Reviewerly/exHarmony huggingface dataset:
Digitalization and Its Dimensions in a Social and Educational Context

The aim of the article is to show the role of digital skills and literacy as a consequence of the processes of digitalization, but also as a prerequisite for conducting online learning in educational systems. The different dimensions of digitalization and the formation of digital skills by people of different ages and professions are analysed. The methodology is based on various surveys, including an online survey conducted by the authors of the article among the Bulgarian population on the state of digitalization and online learning. An important place in the study is occupied by online education in secondary and higher education in the situation of the COVID-19 pandemic. Views and assessments of the participants in the learning process and their readiness for mobile learning are shown. The main conclusion of the article is that in Bulgarian society there is a relatively good availability of digital skills, which are important for modern processes.
Degraded Factors Analysis in Multimedia Data Using Deep Learning Algorithm

Because artificial illumination is created in an indoor environment, multimedia data is recorded in an optimal atmosphere. On the other hand, in an outdoor setting, it is critical to eliminate the weather influence. Outdoor vision devices are used to capture scenes in surveillance. Due to the enormous size of the drips, the items will get motion blurred in changing weather conditions. Several computer vision techniques that employ feature information, such as object identification, tracking, segmentation, and recognition, will be harmed by these disturbances. Even if only a little portion of the object is obscured, the object cannot be accurately tracked. Rainfall features that pixel image is not entirely surrounded by rain in all data. The dynamic adverse weather model is researched for restoration resolution. Rainfall is a very important part of a bad weather system. Rain-fed energy has a strong local structure and is strongly influenced by backlight. When light passes, it is repeated and visible, shining in the surrounding area. The movement fades, when it falls at a high rate. The vehemence of the rain line is therefore, determined by the light of the descent, the radiation of the backdrop, and the unification time of the camera. Particles of rain and ice are very difficult to analyze. Rain-like spatial and temporal occurrences can be produced by some scene dynamics. In this work, we will look into ambient light estimation for submitted multimedia material using image processing algorithms. With in-depth reading techniques, we improved light measurement and used the histogram measurement method.
Text Summarization Using Combination of Sequence-To-Sequence Model with Attention Approach

In daily life, we come across tons and tons of information which can be related to news articles or any kind of social media posts or customer reviews related to product. It is difficult to read all the content due to time constraint. Being able to develop the software that can identify and automatically extract the important information. There are two types of summarization methods. Extractive text summarization is the method where it picks the important content from the source text and gives same in the form of short summary, and on the other hand, abstractive summarization is the technique where it gets the context of the source text, and based on that context, it regenerates small and crisps summary. In this paper, we use the concept of neural network with attention layer to deal with abstractive text summarization that generates short summary of a long piece of text using review dataset.
Analysis of college students’ canteen consumption by broad learning clustering: A case study in Guangdong Province, China

Investigation on college students' consumption ability help classify them as from rich or relative poor family, thus to distinguish the students who are in urgent need for government's economic support. As canteen consumption is the main part of the expenses of the college students, we proposed the adjusted K-means clustering methods for discrimination of the college students at different economic levels. To improve the discrimination accuracy, a broad learning network architecture was built up for extracting informative features from the students' canteen consumption records. A fuzzy transformed technique was combined in the network architecture to extend the candidate range for identifying implicit informative variables from the single type of consumption data. Then, the broad learning network model is fully trained. We specially designed to train the network parameters in an iterative tuning mode, in order to find the precise properties that reflect the consumption characteristics. The selected feature variables are further delivered to establish the adjusted K-means clustering model. For the case study, the framework of combining the broad learning network with the adjusted K-means method was applied for the discrimination of the canteen consumption data of the college students in Guangdong province, China. Results show that the most optimal broad learning architecture is structured with 14 hidden nodes, the model training and testing results are appreciating. The results indicated that the framework was feasible to classify the students into different economic levels by analyzing their canteen consumption data, so that we are able to distinguish the students who are in need for financial aid.
Recognition for American Sign Language letters based on AutoML platform

