Reviewerly/exHarmony huggingface dataset:
Comparative analysis of application of the teacher professional standard in countries with high indicators of education quality

This article presents the results of a study of teachers&amp;apos; professional standards in countries that consistently perform well according to PISA results, including China (Shanghai), Singapore, Canada (Ontario), Finland, England, and New Zealand. In order to find an answer to the question of what makes professional standards a development instrument of teachers&amp;apos; professional skills, practices of design, implementation, and development of teachers&amp;apos; professional standards were studied. The study included an analysis, comparison, and identification of common and unique features of professional standards for teachers in the leading countries, as well as methodological materials, recommendations, and documents publicly available on the websites of ministries (departments) of education and higher education institutions of the countries studied. Analytical reports, reports, newsletters and methodological materials published by international organizations on professional standards in education and professional development of teachers - World Bank, Organization for Economic Cooperation and Development, National Center for Education and Economics, McKinsey &amp;amp; Company, SEAMEO INNOTECH, Research Institute of International and Comparative Education, Economic Forum &amp;quot;Asia-Pacific Economic Cooperation&amp;quot; were reviewed to study the experience of applying professional standards. The study identified characteristic approaches to the development of professional standards, which allow its application as a mechanism for solving the problems of educational policy of the country (territory); increasing the teacher’s status of and the prestige of the profession; teacher’s career and professional growth; formation of a system for evaluating the teacher’s professional development; formation of ethical standards of the profession. The results and conclusions of the study can be the basis for the development of professional standards in education and modeling the system of teacher growth.
CRNet: Context feature and refined network for multi-person pose estimation

Abstract Multi-person pose estimation is a challenging problem. Bottom-up methods have been greatly studied because the prediction speed of top-down methods is related to the number of people in the input image, making these methods difficult to apply in real-time environments. To solve the problems of scale sensitivity and quantization error in bottom-up methods, it is necessary to have a model that can predict multi-scale keypoints and refine quantization error. To achieve this, we propose context feature and refined network for multi-person pose estimation (CRNet), which can effectively solve the problems of scale sensitivity and quantization error in bottom-up methods. We use a multi-scale feature pyramid and context feature to achieve scale invariance of the network. We extract global and local features and then fuse them by attentional feature fusion (AFF) to obtain context feature that adapt to multi-scale keypoints. In addition, we propose an efficient refined network to solve the problem of quantization error and use multi-resolution supervised learning to further improve the prediction accuracy of CRNet. Comprehensive experiments are conducted on two benchmarks: COCO and MPII datasets. The average precision of CRNet reached 72.1 and 80.2%, respectively, surpassing most state-of-the-art methods.
Hindi/Bengali Sentiment Analysis using Transfer Learning and Joint Dual Input Learning with Self Attention

Sentiment Analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect and emotion-based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data. We start by training Word2Vec word embeddings for Hindi HASOC dataset and Bengali hate speech [24] and then train LSTM and subsequently, employ parameter sharing based transfer learning to Bengali sentiment classifiers by reusing and fine-tuning the trained weights of Hindi classifiers with both classifiers being used as the baseline in our study. Finally, we use BiLSTM with self-attention in a joint dual input learning setting where we train a single neural network on the Hindi and Bengali datasets simultaneously using their respective embeddings.
Human-Computer Interaction Research and Development

This chapter explores new research and developments in the area of human-computer interaction. The fundamental goal of HCI research is to create approaches that will improve people’s ability to communicate with machines and make them better and more natural. The use of gadgets for HCI, such as keyboards and mice, reduces the interpretability and naturalness of the interface by creating a significant barrier between the user and the computer. With the rise of pervasive computing, user-computer interaction is no longer restricted to keyboard and mouse interactions. The straight use of hands as input, instead of typical message interfacing through elements user interfaces, is an appealing way to offer more natural human-computer interactions.
Achieving Efficient Data Deduplication and Key Aggregation Encryption System in Cloud

Secure data deduction has been extensively developed in cloud computing in order to minimise the storage capacity and connectivity overheads as it can avoid redundancies over encrypted data. For multi-domain architecture, data deduplication scheme comprises intra-duplication and inter-duplication, which can as far as possible shield the information about message equivalent status from being disclosed. In cloud computing, the greatest problem is the protection of privacy. This paper proposes an easy but efficient way to enforce private data safety that is housed in a shared cloud. An ecosystem is called where our data remains stable, even though the cloud storage provider is unreliable or corrupted. The database owner encrypts the private data files by means of a hidden key, password identifier and hash feature. If a user demands file access, the owner sets up a key with the user and generates a new key to the user. The use of the mutually defined secret key to decrypt the encrypted file can then be used by the consumer. We therefore propose a computer-simple framework, that offers a safe mechanism for private data sharing even through an unaccountable provider of cloud services. It decreases the time and computing cost of searching duplicates.
Problems and Suggestions on Graduation Design of Higher Vocational Colleges

