Reviewerly/exHarmony huggingface dataset:
A Study on the Professional Standards of Pre-service Teachers in Middle and Western Primary Schools in the Horizon of Competency

The research on the professional standards of pre-service teachers in elementary schools is helpful to provide references for the improvement of talent training programs in colleges and universities, and to provide specific guidance for the self-development and self-improvement of pre-service teachers.Taking the western elementary pre-service teachers as the research object, using questionnaires, interviews and mathematical modeling methods to construct the competency structure of western elementary pre-service teachers including knowledge and skills, teamwork, achievement orientation, initiative, sense of responsibility, and training Others and interpersonal understanding factors have a good degree of fit after testing.The competency structure of pre-service teachers in western elementary schools is highly consistent with the professional standards of primary school teachers.The research on the professional standards of pre-service teachers in western elementary schools includes professional concepts and teacher ethics, professional knowledge and professional abilities.
Fuzzy Logic Based Stereo Matching Method for Images with Variation in Exposure Conditions

Disparity maps generated through stereo matching algorithms possess the capacity to provide depth information, when at least two or more images of a scene taken from different viewpoints, are presented. This is a computationally complex task and the presence of radiometric differences, such as exposure variations, in the images only further complicates the stereo matching problem. The authors attempt to overcome this problem and try to extract dense disparity maps from a pair of stereo images using a combination of different data cost metrics followed by a fuzzy disparity selector. The images are preprocessed into small patches of pixels, such that pixels in each patch have similar intensities, before being subjected to the stereo matching algorithm. The effect of the number of segments and the tuning parameter ‘α’, on the various exposure conditions is studied and the performance is compared with other methods that try to tackle the problem of stereo matching under similar conditions.
Use of Big Data Analytics by Tax Authorities

This chapter provides a thematic analysis for the Australian context of the legality and challenges to the use of big data analytics to identify risk, conduct compliance action, and make decisions within the tax administration space. Recent federal court jurisprudence and research is discussed to identify common themes (i.e., privacy/opacity, inaccuracy/bias, and fairness/due process) currently influencing the legal treatment of big data analytics within the tax administration and compliance environment in Australia.
Managing Self-organized Criticality in Neural Networks on the Edge of Stability and Chaos

The features of the modes and their physical properties in the region close to the bifurcation points in systems with self-organized criticality are considered. It is shown that a mode is possible when a system with self-organized criticality operates in the instability region without leaving this region. This mode is called working on the edge of stability or chaos. It is shown that these modes near the bifurcation point can in some cases be considered as a threshold synchronization of nonlinear relaxation self-oscillating systems. It is shown that for this it is necessary that as the amplitude of the unstable mode increases, nonlinear negative feedback occurs, which will stabilize the unstable mode and turn it into a stable one. This situation occurs in most biological systems and living organisms. Examples of the work of physiological sensory systems in which the generation of powerful pulse flows occurs from very weak receptor potentials are considered. Examples of reactions in the implementation of unconditional and conditional reflexes are considered. To study the modes near the bifurcation point, a mathematical model of synchronization of relaxation self-oscillations based on the modified Wiener-Rosenblut axiomatic model and the properties of uniform almost-periodic functions is used.
QAudious: A Question Answering System for Academic Lecture Recordings

This paper puts forth a system for generating context from the media file and serving as a medium to answer questions posed by the user. The system accepts the meeting recordings and generates a textual transcript followed by preprocessing the transcript using embedding and data cleaning techniques of natural language processing. The transcript and the question are fed to the model to get the answer. The SQuAD 2.0 dataset is preprocessed for generation of a more rounded representation of the context, question, answer, span index, and mask. The pretrained models of BERT and Baseline BiDAF were implemented to generate an accurate answer for the given question. The system performance is measured through EM and F1 scores. Non-NLP methods were also executed that predicted the target sentence and not the answer, using supervised methods like fitting multinomial logistic regression, logistic regression with root match feature, random forest, and XGBoost and unsupervised methods like cosine similarity and Euclidean distance.
SGDOL: Self-evolving Generative and Discriminative Online Learning for Data Stream Classification