The advent of sign language has given the deaf a more accurate and convenient way to express themselves, but not everyone knows sign language. Hearing-impaired and deaf people still find it difficult to communicate with normal people without interpreters. Therefore, the application of computer vision technology to America Sign Language letter (ASL) recognition will greatly facilitate such silent communication. However, the development of ASL recognition programs requires engineers to have extensive knowledge of sign language, which is very difficult to achieve. To solve this limitation, our study applied automatic machine learning (AutoML) technology to ASL recognition based on the Google Cloud Platform (GCP). Firstly, we standardized the labels and augmented some of the images. Subsequently, the preprocessed data set was used to train the motion detection model. During training the model, we built and normalized the model to improve the accuracy of the model. Finally, the accuracy and recall rate of the proposed method achieved 99.5% and 92.7%, respectively. In addition, the model was also deployed to an Android system for testing.
Context-based interactive multimodal sentiment analysis

In view of the singleness of traditional text emotion analysis, multimodal emotion analysis extends emotion analysis to the analysis level of text, image, sound and so on. In order to use the context interaction information expressed in each modality, a multimodal interactive emotion classification model based on video context is proposed in this paper. The ALBERTBiGRU network structure is built for text feature learning, and the independent BIGRU model is used to extract context features from text, audio and video modality. Based on the attention mechanism, the emotion analysis task is completed after the fusion of the three modal features. Compared with the existing models on MOSI and IEMOCAP data sets, the accuracy and F1 value of emotion classification reached 81.71% and 81.44 on MOSI data set, 66.97% and 67.20 on IEMOCAP data set, which were 1.41% and 2.16% higher than the highest benchmark value respectively, effectively improving the accuracy of multimodal emotion prediction.
Chinese text paraphrase recognition based on OpenPrompt introducing hybrid prompts

The fine-tune paradigm adopted by traditional paraphrase recognition tasks cannot fully exploit the knowledge of pretrained language models (PLMs). At this stage, the Prompt paradigm reconstructs downstream tasks by constructing templates to make it more suitable for the training form of PLM. However, there are many processes, the code base is not supervised, and a single discrete template limits the model prediction ability. In response to this problem, this paper proposes a method of introducing hybrid prompts based on OpenPrompt. OpenPrompt makes the Prompt process have a unified framework, and hybrid prompts solve the problem that discrete templates cannot fully mine PLM knowledge. This paper first constructs a hybrid template with [mask] slots, and then transforms the original input through the template to obtain x<sub>prompt</sub> , and then x<sub>prompt</sub> is input to the bert-base-chinese model of the multi-layer bidirectional transformer based on the attention mechanism for training. When the model trains the optimal prompt, calculate the label with the highest probability of filling in the label set, and finally map the label to the prediction result. The experimental results show that compared with the fine-tune paradigm, the F1 value of the prompt paradigm exceeds 4.69% under the same PLM and hyperparameters. Compared with not using soft prompt, the average accuracy and average F1 value obtained with soft hints are 2.294% and 2.31% higher, respectively. Meanwhile, when the number of soft hints is 6, the accuracy and F1 value reach the highest.
Comparative analysis of mask detection models and mobile device deployment based on different automated machine learning platforms

In the current situation of a severe epidemic, the feasibility of model training on the automated machine learning platform and deployment on mobile terminal for mask detection is feasible since it may provide higher accuracy. In this study, we optimized the original dataset, adjusted the quantity proportion of various types, screened the image quality to alleviate the effect of the overfitting. The dataset is made into an open source that supports different formats and has been uploaded to Kaggle. Then, the trained models based on Create ML and EasyDL platforms are deployed in the mobile terminal, the accuracy of each model and detection effect on mobile terminals were obtained, respectively. Lastly, the advantages and disadvantages of the models and applications of the two platforms (Create ML and EasyDL) are compared.
Some typical algorithms of Chinese handwriting text recognition in history

Language, the most apparent distinction between the human and the animal, has been focused by the data scientists in recent decades. They are dying to discover a method that the human terms can be translated into machine language. After the seething of the English recognition, more researchers focus on the Chinese recognition both in China and abroad. However, differing with the English recognition, Chinese recognition is more challenging because of the tremendous character categories. Moreover, compared with single character, Chinese text recognition can be more challenging due to the join-up and the different writing styles. In this article, three algorithms about the Chinese text recognition are going to be illustrated and evaluated, including the segmentation recognition and the no segmentation. In the segmentation recognition part, we will describe briefly about the single character recognition because the segmentation recognition process includes this representative method, and we also introduce a classical algorithm about Chinese text recognition written by Wang in 2012 [1]. In the no segmentation part, we display two kinds of ways to solve the problem. One is based on HMM and Another is based on FCRN.
Image sentiment analysis method based on multi-level feature fusion

