Reviewerly/exHarmony huggingface dataset:
Factors in the development of logical thinking in mathematics lessons

This article describes the views formed on the basis of science and experience aimed at developing independent thinking and creativity in students in the educational process. In this regard, it can be said that the teaching module of the subject in the education system: educational-methodical complex, electronic educational-methodical complex (syllabus), glossary in many respects has the ability to provide students with a variety of additional information. Such cases are quite common in the interpretation of data types and additions, data classifications. This is probably necessary for transition textbooks, but they cannot be justified from a mathematical and didactic point of view. Therefore, such withdrawals should be phased out of textbooks.
Modeling Frustration Trajectories and Problem-Solving Behaviors in Adaptive Learning Environments for Introductory Computer Science

Modeling a learner’s frustration in adaptive environments can inform scaffolding. While much work has explored momentary frustration, there is limited research investigating the dynamics of frustration over time and its relationship with problem-solving behaviors. In this paper, we clustered 86 undergraduate students into four frustration trajectories as they worked with an adaptive learning environment for introductory computer science. The results indicate that students who initially report high levels of frustration but then reported lower levels later in their problem solving were more likely to have sought help. These findings provide insight into how frustration trajectory models can guide adaptivity during extended problem-solving episodes.
Exploring the Role of Graphic Design-Driven Virtual Reality in Sustainable Tourism

Since the COVID-19 pandemic, tourism has declined precipitously as a result of lockdown procedures that have prohibited the majority of international travel and ultimately increased people's online activity at home. Virtual reality (VR) is an example of an online activity that simulates and infiltrates the real world through games and interactive media. This chapter aims to determine whether using newer generation wearable devices to create virtual reality experiences via immersive graphic design has an effect on people's intentions to visit virtual destinations and tourist attractions in lieu of physical travel. The chapter also determines the effect of perceived visual appeal (PVA) and emotional involvement (EI) on behavioural intentions toward visiting a cultural heritage site in a virtual destination. Using the Saudi Arabian holy pilgrimage of Hajj as a case study, the chapter hypothesises that delivering the PVA of the virtual experience via virtual reality via immersive graphic design will result in a significant increase in intentions to visit a virtual destination.
Формирование медиаграмотности будущих воспитателей дошкольных образовательных учреждений

The article deals with the relevance and necessity of forming media literacy among future pre-school teachers. The aims, objectives, means and features of using digital technologies in education are analyzed. The role of digital technologies in the process of forming the media literacy of future pre-school teachers is examined. The results of the pilot study on the development of digital media literacy among future pre-school teachers are presented in the article.
Densely connected convolutional networks for breast cancer histopathological image classification

The computer-assisted classification of breast cancer histopathological image in the future is an essential method for the improvement of the diagnostic performance, thus reducing breast cancer deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs, many image classification tasks still remain challenging due to the insufficiency of training data and the lack of the ability to focus on improving classification efficiency. To address these issues, we share a densely connected convolutional network (DenseNet) model that is composed of 3 layers DenseNet, pooling layer, and a classification layer for breast cancer classification in microscopic images. Each layer of DenseNet contains 4 dense blocks. Each dense block jointly uses the dense connection and a novel attention learning mechanisms to increase its ability for discriminative representation. Meanwhile, the transfer learning algorithm is applied to determine the model parameters to extract the features of the patient image that is performed. In order to ensure sufficient data volume, a data enhancement method based on the quad-tree principle is proposed for high-resolution images. On the other hand, the classification probability of each part after dicing is fused by three algorithms of addition, product, and maximum. We evaluated our DenseNet model on the BreastKHis dataset. Our results indicate that the DenseNet model and data enhancement method we adopted can adaptively focus on the study of breast cancer histopathological image classification, thus achieving the state-of-the-art performance in breast cancer classification. The results of the experiments are in terms of patient-level and image-level accuracy. The best recognition accuracy increased to 90.9%-92.5% and 89.3%-91.8%, respectively, compared with previous studies.
Efficient Mini-batch Training for Echo State Networks

research-article Share on Efficient Mini-batch Training for Echo State Networks Authors: Chunyuan Zhang Hainan University, China Hainan University, ChinaView Profile , Chao Liu Hainan University, China Hainan University, ChinaView Profile , Jie Zhao Hainan University, China Hainan University, ChinaView Profile Authors Info & Claims ICRAI 2020: 2020 6th International Conference on Robotics and Artificial IntelligenceNovember 2020 Pages 236–240https://doi.org/10.1145/3449301.3449341Online:09 June 2021Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Study on the application of solar power generation in gymnasiums