Higher vocational colleges are an important base for cultivating technical applied talents.With the rapid growth of society's demand for technical application talents, in recent years, higher vocational colleges are also expanding their enrollment year by year.Enrollment expansion will bring about problems such as the quantity of teachers to keep up, thus affecting the quality of student training.Graduation design is the key manifestation of the quality of higher vocational education.Combined with teaching practice and relevant literature research results, this paper deeply analyzes the influence of teacher resources, internship and employment arrangements and plans, graduation design topic selection, graduation design process management, and graduation design evaluation system on the quality of graduation design.Accordingly, four suggestions are put forward to improve the quality of graduation design.It is hoped that these suggestions can help improve the quality of graduation design and vocational education.
Challenging Children Handwriting Recognition Study Exploiting Synthetic, Mixed and Real Data

In this paper, we investigate the behavior of a MDLSTM-RNN architecture to recognize challenging children handwriting in French language. The system is trained across compositions of synthetic adult handwriting and small collections of real children dictations gathered from first classes elementary school. The paper presents the results of investigations concerning handwriting recognition in a context of weak annotated dataset and synthetic images generation for data augmentation. Considering very poor databases of children handwriting, we propose series of experiments to show how the model can cope with small quantity of data. In the paper, we show that assuring a controlled variability of words of varying lengths composed by different instances of degraded or poorly-shaped characters, allows a better generalization of the Handwriting Text Recognition (HTR). We also investigate different choices and splitting propositions to compose both training and validation sets, with respect to children styles distributions. The paper presents conclusions of best suited strategies improving HTR accuracies. Compared to performances to train children real data only, the paper illustrates the impact of transfer learning from adults handwriting (from IAM dataset) and the impact of GAN handwriting styles augmentations to improve children handwriting recognition. We show in the paper also that data augmentation through scaling, rotation or even repeating same instances of words allows to enhance performances reaching sometimes human level.
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

Complex word identification (CWI) is a cornerstone process towards proper text simplification.CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages.As such, it becomes increasingly more difficult to develop a robust model that generalizes across a wide array of input examples.In this paper, we propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations.This technique addresses the problem of working with multiple domains, inasmuch as it creates a way of smoothing the differences between the explored datasets.Moreover, we also propose a similar auxiliary task, namely text simplification, that can be used to complement lexical complexity prediction.Our model obtains a boost of up to 2.42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset.At the same time, we obtain an increase of 3% in Pearson scores, while considering a cross-lingual setup relying on the Complex Word Identification 2018 dataset.In addition, our model yields state-ofthe-art results in terms of Mean Absolute Error.
Optimal Tightness for Chain-Based Unique Signatures

Unique signatures are digital signatures with exactly one unique and valid signature for each message. The security reduction for most unique signatures has a natural reduction loss (in the existentially unforgeable against chosen-message attacks, namely EUF-CMA, security model under a non-interactive hardness assumption). In Crypto 2017, Guo et al. proposed a particular chain-based unique signature scheme where each unique signature is composed of n BLS signatures computed sequentially like a blockchain. Under the computational Diffie-Hellman assumption, their reduction loss is $$n\cdot q_H^{1/n}$$ for $$q_H$$ hash queries and it is logarithmically tight when $$n=\log {q_H}$$ . However, it is currently unknown whether a better reduction than logarithmical tightness for the chain-based unique signatures exists. We show that the proposed chain-based unique signature scheme by Guo et al. must have the reduction loss $$q^{1/n}$$ for q signature queries when each unique signature consists of n BLS signatures. We use a meta reduction to prove this lower bound in the EUF-CMA security model under any non-interactive hardness assumption, and the meta-reduction is also applicable in the random oracle model. We also give a security reduction with reduction loss $$4\cdot q^{1/n}$$ for the chain-based unique signature scheme (in the EUF-CMA security model under the CDH assumption). This improves significantly on previous reduction loss $$n\cdot q_H^{1/n}$$ that is logarithmically tight at most. The core of our reduction idea is a non-uniform simulation that is specially invented for the chain-based unique signature construction.
Effect of Noise on Concurrence of Compact Photonic CNOT Gate Designed Using Universal Cloner