Data streams are usually non-stationary obtained from the same or different data sources. It needs to be processed sequentially, hence termed stream processing. Stream processing often demands evolving neural network architectures that can alter the number of nodes and layers on-demand to classify data streams in an online manner, known as evolving online learning. Traditional deep neural networks (DNNs) uses batch data processing, often limited by their static network structures and offline learning approaches while addressing data streams. In this work, we propose a novel evolving deep neural network framework, known as Self-evolving Generative and Discriminative Online Learning (SGDOL), which utilises an online learning approach to evolve both generator and discriminator network structure from scratch, and on-demand to classify data streams. The dynamic feature learning mechanism of autoencoder-based generative models have demonstrated its potential in learning latent feature representations from data streams. These latent features are fed to the evolving feed-forward DNN-based discriminator as input. The mechanism of adding or pruning nodes in the evolving architecture of discriminator supports in dealing with catastrophic forgetting problems; a new layer is added to the discriminator when a new concept appears in the data stream. To back these theoretical contributions of SGDOL, experiments were conducted using nine benchmark datasets and compared with ten state-of-the-art online learning algorithms. SGDOL performance measure of testing classification rates was better in seven datasets out of nine than the existing algorithms, which clearly indicates its ability to deal with the data stream.
Are accessible software accountable?: A commentary

Accessible software is increasingly important as the incidence of disability continues to increase and the population ages globally, ensuring people are not left behind in the digital revolution. Likewise, there is increased interest in ensuring software is accountable such that it is clear about the information it uses and the actions that it takes. While there have been some agreed-upon definitions for accessible, interpretable, and transparent software, accountable software lacks a universal definition and methodology. We argue that for a software to be accountable, it must also be accessible, interpretable, and transparent, and provide a methodology for developing accountable software.
Mining Japanese-Vietnamese multi-level parallel text corpus from Wikipedia data resource

This paper presents the task of mining a Japanese - Vietnamese parallel text corpus from comparable data resources in application of machine translation. Data resource for this language pair is few and rare so the parallel text should be extracted at multi levels, sentence level and fragment level, to get as much data as possible. Moreover, the proposed method considers word order independently so it can be applied to different language families. The result applied on Japanese- Vietnamese Wikipedia resource shows that the proposed method increases significantly the number of extracted parallel data. The extracted multi-level parallel text contributes to the quality of machine translation as well. More than 144,000 pairs of parallel sentences and 148,000 pairs of parallel fragments had been mined and opened to the research community.
Short Trainings on The Use of QR Code

A basic short training framework for using QR code with different user groups is presented. The key points of the training program with teachers and students of technical sciences are described. Some interesting editing options for users when working with this type of application are listed. Some of the results of surveys on the degree of digital competence of users to read, create and edit QR codes are given. Some professional use of QR codes and some daily activities are discussed.
Mine Escape Path Planning Algorithm Based on Simulation Model Technology

When a disaster accident occurs, choosing an escape route quickly and reasonably can not only reduce unnecessary economic losses as much as possible, but also effectively protect the lives of the people. The choice of escape route belongs to the optimal route choice problem in the route planning problem. In recent years, its importance has become more prominent, and it is regarded as a hot spot in many fields for key research. In the field of mine safety, the correct choice of the optimal path can enable underground workers to escape to a safe area in the fastest time when a disaster occurs. The purpose of this paper is to study the algorithm of mine escape route planning based on simulation model technology. After analyzing the related algorithms of path planning, the comparison and selection of algorithms, the Dijkstra algorithm is improved, and various improvement strategies are proposed for various problems existing in the mine. Finally, the improved Dijkstra algorithm is applied to the mine underground point network model during the catastrophe period for simulation experiments. The experimental results show that the improved algorithm can indeed find the optimal escape exit through a single calculation, which basically achieves the purpose of the design.
Towards Building a Flexible Online Learning Model for Data Stream Classification

In the current era, data are generated by almost every electronic devices. These data are often produced continuously from many IoT devices resulting in an enormous corpus of data streams. To learn and model such data streams, an adaptive, robust and scalable learning algorithm is needed which can handle concept drift and also able to retain previously learned knowledge. In real-world applications, labeling every data item in data streams is time and resource consuming. Therefore, in this work, we propose a self-evolving online learning algorithm that not only efficiently learn the data streams under limited labeled data conditions but also handle concept drift and retain the previously learned knowledge by evolving its network structure on-demand and from scratch. For experimental purpose, we have utilized a publicly available streaming data having i) massive and highly imbalanced network intrusion detection and, ii) binary and multi-class classification problem. Our results show the network evolution and adaption to obtain a test accuracy of above 99% for a least of 1% of labeled data.
Application of Edge Computing in Investigation and Interrogation

