Reviewerly/exHarmony huggingface dataset:
THE RENEWAL OF GENERAL CURRICULUM FRAMEWORK: WHERE ARE WE MOVING TOWARDS?

The need to update the curriculum and the Curriculum framework of general education has been discussed for several years, as long as a year ago, the renewal work of primary and basic education curriculum began, and this year the renewal of the secondary and preschool education curriculum started. The curriculum of the general preschool education is described for one year, with the possibility for the child to study for the second year. The preschool education programme is not structured in separate subjects – the knowledge is acquired integrally, by developing seven competencies in all activities in which the child participates, in order to ensure the balance and coherence of all competencies in different content areas. Excluded content areas are given equal volume. It is being focused on the purpose and goals of preschool education, areas of achievement and intended achievements. The learning content is divided into five areas of learning content: • language education; • social education; • STEM education(natural science, mathematics and informatics, technological education); • health and physical education; • artistic education. The general curricula for pre-primary, primary, basic and secondary education must ensure vertical and horizontal coherence of content, possible interdisciplinary links, and indicate how they can be meaningfully disclosed including the interdisciplinary topics set out in the General curriculum framework updating guidelines: • personal power, • cultural identity and community spirit, • sustainable development. The volumes of primary, basic and secondary education curricula are presented distinguishing between compulsory subject content (about 70 per cent) and optional content (about 30 per cent), which is selected and modelled by the teacher himself, taking into account school, classroom context, student needs, achievements, possibilities and in coordination with other teachers as required. When implementing optional content, more time is devoted to the development of certain skills, values, and learning of subject topics. Project works can be organised, interdisciplinary themes can be developed, time can be allocated for cognitive, cultural, artistic, and creative activities. According to the concept of the Guidelines for the Renewal of the General Curriculum Framework, this distribution of the content ratio is focused on deeper learning, integration, topic development, competence development. Keywords: curriculum framework, general education, interdisciplinary topics, project works.
Extensive Study on Color and Light Translation of 2D Images using Machine Learning Approaches

For machine learning applications, digital image production provides for the efficient generation of huge volumes of training data while preserving control over the generation process to ensure the optimal content distribution and variation. Synthetic data has the potential to become an important element of the training pipeline as the demand for deep learning applications grows. Over the last decade, a broad range of strategies for producing training data have been presented. The collecting of these for comparison and categorization is required for future improvement. This study presents a complete list of available visual machine learning image synthesis approaches. In the context of 2D picture production, these are classed as light transfer and colour transfer. The focus is on the computational features of approaches for developing machine learning colour transfer between image-to-image translation in the future. Finally, the learning potential of each approach is assessed based on its reported quality and performance. The study is meant to serve as a complete reference for both data and application developers. This is a comprehensive list of all the methods and approaches discussed in this page.
Super RaSE: Super Random Subspace Ensemble Classification

We propose a new ensemble classification algorithm, named super random subspace ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the random subspace ensemble algorithm (RaSE). The RaSE method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success of RaSE largely depends on the proper choice of the base classifier, which is unfortunately unknown to us. In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. As a result, Super RaSE is more flexible and robust than RaSE. In addition to the vanilla Super RaSE, we also develop the iterative Super RaSE, which adaptively changes the base classifier distribution as well as the subspace distribution. We show that the Super RaSE algorithm and its iterative version perform competitively for a wide range of simulated data sets and two real data examples. The new Super RaSE algorithm and its iterative version are implemented in a new version of the R package RaSEn.
Key-Point Based Distribution Probability Optimization for Object Detection in Remote Sensing Images

Recently, object detection in remote sensing images has been an important topic in computer vision. As a mainstream approach to detectors, single-stage detectors usually compute the important points of the bounding box to get the location of the target, and optimize them by Focal Loss. One of the development trends for one-stage detectors is to combine with Localization Quality Estimation (LQE), which can provide accurate ranking scores, and thus bring benefits to the Non-Maximum Suppression as well as the following detection results. In this paper, we propose a one-stage detector, named Key-point based Distribution Probability Optimization (KDPO), for remote sensing object detection. Specially, we first classify and locate the object through five key points. Then, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification. Next, we utilize a vector to represent arbitrary distribution of box locations. The improved representations avoid the inconsistency risk and accurately describe the flexible distribution of the data. Meanwhile, we use the improved Focal Loss for the succeed optimization. The proposed method is tested on two popular aerial image datasets, including HRSC2016 and DOTA. In all experiments, our approach achieves promising performance comparing to the state-of-the-art detectors.
Smart Contract Vulnerability Detection Based on Symbolic Execution Technology