Abstract To accelerate the promotion of green and low-carbon development, the development of green buildings, new energy development into the “14th Five-Year” planning recommendations, in the global climate change, energy shortage situation, solar power generation as a clean and renewable energy, in today’s society has received more and more attention. Drived by China’s successful hosting of the 29th Summer Olympic Games in 2008 and its bid for the 2022 Winter Olympic Games in 2015, there has been a boom in the construction of sports venues and facilities across the country. Study on the present situation of the application of solar power in the stadium hall has carried on the system analysis and research.
Chinese Government Official Document Named Entity Recognition Based on Albert

The automated processing of Chinese government documents is in its early stage, and information extraction based on Named Entity Recognition (NER) plays an important role in the automated processing and analysis of Chinese government documents. This paper proposes and implements the pre-trained language model called GovAlbert Based on Albert which the pre-trained language model, which for the processing of Chinese government official documents. We study and analyze NER tasks of the Chinese government official document based on the pre-trained language model, and annotate the Chinese government official documents' Entity recognition corpus, and construct four named entity recognition models based on GovAlbert. The experimental results show that the GovAlbert model for government official document processing has an improved macro-average F1 value (harmonized average of accuracy and recall) than Albert. four named entity recognition models based on GovAlbert in multiple NER tasks of government official documents are all better than the public pre-training model, and through experiments, it has been explored that the GovAlbert-CRF combined model can achieve the best F1 value, so it can be better qualified for the NER tasks of government official documents.
HDM : AN OPEN FRAMEWORK FOR DEALING WITH BIG DATA

over the previous years, systems, for example, MapReduceas well as Spark have been acquainted with facilitate the errand of growing enormous information projects and applications. Nonetheless, the occupations in these systems are generally characterized and bundled as executable containers without any usefulness being uncovered or depicted. This implies conveyed employments are not locally composable and reusable for ensuing improvement. Moreover, it additionally hampers the capacity for applying advancements on the information stream of employment arrangements and pipelines. In this paper, we present the Hierarchically Distributed Data Matrix (HDM) which is a practical, specifically information portrayal for composing huge information applications. Alongside HDM, a runtime system is given to help the execution, incorporation and the board of HDM applications on circulated frameworks. In light of the practical information reliance chart of HDM, different advancements are connected to improve the presentation of executing HDM employments. The exploratory outcomes demonstrate that our improvements can accomplish enhancements between 10% to 40% of the Job-Completion-Time for various sorts of uses when contrasted and the current condition of workmanship, Apache Spark.
Scapegoat or Saviour: The Role of Design in Upcycling Research

Upcycling is a portmanteau term that researchers have filled with a range of ideas and practices. Many of these remain restless in terms of consistent use and stability of definition. ‘Design’ is one of those unsettled terms, and this chapter explores that restiveness by considering, (i) design in upcycling literature, (ii) design in upcycling practice, (iii) the relationship of design to other common upcycling terms including waste and value, (iv) and to provide some notion of what ‘design for upcycling’ might include.
Measurement algorithm for wafer alignment based on principal component analysis

We propose a novel measurement algorithm for wafer alignment technology based on principal component analysis (PCA) of a mark image. The waveform of the mark is extracted from the enlarged mark image, which is collected by CCD. The position of the mark center on the CCD can be calculated based on the extracted waveform. By applying PCA to the mark image, the first principal component containing position information of the mark can be obtained. Therefore PCA can be used to extract the waveform from the mark image. Compared with the typical waveform extraction method (the summed projection (SP) method), the proposed PCA method can use the position information contained in the mark image more effectively. Through simulation and experiment, it is proved that the proposed PCA method can improve the contrast of the normalized waveform, and then improve the alignment accuracy.
Language Model Fine-Tuning with Second-Order Optimizer

At present, the artificial intelligence domain is completely dependent on first-order optimizers, the popular ones of which are stochastic gradient descent (SGD) and Adam. And it is obvious also as both of these give good rate of convergence and are lightweight as these have decent memory and computation requirements. This paper proposes a second-order training approach to train deep neural networks. The experiments are conducted in different domains, namely image classification and natural language processing. A comparison of first order and second order is presented for readers. In this study, the second-order quasi-Newton approach based on approximate Hessian calculation is applied to the famous natural language processing model Google BERT, which improves the peak accuracy in fewer iterations than Adam. This paper shows results of multiple implementations of second-order method on NLP and image classification tasks, and both these domains have shown improvements.
A Novel Fog-based Framework for Preventing Cloud Lock-in while Enabling Searchable Encryption