Under the background of the trial-centered litigation system reform, the investigation and interrogation work, as the basic link before trial in criminal proceedings, has required higher standards of evidence collection procedures. The characteristics of edge computing, such as low delay transmission, reliable sharing, data optimization and data security management, as well as its supporting moving object detection technology, video frame diversion technology and intelligent monitoring system, make it highly adaptable to the application in the field of investigation and interrogation. At the same time, the algorithm and model carried by edge computing can well calculate and control the evidence collection process. Moreover, the special method of processing video data of investigation and interrogation procedures also reduces space of data storage and the cost of examination, thus continuously advancing the trial-centered litigation system reform.
Reform and Optimization of University Archives Management Under the Background of Big Data

The development of Internet technology has led to a rapid increase in the amount of data in today’s society, and the information age has gradually shifted to the era of big data. File management is an important core task of university colleges and an important part of the management of university colleges. Since the reform and opening up, China's education has developed rapidly, the development of university education has accelerated, and the level of university archives management has further improved. However, with the changes and development of the new situation, a new round of evaluation activities for university colleges has begun. Although there are new developments in document management, there are still some problems: the quality of management personnel, the importance of work, funds and information. These factors can cause file management problems. This article uses big data to reform and optimize the file management of colleges and universities. At the same time, bibliographic research methods are used to summarize and summarize previous literature management methods. Ensure proper file management. The error rate of file storage through the method in this paper is reduced by 7%.
A Comparison Study of Abstractive and Extractive Methods for Text Summarization

The topics of identification, description, summary generation, and evaluation of the summaries produced have gained popularity over the years. Abstractive summarization and extractive summarization are two methods of generating summaries from the text. However, a comparison of different abstractive and extractive techniques has been less explored. This study's primary purpose is to evaluate the performance of abstractive and extractive models on text summarization. Abstractive models (sequence-to-sequence decoder with attention) and extractive models (TextRank, KNN, and BERT) were developed for text summarization on an Amazon fine food reviews dataset. Different comparison measures (Rouge-1 percentage, count vectorizer, tf-idf vectorizer, and soft-cosine similarity) were implemented to evaluate the text summaries produced. Results revealed that the BERT model outperformed the KNN, sequence-to-sequence decoder with attention, and TextRank algorithms during training and test on the Amazon fine food review dataset. We highlight the implication of using the extractive and abstractive models for text summarization on real-world datasets.
FHTC: Few-Shot Hierarchical Text Classification in Financial Domain

As an extensively applied task in the domain of natural language processing, text classification has moved a long way since deep learning technology develop rapidly. Especially after the pre-trained models arrived, the classification performance has been tremendous improved. However, complicated financial text often has multiple structured labels, and there are also many difficulties to have large amounts of labeled samples to ensure high-quality predictions. The existing competitive classification models can only solve one of the problems. To address these issues, we propose a hierarchical classification structure with two level. In the first level, the basic classifier is enhanced by label confusion algorithm to mine the dependency between labels and samples. In the second level, a few-shot classification model under meta-learning framework can complete the classification task based on the predictions from the previous level and a few labeled training samples. We explain our model on two large Chinese financial datasets, and find that it has superiority in both performance and computational expenditure compared to existing competitive classification model, few-sample classification model and hierarchical classification model.
Categorical Analysis of Code Smell Detection Using Machine Learning Algorithms

In software development, a code smell is some surface indication that possibly corresponds to a deeper problem in the programme. Whether a piece of code has a code smell or not is very subjective and it varies mostly by the language and developer, but also the developmental technologies. The term code smell was popularised by Kent in the late 1990s. Since then several machine learning algorithms have been employed in order to detect code smells. Code smells have been categorised into several categories. With the lack of studies done in respect to smells and their relation to their respective classes, it is imperative to look into it for a deeper understanding of smells. In this paper, we have tried to analyse and study the detection accuracies of these smells categorically for unambiguous understanding of how smells work for efficient refactoring.
Generative Adversarial Networks for Medical Image Super-resolution