With the development of social platforms, people are more likely to use pictures to share their sentiments and opinions. With the development of deep learning, the current mainstream image sentiment analysis research mainly focuses on the use of high-level semantic sentiment features of images, and less on the use of low-level sentiment features such as colors, lines and so on. Aiming at the above problems, this paper proposes an image sentiment analysis model based on multi-level feature fusion (MLFF). Firstly, the convolution neural network is used to learn different levels of image features; Then the multi-level features are input into the bidirectional sequential neural network, and attention is introduced to fuse to generate multi-level fusion features; Finally, the multi-level fusion features are input to the full connection layer for classification. The experimental results show that the fusion model proposed in this paper can make effective use of multi-level image features and effectively improve the performance of image sentiment analysis.
Retrieval methodology for similar NPP LCO cases based on domain specific NLP

Nuclear power plants (NPPs) have technical specifications (Tech Specs) to ensure that the equipment and key operating parameters necessary for the safe operation of the power plant are maintained within limiting conditions for operation (LCO) determined by a safety analysis. The LCO of Tech Specs that identify the lowest functional capability of equipment required for safe operation for a facility must be complied for the safe operation of NPP. There have been previous studies to aid in compliance with LCO relevant to rule-based expert systems; however, there is an obvious limit to expert systems for implementing the rules for many situations related to LCO. Therefore, in this study, we present a retrieval methodology for similar LCO cases in determining whether LCO is met or not met. To reflect the natural language processing of NPP features, a domain dictionary was built, and the optimal term frequency-inverse document frequency variant was selected. The retrieval performance was improved by adding a Boolean retrieval model based on terms related to the LCO in addition to the vector space model. The developed domain dictionary and retrieval methodology are expected to be exceedingly useful in determining whether LCO is met.
Probability and random processes (4th edition) by Geoffrey R. Grimmett and David R. Stirzaker, pp. 669, £40 (paper), ISBN 978-019884759, Oxford University Press (2020)

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Universal properties

In this chapter we formalize the idea of characterizing things by the role they play in context. First we discuss the difference between characterizing something by role and characterizing it by intrinsic characteristic. We consider “extremities” in some of the small drawable categories we’ve seen before, that is, the places where all arrows start or all arrows end. We make this more precise and more formal in the definition of initial objects. We explore what sort of categories do not have initial objects, including those with an infinite string of composable arrows, non-trivial loops, or disconnected parts. We prove that initial objects are unique up to unique isomorphism. We then define terminal objects and explore them analogously, mentioning the fact that this is the dual concept, although duality is discussed more fully in the next chapter. We then examine terminal and initial objects in some of the categories we’ve seen already: sets, posets, monoids, groups, and different categories of privilege. We observe that we can only describe universal properties in context, as something initial in one category may not be initial in another.
A Study on the Competency and Needs of Teachers for the Operation of the High School Credit System: Focusing on High Schools in Chungcheongnam Province

Purpose: The purpose of this study was to specify the competencies required of teachers for the smooth operation of the high school credit system, analyze the demand for support depending on the operating competency, and derive the parts to be supported by the Office of Education for an effective high school credit system.&#x0D; Methods: To this end, sixteen competencies required for the operation of the high school credit system were noted, A survey was conducted with seventy-five teachers working on high school credit system research and leading schools in Chungnam, and the competency demand was analyzed using Borich's demand analysis and The Locus for Focus model.&#x0D; Results: The competencies with the highest demand were ‘career exploration guidance’, ‘personal timetable management’, ‘student demand analysis’, ‘selection of completion standards by subject’,‘academic design consultation’ and ‘online media utilization’. As a result of analyzing this data by dividing it by each school‘s size, 'space utilization' and 'online media utilization' capabilities were high in large schools, and 'academic design counseling' and 'multi-subject instructional design operation'were high in small and medium-sized schools.&#x0D; Conclusion: Based on the results of the study, it was proposed to provide guidelines for completion standards for each subject, activate guidance programs for students who have not completed the areas,establish career exploration support programs and networks, organize and support professional support groups for the high school credit system, and assign professional teachers.
Sentence Structure and Boundary for Deep Neural Machine Translation Alignment Model