Cloud computing has helped in managing big data and providing resources remotely and ubiquitously, but it has some latency and security concerns. Fog has provided tremendous advantages over cloud computing which include low latency rate, improved real-time interactions, reduced network traffic overcrowding, and improved reliability, however, security concerns need to be addressed separately. Another major issue in the cloud is Cloud Lock-in/Vendor Lock-in. Through this research, an effort has been made to extend fog computing and Searchable Encryption technologies. The proposed system can reduce the issue of cloud lock-in faced in traditional cloud computing. The SE schemes used in this paper are Symmetric Searchable Encryption (SSE) and Multi-keyword Ranked Searchable Encryption (MRSE) to achieve confidentiality, privacy, fine-grained access control, and efficient keyword search. This can help to achieve better access control and keyword search simultaneously. An important use of this technique is it helps to prevent the issue of cloud/vendor lock-in. This can shift some computation and storage of index tables over fog nodes that will reduce the dependency on Cloud Service Providers (CSPs).
Light Field Disparity Map Enhancement with Morphological Filtering

Light field disparity estimation algorithms are comprised of two steps: an initial estimation step and a global optimization step. The initial estimation is often noisy and may contain high amplitude artefacts. Global optimization techniques might inadequately propagate these artefacts, providing suboptimal results. In this paper, an iterative morphological filter is proposed as an intermediate step or replacement to global optimization techniques. This algorithm iteratively filters the disparity map with an average of Open followed by Close and Close followed by Open morphological operations, enabling the removal of artefacts and noise, without adversely affecting the structure of the disparity map. The iterative open-close close-open filter attenuates the effect of artefacts and noise from an initial disparity estimation, achieving improvements of up to 90%, and more than 30%, on average, in terms of mean square error, when applied to the a structure-tensor-based initial estimation. In addition, the proposed method proves to be competitive with another state of the art algorithm, in terms of mean square error, and superior in terms of percentage of bad pixels.
A Novel Improved Grey Wolf Optimization Algorithm Based Resource Management Strategy for Big Data Systems

Presently, big data is very popular, since it finds helpful in diverse domains like social media, E-commerce transactions, etc. Cloud computing offers services on demand, broader networking access, source collection, quick flexibility and calculated services. The cloud sources are usually different and the application necessities of the end user are rapidly changing from time to time. So, the resource management is the tedious process. At the same time, resource management and scheduling plays a vital part in cloud computing (CC) results, particularly while the environment is employed in the analysis of big data, and minimum predictable workload dynamically enters into the cloud. The identification of the optimal scheduling solutions with diverse variables in varying platform still remains a crucial problem. Under cloud platform, the scheduling techniques should be able to adapt the changes quickly and according to the input workload. In this paper, an improved grey wolf optimization (IGWO) algorithm with oppositional learning principle has been important to carry out the scheduling task in an effective way. The presented IGWO based scheduling algorithm achieves optimal cloud resource usage and offers effective solution over the compared methods in a significant way.
Nontransitive Policies Transpiled

Nontransitive Noninterference (NTNI) and Nontransitive Types (NTT) are a new security condition and enforcement for policies which, in contrast to Denning's classical lattice model, assume no transitivity of the underlying flow relation. Nontransitive security policies are a natural fit for coarse-grained information-flow control where labels are specified at module rather than variable level of granularity. While the nontransitive and transitive policies pursue different goals and have different intuitions, this paper demonstrates that nontransitive noninterference can in fact be reduced to classical transitive noninterference. We develop a lattice encoding that establishes a precise relation between NTNI and classical noninterference. Our results make it possible to clearly position the new NTNI characterization with respect to the large body of work on noninterference. Further, we devise a lightweight program transformation that leverages standard flow-sensitive information-flow analyses to enforce nontransitive policies. We demonstrate several immediate benefits of our approach, both theoretical and practical. First, we improve the permissiveness over (while retaining the soundness of) the nonstandard NTT enforcement. Second, our results naturally generalize to a language with intermediate inputs and outputs. Finally, we demonstrate the practical benefits by utilizing state-of-the-art flow-sensitive tool JOANA to enforce nontransitive policies for Java programs.
Research on Evaluation Algorithm of Teacher's Teaching Enthusiasm Based on Video