In this paper a two Qubit deterministic compact photonic CNOT gate is analyzed under noisy conditions. Concurrence of the designed compact photonic CNOT gate has been calculated with and without noise parameter. The behavior of this gate is universal. The feasibility of the scheme by observing variation in concurrence with different system parameters are assessed and reported. In strong ( $$g/k = 2.4$$ and $$k_s /k = 0.05$$ ) and weak ( $$g/k = 0.1$$ and $$k_s /k = 0.01$$ ) coupling condition the concurrence calculated is 0.01441 and 0.753, respectively. Under noisy environment the computed concurrence in strong ( $$g/k = 2.4$$ and $$k_s /k = 0.05$$ ) and weak ( $$g/k = 0.3$$ and $$k_s /k = 1$$ ) coupling condition is 0.1231 and 0.5265, respectively.
CAVE — SANCTUARY — MYTH: INTERPRETATION CRITERIA FOR THE UPPER PALEOLITHIC DECORATED CAVES IN THE URALS

Caves with the Upper Paleolithic wall paintings are known in the Southern Urals — Kapova (Shulgan-Tash) and Ignatievskaya (Yamazy-Tash). They represent the largest karst cavities in their areas with lots of figurative and non-figurative motifs. In many respects, the Urals’ decorated caves are similar to those in Western Europe: in terms of the structural organization of space, pictorial assemblages and their context. The Western European caves are often referred to as “deep sanctuaries”. Within the framework of this article, the authors address to the works of foreign experts, who have been developing this subject for more than a dozen years, in order to identify the criteria for such an interpretation. Due to the kinship of the Ice Age cave art from the Atlantic to the Urals, the interpretations of Western European art are also applicable to the Ural cave sites. Archaeological, phenomenological and mythological criteria stand out as the most important ones. Archaeological criteria were formulated by C. Renfrew: focusing attention; the border zone between this and other worlds; the presence of a deity; participation and offering. Phenomenological criteria are relied on a person’s sensory experience which is based on the main sense organs — sight, hearing, smell, touch. It is they which provide the caves with indicators of hierophony, constructing, according to M. Eliade, a collective sacred space. Of all the characteristics of myth in relation to the Paleolithic art, M. Groenen considers the presence of composite anthropomorphic creatures, composite animals and surreal, fantastic animals. Comparing unreal and hybrid creatures from the regions of Franco-Cantabria and the Urals shows their close similarity. Together with the considered archaeological criteria and criteria of the sensory experience of an ancient man in relation to the underground cavity, we can assert that the Ural decorated caves of the Upper Paleolithic are sanctuaries of the Ice Age, preserving fragments of the mythology of that distant time.
HkAmsters at CMCL 2022 Shared Task: Predicting Eye-Tracking Data from a Gradient Boosting Framework with Linguistic Features

Eye movement data are used in psycholinguistic studies to infer information regarding cognitive processes during reading.In this paper, we describe our proposed method for the Shared Task of Cognitive Modeling and Computational Linguistics (CMCL) 2022 -Subtask 1, which involves data from multiple datasets on 6 languages.We compared different regression models using features of the target word and its previous word, and target word surprisal as regression features.Our final system, using a gradient boosting regressor, achieved the lowest mean absolute error (MAE), resulting in the best system of the competition.
Impact of solar activity on the seasonal rainfall of Kerala, India

In this paper, we explore the influence of solar activity on rainfall over Kerala, India. Sunspot number, a proxy for solar activity and rainfall over Kerala in mm, during 1871-2016 are used for the study. Correlative studies are performed for the seasonal months, January-February (JF), March-May (MAM), June-September (JJAS), and October-December (OND) and the corresponding correlation coefficients and their significance are computed. We find that Kerala rainfall is correlated with solar activity, with varying significance. JF and JJAS seasons show a significant correlation, compared to other seasons, for short time scales. Contour plots are drawn to detect periodicity in the seasonal rainfall and sunspot numbers. Common periodicities are noted for JF and OND seasons, at ~ 10 years and MAM seasons at ~ 2-3 years, indicating a possible influence of solar activity over rainfall. We speculate a physical association between solar activity and the rainfall variability.
Development of Standards for Production of Immersive 360 Motion Graphics, Based on 360 Monoscopic Videos: Layers of Information and Development of Content