Abstract With the rapid development of the blockchain, smart contract technology has been widely applied. The number of smart contracts has grown at a high rate and nearly at an average of thousands per day. However, the correctness and security of the smart contract itself are facing huge problems. The well-known DAO vulnerability, and Parity multi-signature wallet’ vulnerabilities have leaded to a hundreds of millions dollars loss, and they are both caused by the security problems of smart contracts. Once the smart contract vulnerability is exploited, it is very likely to bring the loss of cryptocurrencies, the disorder of the financial order and other catastrophic consequences. Therefore the security of smart contracts is imminent. This project has designed and implemented a vulnerability detection system of Ethereum smart contract. The system uses the assembly instruction sequences of the smart contract to generate the control flow graph, then performs symbolic execution and vulnerability constraint solving over the control flow. The system can detect some common types of vulnerabilities, such as the integer overflow and underflow vulnerability, reentry vulnerability and unchecked call return value vulnerability. It has a high accuracy of detection result, and gives support for export vulnerability report.
A Security Framework for Railway System Deployments

Railway systems are critical transportation infrastructure assets that must be protected from cyber attacks. However, deployments and upgrades of operational technology systems are always challenging due to the short timeframes available for maintenance. Specifically, there is insufficient time to test the safety and robustness of software updates and patches during railway system operation. Cyber security guidelines have been specified for the railway sector. However, the guidelines only mention the security requirements, not how they should be implemented in railway systems. This chapter proposes a security framework for railway system deployments. The framework can also be used as a reference for cyber security testing.
Detection of Undeserved Sick Leaves in Hospitals using Machine Learning Techniques

Artificial intelligence and Machine Learning are nowadays playing an important role in improving medical services. One of the services which needs the dedication of such techniques is the attribution of sick leaves. This need is raised by the observed abuse of the service. Undeserved sick-leaves can be obtained by employees and students from hospitals, either by pretending sickness or by exploiting connections with medical staff or physicians. In this paper, undeserved short sick-leaves detection problem is investigated under data imbalance. A highly skewed real dataset where 93 % of the records are deserved sick leaves and only 7% are undeserved sick leaves is used. Classification techniques namely Naïve Bayes (NB), Logistic Regression (LR) and K-Nearest Neighbor (K-NN) are built, tested, and compared. Also, Random under-sampling technique is utilized for the remedy of data imbalance. That is, four proportions of the dataset with different ratios among the classes (deserved Vs undeserved) have been created. Each classification technique is evaluated under each of the sampled data proportions considering a set of measures such as accuracy, specificity, and Area Under-Curve (AUC). The best performance on the original data is shown by LR classifier (accuracy = 97 %, specificity = 76 % and AUC = 87 %), followed by NB than K-NN. However, on the sampled data, NB outperformed both LR and K-NN with an accuracy up to 90 %, specificity up to 94 % and AUC up to 88 %. Also, it has been proven that the best data sampling ratio is 34 % for deserved sick leaves and 66 % for undeserved sick leaves.
A specialized framework of web application for efficient data retrieval on social media tools

The main motive of this paper is to describe the unique approach of service-oriented web tool development “Social listening”. Which helps to read the real customer thoughts, opinions, complaints, feedback, comments and issues of a particular product on social networks through this paper. The monitoring tools, architecture, design, functional requirements, nonfunctional requirements, and the quality attributes of the system are explained. This entire paper advocate's organized architecture structure which is involved in this web application tool is to mutate high level business information to architecture that implements the customer's voice of goods produced by the companies with the web application tool. However, the architecture shows how data extraction will be much easier, regarding certain products/services from multiple sources like Twitter, Facebook. And analyze the reviews of the customers and convert the result into a graph. The present modeling used in transformation techniques is analyzed, and then a flow-based approach of services is introduced through case studies with an esteemed business description.
Data Abstraction

So far you’ve been writing small programs. Imagine for a moment that—perhaps 10 years from now—you’re a member of a team building the software for an autonomous robotaxi. At that point, you’ll be working on much larger programs in collaboration with other team members.
The Text-Dependent Chinese Speaker Recognition System Based on the Universal Individual Identification