In terms of social globalization, the world remains more connected than ever due to the widespread use of digital technologies. The translation is a gate for those communication. When we want to understand, study, and express any kind of information prepared in a language other than one's native language, no matter where you are in the world, we will need translation. Due to social media usage, consumers are more likely to get information written by other users in foreign languages, and researchers need to conduct research in many languages and publish the results in one's second languages. Because professional translation requires so much hard work, automated translations, known as machine translations, have played a vital role in helping millions of consumers understand information written in a foreign language. In addition to being used by ordinary users to make every day common translation, it is also possible to help professional translators translate quickly. The modern neural machine translation not only performs better than systems that consider the sentences structure, but is also able to find complex relationships for those translation candidates. It offers a simpler modeling that makes it easier to implement. Neural machine translation no longer requires intermediate steps such as word rank, which is a key component of a system that uses word and sentence structure. While those easiness can be count as a benefit, on the other view, the absence of careful wording is a loss of accordance over translation. On the other corner, neural machine translation is more supple for translation that does not exactly match the training data. The prevalent usage of neural machine translation in translation systems has the benefit of allowing users to translate certain terms and translate untrained data to a positive extent, but in some cases often results in distorted sentence structure and boundary. This paper aims to address issues such as neural machine translation control, more precise translation of unrecognized data, correct sentence structure and boundaries.
Virtual Assistant for Querying Databases in Natural Language

This paper reports on creating virtual assistants (VA) that enable users to query a database in the natural language. Building SQL queries from the natural language is a complicated task. We build the query via a conversation between the user and the virtual assistant allowing the users to describe their needs during a more detailed conversation. The VA uses information about the schema of the data source to guide the user. The query is built incrementally. To test the proposed method, we implemented a dialogue system for querying a part of the Open Food Facts database. The evaluation results show that users successfully completed the task in most cases. The easiest task was completed by 72% of users, the most sophisticated task was completed by 58% of users. To finish the tasks, users had to provide parameters that the VA prompted for, to sort the records, and to add filtering conditions using natural language. The proposed approach allows the building of similar VAs for different databases.
Communicating Vessels Model for the Intelligent Monitoring System of the Service Guarantee in the New Generation of Digital Open Universities (NG-DOU)

The method of course exemption is improved through technology evolution. An example is given by the distance education, the electronic learning, or the mobile learning (m-learning) and the cloud learning. Using these techniques, some recent works highlight the advantages to build a New Generation of Digital Open Universities (DOUNG). This new model uses hybrid architecture based on the interconnection between Internet and GSM (Global System for Mobile). It then becomes necessary to establish the threshold of parameters that can compromise the service of course delivery alive because of the multimedia traffic constraints. This paper aims to contribute by highlighting significant parameters integrated in the knowledge base of the Intelligent Interface of Monitoring the Service Guarantee (IIMSG). From the network constraints and the lecture warehouse complexity, the parameters are set for the need of “service guarantee” mainly deriving from the synchronous access mode that brings the resource mobilization to reach a critical threshold. We use the communicating vessels model to illustrate the system operation and to identify all important parameters.
A Generic Neural Network Implementation on GPU and Its Performance Benchmark

Due to the parallel and computationally intensive nature of Artificial Neural Networks, we use GPUs to implement a generic Multilayer Perceptron (MLP) framework and compare the speed to an implementation on the CPU. The speedup achieved increases as the size of the network increases, but is also contingent on the hardware used. Three GPUs are tested, the Tesla K80, the Tesla T4, and the Tesla P100. For the largest ANNs tested, speedups ranged from 331.14 $$\times $$ for the K80 up to 2379.2 $$\times $$ on the P100.
Novelty detection in rover-based planetary surface images using autoencoders

In the domain of planetary science, novelty detection is gaining attention because of the operational opportunities it offers, including annotated data products and downlink prioritization. Using a variational autoencoder (VAE), this work improves upon state-of-the-art novelty detection performance in the context of Martian exploration by >7% (measured by the area under the receiver operating characteristic curve (ROC AUC)). Autoencoders, especially VAEs, perform well across all classes of novelties defined for Martian exploration. VAEs are shown to have high recall in the Martian context, making them particularly useful for on-ground processing. Convolutional autoencoders (CAEs), on the other hand, demonstrate high precision making them good candidates for onboard downlink prioritization. In our implementation adversarial autoencoders (AAEs) are also shown to perform on par with state-of-the-art. Dimensionality reduction is a key feature of autoencoders for novelty detection. In this study the impact of dimensionality reduction on detection quality is explored, showing that both VAEs and AAEs achieve comparable ROC AUCs to CAEs despite observably poorer (blurred) image reconstructions; this is observed both in Martian data and in lunar analogue data.