introduce a shift-variant fully connected component to capture global and location-dependent information. Instead of using the native CNN of our base model, in our second method, we use a VGGNet to capture the global and context information of the scene. To show the effectiveness of our methods, we use them to extend the SAM-ResNet saliency model. To evaluate our proposed approaches, four challenging saliency benchmark datasets were used. The experimental results showed that our methods could outperform the existing state-of-the-art saliency prediction models.
Visual Effect System for Projection Mapping with Japanese Drum Performance

Recently, there have been several collaborations between new projection mapping projects and various performances. To remake it, we collaborated with a traditional Japanese drum performance. Since unity is an essential factor for a drum performance, we developed two systems to increase the sense of unity between a drum performance and visual effects on projection mapping. One of the systems employed an acceleration sensor and classified different drumming patterns using deep learning. The system synchronized each drumming pattern with a corresponding visual effect. The other system was used for floor projection visual effects. In our collaborated performance, we were unable to install any sensors due to the restrictions of the event. Thus, we used an optical see-through device to operate some visual effects interactively. This system enabled an operator to place and relocate each visual effect according to the location of each performer. These two systems will help in improving the sense of unity in a collaborated performance.
Homo Informaticus: Biological and Socioanthropotechnical Evolution

The article is devoted to the philosophical analysis of the problem of the ways of evolution of modern man and the man of the future, who is often called digital, information man or e-homo (electronic man). The purpose of the article is to identify the strengths and weaknesses of the digital person, as well as to understand which directions of his evolution are promising, and which are dangerous and lead to dehumanization. The author’s hypothesis is that, although the main directions of evolution of Homo sapiens in Homo informaticus lie in the socio-anthropotechnical plane, that is, they are carried out using artificial methods, yet the possibilities of biological evolution are not completely exhausted. The methodological basis of the research is a systematic review and analysis of literature in two main areas: biological (evolutionary biology, neurophysiology) and socio-philosophical (literature on the problems of building an information society and human existence in it). Interdisciplinary analysis shows that the brain of the becoming Homo informaticus undergoes significant physiological changes, entailing changes in thinking, memory, human behavior, and, therefore, indirectly – and changes in society and culture. From the “book culture” of perception of a voluminous coherent printed text, we move on to the “clip culture”. Stress has proven evolutionary significance, accordingly, in the impact of information stress, to which the inhabitants of a digital society are very much exposed, one can look for evolutionary potential. This problem is acutely relevant for modern research in the field of philosophy of biology and human ecology, as it opens up new perspectives in understanding the problem of human evolution. Never before in the history of mankind have technologies reached a level that allows them to directly interfere with the foundations of human existence. The result of the research is a philosophically grounded answer to the question in which direction the further evolution of man is possible, which, in turn, will allow us to find new and take a fresh look at the already known philosophical meanings of the concept of “man”.