In the past few decades, with the increase and popularization of smart devices, the security problem of device certification has been derived. Biometric authentication is becoming more and more popular, and contactless speaker recognition is one of its elegant forms. For the research of speaker recognition, most researchers are training based on a large amount of data. But in real life, for the use of a product, it is difficult to obtain a large amount of user data in a short time, so the setting of this user data is very important. Therefore, the purpose of the paper is to find the universal individual utterances in the Chinese system. We created a dataset of 30 speakers about the Chinese Pinyin table in the paper. The focus of this research is the use of initials and finals in Chinese Pinyin. According to the experimental results, the use of certain initials and finals in Chinese Pinyin helps to effectively determine the identity of the speaker. In the paper, we successfully found Chinese disyllable and trisyllable characters that can effectively identify the speaker. These Chinese characters can be used to develop text for any speaker-based authentication system.
Multi-Task Learning with Personalized Transformer for Review Recommendation

Drastic increase in item/product review volume has caused serious information overload. Traditionally, product reviews are exhibited in chronological or popularity order without personalization. The review recommendation model provides users with attractive reviews and efficient consumption experience, allowing users to grasp the characteristics of items in seconds. However, the sparsity of interactions between users and reviews appears to be a major challenge. And the multi-relationship context, especially potential semantic feature in reviews, is not fully exploited. To address these problems, Multi-Task Learning model incorporating Personalized Transformer (MTL-PT) is proposed to provide users with an interesting review list. It contains three tasks: the main task models user’s preference to reviews with the proposed poly aggregator, incorporating the user-item-aware semantic feature. Two auxiliary tasks model the quality of reviews, and user-item interactions, respectively. These tasks collaboratively learn the multi-relationship among user, item, and review. The shared latent features of user/item link the three tasks together. Especially, the personalized semantic features of reviews are also fused into the tasks with the proposed personalized transformer. Two new real-world datasets for personalized review recommendation are collected and constructed. Extensive experiments are conducted on them. Compared with the state-of-the-arts, the results validate the effectiveness of our model for review recommendation.
The Evaluation of Teaching Effect based on Interpretable Machine Learning

The Evaluation of Teaching Effect is an effective method to ensure the quality of classroom teaching, which is conducive to the control and improvement of teaching quality. There are various methods of teaching evaluation. Nowadays, with the continuous development of artificial intelligence technology and big data technology, machine learning algorithm has been applied to evaluate teaching effect, which improves the efficiency and level of evaluation. However, the working principle of machine learning algorithm is still unexplained. Therefore, this paper proposes a method of the Evaluation of teaching effect based on machine learning interpretability, which studies the overall change of teaching effect when an evaluation index changes, and considers the correlation of each evaluation index, so as to study the importance of each evaluation index and promote the improvement of the final teaching effect.
External Sparsity Combined with Internal Low-Rankness for Image Denoising

Nonlocal self-similarity (NSS) as a remarkable prior has been successfully applied to image processing including image denoising. For each local patch in an image, we can search for many nonlocally similar patches under NSS prior and stack them into a group, and then process the group instead of a patch to better capture the nonlocal structure. Most existing methods consider the NSS either of the internal degraded image or from an external clean corpus, which may limit the denoising performance. In this paper, we propose a hybrid model to catch both the external and internal NSS. Specifically, we develop a model with two regularizers, the internal NSS is exploited by a low-rankness regularizer, and the external NSS is exploited by a sparse regularizer. An alternating minimization method is developed to solve our model. Experimental results demonstrate that our algorithm achieves better results compared with several state-of-the-art denoising methods.
Lens Library to Facilitate Composing of Virtual Scene for Optic Experiments

While the mathematical modeling of optical phenomena, a computer calculation is often performed, confirming the conclusions made. To do this, a virtual computer model of the optical installation is created in the form of a 3D scene. Also, virtual scenes are often used in training when creating presentations. This paper describes the SphL library, which provides a convenient assignment of spherical lenses and the calculation of the interaction of linear polarized light rays with them. It is focused on applications that use ray tracing. It is known that light of any polarization can be represented on the basis of the mentioned one. The reflected and all rays passing through the lens that arise due to internal reflections are calculated from the ray incident on the scene object. The number of internal reflections is set by the parameter. All output rays are calculated based on the application of Fresnel’s equations and are characterized by intensity values and polarization parameters. In this version of SphL, the main objects at the end–user level are spherical lenses, since they are most often used in optic installations. They are constructed on the basis of the application of the set-theoretic intersection of geometric primitives: a half-space, a sphere, a cone, a cylinder and their complements to the scene space. An advanced user can build their own objects by analogy, for example, cylindrical lenses.
Review on human speech Recognition Techniques