Most of the current research on teachers' teaching enthusiasm is to define teachers' emotions in a qualitative way, but lacks intuitive digital or image representations. Based on the online teaching platform, this research collects various types of teaching videos, and establishes a teacher's teaching emotion data set based on the PAD emotion model, then extracts teacher's teaching features through various algorithms such as sound feature extraction, facial expression recognition, target detection, and pose estimation. Analyze the features of different modalities, and cascade the features based on the contribution of each feature to the result. Finally, use BP neural network to predict teachers' enthusiasm. The study found that the model prediction performance is better after adopting the cascading feature fusion method based on feature contribution. The loss of the P prediction model is 0.0586, the loss of the A prediction model is 0.0517, the loss of the D prediction model is 0.0428, and the average loss is 0.0510.
Inequality and Gender Inclusion: Minimum ICT Policy Thresholds for Promoting Female Employment in Sub-Saharan Africa

The study assesses how ICT modulates the effect of inequality on female economic participation in a panel of 42 countries in sub-Saharan Africa over the period 2004-2014. Three inequality indicators are used, namely: the Gini coefficient, the Atkinson index and the Palma ratio. The adopted ICT indicators are mobile phone penetration, internet penetration and fixed broadband subscriptions. Three gender economic inclusion indicators are also used for the analysis, namely: female labour force participation, female unemployment and female employment. The Generalised Method of Moments is employed as empirical strategy. The findings show that enhancing ICT beyond certain thresholds is necessary for ICT to mitigate inequality in order to enhance gender economic participation. First, for female labour force participation, a minimum threshold of 165.714 mobile phone penetration per 100 people is required for the Palma ratio. Second, minimum ICT thresholds for the reduction of female unemployment are: 87.783, 107.486 and 152.500 mobile phone penetration per 100 people for respectively, the Gini coefficient, the Atkinson index and the Palma ratio; 39.618 internet penetration per 100 people for the Atkinson index and 4.500 fixed broadband subscriptions for the Palma ratio. Third, the corresponding ICT thresholds for the promotion of female employment are: 120.369 and 85.533 mobile phone penetration per 100 people for respectively, the Gini coefficient and the Atkinson index and 30.005 internet penetration per 100 people for the Gini coefficient. The established thresholds make economic sense and can be feasibly implemented by policy makers in order to induce favourable effects on gender economic inclusion dynamics.
How to use human pose estimation to measure the hand-arm motion in craft application with no influence on the natural user behavior

The interaction between human and machine plays an important role in the design and optimization of human-machine systems. This interaction is characterized by human motion using the technical system. Especially in the field of hand and power tool applications, the motion capture should be performed under the real working condition and without influencing the user. There are already motion tracking systems that allow capturing the motion during the interaction, but there is no mobile motion capture system that allows an individual analysis of the user for biomechanical analysis in the normal work process without influencing him. Therefore, requirements for a motion capture system are derived and a system is presented that meets these requirements. This system consists of two cameras and is based on the pose estimation algorithm OpenPose. The comparison of the presented system and the state-of-the-art system Xsens is performed and based on the measured elbow angle and the wrist position. The results show a very good correspondence between the curve characteristic of the elbow angle and the wrist position of both systems. However, inexplicable values shifting at two different levels still occur, which need to be investigated further. Overall, the presented system shows great potential in terms of mobility and flexibility of the presented system with some weaknesses in the data processing and efficiency. By addressing these weaknesses, the presented system can be used in the analysis and optimization of human-machine systems.
Head pose‐free gaze estimation using domain adaptation

Human gaze information has been widely used in various areas, such as medical diagnosis and human–computer interactions (HCI). This study proposes a head pose-free 3D gaze estimation method using a deep convolutional neural network (DCNN). To infer gaze direction, only a small grayscale image is required without any special devices such as an infrared (IR) illuminator and RGBD sensor. A domain adaptation method to reduce the feature gap between real and synthetic image data is also proposed here. Moreover, a novel synthetic dataset (SynFace) that contains head poses, gaze directions, and facial landmarks is established and released. The proposed method outperforms state-of-the-art methods and achieves a mean error of less than 4○.