Super-resolution (SR) techniques are very useful in enhancing low resolution images. This becomes even more effective when the clinicians and radiologists need to detect tiny bone fractures in some low-resolution medical images such as X-Rays. In this short paper, the application of new deep learning single-image SR techniques to medical bone X-Ray images have been investigated. The quality of the results, when applied to plain hand X-Rays, are assessed based on peak signal-to-noise ratio and mean opinion score (MOS) and the superiority of generative adversarial networks (GANs), particularly in terms of MOS, has been verified for such applications.
IoT data streams: concepts and models

This chapter introduces the main data stream characteristics, concepts, models, and processing in the context of IoT and Big Data. Further, the domain of Concept Drifts, an undesirable property of streaming data, will be described. Finally, the IoT in the context of healthcare applications and different stream scenarios will be introduced.
Sentence Rewriting with Few-Shot Learning for Document-Level Event Coreference Resolution

The existing event coreference resolution models is hard to identify the coreferent relation between non-verb-triggered event mention and verb-triggered event mention, due to their different expressions. Motivated by the recent successful application of the sentence rewriting models on information extraction and the fact that event triggers and arguments are beneficial for event coreference resolution, we employ the sentence rewriting mechanism to boost event coreference resolution. First, we rewrite the sentences containing non-verbs-triggered event mentions and convert them to verb-triggered by the fine-tuning pre-training model and few-shot learning. Then, we utilize semantic roles labeling to extract the event arguments from the original sentences with verb-triggered event mention and the rewritten sentences. Finally, we feed the event sentences, the triggers, and the arguments to BERT with a multi-head attention mechanism to resolve those coreferent events. Experimental results on both the KBP 2016 and KBP 2017 datasets show that our proposed model outperforms the state-of-the-art baseline.
Erişim İzinlerinde Güvenlik Açığı Tespiti için Windows'un Kaynak Tabanlı İzin Mekanizmasına Tersine Bir Yaklaşım

In organizations, employees work with information stored in files according to their duties and responsibilities. Windows uses resource-based access permissions that any permission for any user has to be set separately per resource. This approach gets complicated as the number of resources and users increase, and causes oversights in assigning permissions. Therefore, a special mechanism is required to scrutinize what permissions any employee has on any set of resources. This requirement is circumvented by reversing the Windows’ approach in terms of user-accessible resources. This approach is implemented by a program allowing quick and easy examination of any type of permissions granted or denied to active directory users on any folder. In this way, administrators can make sure there is no any missing or overlooked setting that could cause a security vulnerability. This approach can easily be extended to scrutinize other resources, and for other local or active directory objects.
Research on the Security of Beyond-the-Sight Services Based on Blockchain

In 2008, blockchain was proposed as the basic technology of Bitcoin, and it is essentially a decentralized database. This technology is widely used in financial industry, data storage, edge computing and other scenarios. With the rapid increase of the number of devices connected to the Internet of Things (IoT), the traditional centralized cloud computing system is unable to satisfy the Quality of Service (QoS) of many IoT applications, especially for areas with real-time. IoT services based on Mobile Edge Computing (MEC) are gradually becoming popular, and the characteristics of the distributed accounting and consensus mechanism of the blockchain can ensure the security of MEC-based IoT services. In this paper, we first summarize the existing blockchain and MEC related technologies, and then propose a future-oriented Internet of Vehicles (IoV) service: beyond-the-sight service. At last, we specifically discuss how the blockchain can ensure the security of beyond-the-sight service in terms of terminal equipment identification and data transmission.
Learning strategies used by EFL learners for print and digital reading: Critical review

Equipping English foreign language (EFL) learners with adequate learning strategies to master reading skills while reading print or digital material is essential to progress with their academic studies in higher education. Effective use of strategies while reading various online texts on the computer and smartphone screens significantly affects their reading comprehension, influencing their higher education studies. Whether EFL students transfer their learning strategies for print material to comprehend digital reading material is an important process that requires in-depth analysis. Most importantly, this analysis explores the different strategies used by EFL learners while reading online and printed texts in several EFL learning settings. Along with strategies used for reading printed texts, SL/FL readers tended to use other strategies for reading online texts, such as navigational strategies and the use of the cursor to follow sentences on the screen. Most of the different strategies employed for reading online texts were influenced by the features of the online text and the task assigned to the readers. The paper ended with some directions for further research to investigate reading strategies in various text formats and language learning settings.