Biometric technology such as gait recognition can be used to monitor people without their knowledge. Banks, military institutions, and even airports must be able to detect risks rapidly and grant varied levels of access to different user groups. Gait recognition is the process of identifying an individual based on their walking style. And gait is a less invasive biometric that can identify people at a distance, without the subject's engagement or cooperation. In this article an effort has been made to review articles on Analysing human speech using gait recognition technology and related technologies.
Fake Review Detection Using Sentiment Analysis and Deep Learning

When it comes to determining whether or not to buy a product, reviews are very crucial. These review sites are continually bombarded with inaccurate information, which may be perpetrated by individuals or groups who fabricate fake reviews in order to promote or demonize specific goods. This research work concentrates on the identification of fake reviews or feedbacks using sentiment analysis and natural language processing. To transform the data into a format appropriate for analysis and detection, a pre-processing technique is used. The proposed method involves analyzing review feedbacks with deep learning neural networks like the Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi-LSTM) & Long Short Term Memory (LSTM), and evaluating the results using activation functions like ReLu, TanH, and Sigmoid.
Knowledge graph based platform of COVID-19 drugs and symptoms

Since the first cased of COVID-19 was identified in December 2019, a plethora of different drugs have been tested for COVID-19 treatment, making it a daunting task to keep track of the rapid growth of COVID-19 research landscape. Using the existing scientific literature search systems to develop a deeper understanding of COVID-19 related clinical experiments and results turns to be increasingly complicated. In this paper, we build a named entity recognition-based framework to extract information accurately and generate knowledge graph efficiently from a myriad of clinical test results articles. Of the tested drugs to treat COVID-19, we also develop a question answering system answers to medical questions regarding COVID-19 related symptoms using Wikipedia articles. We combine the state-of-the-art question answering model - Bidirectional Encoder Representations from Transformers (BERT), with Knowledge Graph to answer patients' questions about treatment options for their symptoms. This generated knowledge graph is user-friendly with intuitive and convenient tools to find the supporting and/or contradictory references of certain drugs with properties such as side effects, target population, etc. The trained question answering platform provides a straightforward and error-tolerant way to query for treatment suggestions given uses' input symptoms.
Determinants of Compliance With Information Systems Security Controls

The purpose of this study is to determine the factors that critically contribute to employees of an organization complying with the information security controls that have been set in place. Whether it is an attitude or an intention that leads to complying, this chapter looks at the theory of planned behavior (TPB). A company was chosen, and a questionnaire was handed out to the employees. The questionnaire distributed focuses only on the elements that are within the theory of planned behavior model. The questionnaire is anonymous, and because of this, the confidence and confidentiality are given. As a result of these two factors, it was seen that the elements of the chosen model are not 100% the contributing factor as to why employees comply with information security controls within Company X. It is therefore implied that more research has to be done to figure out what exactly makes the employees comply.
An Explainable Educational Resource Recommendation Model Based on Matrix Factorization

The recommendation algorithm based on hidden variables are widely used in educational resource recommendation systems. However, such algorithms and their recommendation results lack explainability, which affects the application effect of recommendation. Therefore, we propose an explainable educational resource recommendation (EERR) model to solve this problem. The model is constructed by three steps. To begin with, we extract explainable features from educational resource manually. Then, the recessive feature is correlated with explicit feature by using of matrix decomposition. Finally, the alternating least square algorithm is used to obtain the recommended results. Experiment results show that the proposed model has better performance under the RMSE evaluation criteria, and it can improve users' trust in the recommendation system.
RETRACTED CHAPTER: Software Effort Estimation of Teacher Engagement Application

This study tries to develop an application that contains an independent self-diagnostic instrument that is used to view the profile of teacher involvement in Indonesia which is called the Indonesian Teacher Engagement Index. We measured the estimated effort of developing a neural network-based ITEI application. The system development is carried out so that the ITEI application can run more dynamically. The method to measure the software is use case point (UCP) approach. In estimating productivity of software projects, the use case point method is used to determine the software size to use case model used. The steps taken in measuring the effort estimation are calculation of unadjusted use case point, then calculation of use case point and calculation of effective effort. The results showed that project has small software size with UCP value = 52.9592, and the effective effort measurement value obtained was 1,059.184 h.