Virtual reality and immersive technologies are currently in full development. One of the most widely used formats in the medium are the 360 linear videos, which are proliferating thanks to the 360 cameras available today for the user. The forms of production, including filming and post-production, in this new medium have been transformed in many of their technical procedures. But what about computer-generated graphics, such as motion graphics? The creation of linear video content with the motion graphics technique, although increasingly common, requires specific procedures and techniques that differ from formats that do not fall into the category of 360, immersive or otherwise. In this paper, we aim to establish a series of mechanisms and standards, based on the knowledge gained from experience in filmed 360-degree videos, to help facilitate the development of motion graphics proposals, also considering the parameters of usability in virtual reality.
Text Region Identification from Natural Scene Images Using Semi-Supervised MSER Method

In today’s world, visual detection and recognition of text from an image are very demandable due to its application in content-based image retrieval, robotic navigation, and automatic number plate recognition, extracting information from passport or business cards or bank statements, etc. Text detection in natural images has been increasingly popular because text images represent much technical and digital information. Text can be traced in scattered form from any natural scene images, and it is available in different fonts, colors, and shapes. This paper has been classified into five main chapters. The first chapter presents an idea about the domain of text detection, different challenges, and the motivation behind the present work. The second chapter highlights the evolution of different text detection methods and provides a literature survey of the novel approaches utilized in the avenue of text detection. A brief introduction of the datasets has also been included in this paper. The third chapter provides details of the study of text detection using the maximally stable extremal region (MSER) method. The proposed methodology has been mathematically established for the localization of the texts. The fourth chapter highlights the efficiency of the method in localizing texts in natural images. The corresponding challenges related to our work and the advantages of using this method are also described in this paper. The last chapter concludes and discusses relevant future scope in scene text understanding in natural Images.
Face recognition Attendance system using HOG and CNN algorithm

Recognition of faces is one of the most useful applications and has a critical role in the technological field. Recognizing the face is a lively concern for authentication, specifically in the context of taking attendance. Attendance system using face recognition is a process of recognizing the profile of the person by using facial features supported by various computing technology and monitoring. The evolution of this process is focused on achieving the digitizing of the orthodox system of taking manual attendance. Current approaches for taking attendance are monotonous and inefficient. Manual records of attendance can be easily manipulated. The orthodox process of checking attendance such as current fingerprint or card scanning systems are susceptible to proxies. To tackle these issues, this paper has been proposed. The proposed system makes the utilization of various algorithms such as histogram of oriented Gradient (HOG), convolutional neural network (CNN) and support vector machine (SVM). After the face is recognized, the reports of attendance are going to be created, maintained and stored in excel format. The system is examined in various situations like illumination, head movements, and the variation of distance between the face and cameras. The proposed system was found to be efficient and reliable for marking attendance during a classroom with negligible time consumption and no manual work. This system is inexpensive as less installation is required.
Software Defect Prediction Method Based on Cost-Sensitive Random Forest

In this paper, a new method was proposed to reduce the misclassification cost of software defect prediction under the condition of imbalanced classes. The effectiveness of the method was evaluated by the correct rate of sample classification, precision rate, recall rate, and F-Measure index. The main results of this research were as follows: (1) The proposed method can maintain a high accuracy rate while maintaining a relatively low cost of misclassification; (2) In the data preprocessing stage, a median assignment method is proposed, Used to deal with the field missing value problem of a reasonable sample in the data set; (3) In the classification stage of the decision tree and the voting classification stage of the formed random forest, the cost-sensitive factors defined according to different objects are introduced respectively, and the training is based on the cost-sensitive The improved random forest model. Experimental results show that this method can reduce the cost of misclassification while maintaining a high accuracy rate.
Implementation of the Multiliteracy Learning Model in Advertisement Text for MTs Students

Multiliteracy learning was learning that puts the ability of reading, writing, listening, and speaking to improve thinking skills including the ability to criticize, analyse, and evaluate.This study aimed to determine the planning and implementation of a multiliteracy learning model in advertisement text for MTs students.The type of research used in this study used descriptive qualitative research with a case study approach.The subjects of this study were Indonesian language teachers and students of VIII class.Data collection techniques were observation, interviews, and documentation.The interactive model analysis consists of four components, namely data collection, data reduction, data presentation, and drawing conclusions.The results showed that the planning for using the multiliteracy model that was prepared were lesson plans, observation guidelines, advertising media, student worksheets, and evaluations.The implementation of the multiliteracy learning model in advertising text applies four principles.Student learning outcomes were able to present advertising texts with creative ideas.The sentences in the advertisement were in accordance with the image, concise, clear, right-on target, interesting, and have used persuasive sentences.