Reviewerly/exHarmony huggingface dataset:
Pupils’ Written Language in the Latvian Language and History State Examinations in Riga in 2021

The research examines pupils’ text creation skills in the Latvian language and history state examinations in 2021. It compares the quality of written language in two examination papers by 15 pupils. The statistical method has been used to evaluate the types of errors in orthography and syntax and establish the frequency of the use of the language means. Pupils’ skills in orthography vary. Only 4 examination papers in Latvian and history do not contain orthographic errors. Writing complex proper names and the use of macrons cause problems for pupils. Similarly, the skills of separate spelling of some words have been underdeveloped. Also, an unjustified lack of a letter or unjustified use of it can be observed. Pupils pay more attention to orthography in the Latvian language examination. Pupils’ skills in syntax are also varied. Syntactical means used in text creation are uniform. In the Latvian language examination papers, 143 instances when coordinated parts of sentence were used have been registered; 78 such instances have been registered in the history examination. In both examinations, a connection of two coordinated parts with the conjunction un ‘and’ was used most frequently, with 78 cases in Latvian and 32 in history. Also, the participial clause, including the undeclinable participle with the suffix -ot and auslaut -oties, was dominant (60% in history, 40% in Latvian). In the third part of the Latvian language examination, insertions were used more often than in the history examination, 63% and 37%, respectively. Other syntactic means were rarely used. It can be concluded that there are no significant differences in the types of orthographic and punctuation errors in the Latvian language and history examination papers; the differences are visible in the choice of language means and their quantity.
Conceptualising Digital Competency for ICT Specialists

A competency-based approach to education has become central in discussing and assessing the quality of education. Competency is vital for any individual to cope successfully with challenging tasks in his/her professional, social and private life and achieve the goals set. In order to practically implement the approach in higher education institutions, it is necessary to understand what competencies are required by the labour market and what their structure is, as well as to select or develop models or frameworks for describing these competencies. One of the most relevant competencies in the modern world is digital competence which implies knowledge and skills to use information and communication technology (ICT) to achieve professional, social and personal goals. Although the need for developing digital competence is actual for specialists in any field, the definition of its structure for ICT professionals is mainly ignored. Therefore, it can hinder the development of quality educational programs and prevent graduates from being equipped with the necessary skills and knowledge. This study applies a systematic literature review to answer three research questions concerning the types and structure of digital competence of ICT professionals, the developed models and frameworks of digital and professional competence of ICT specialists, and the representation of digital competence in the existing competence frameworks and models. The research results offer the conceptualisation of digital competence for ICT specialists. It includes transversal digital competence, general professional digital competence, and specific professional digital competence, together with indicating an appropriate framework for each of the competence types mentioned.
Simulator Laras Wicara Learning Media Needs According to The Perceptions of The Students of The Indonesian Language and Literature Education Program of Universitas Tidar

The research tries to study the lack of learning media that supports and improves the speaking skills of the students of the Indonesian Language and Literature Study Program (PBSI), Faculty of Teacher Training and Educational Sciences, Universitas Tidar, in the Speaking course. During this time, the
A Multi-level Optimized Strategy for Imbalanced Data Classification Based on SMOTE and AdaBoost

Many applications require effective classification of imbalanced data, which is found everywhere. Existing classification algorithms often misclassify the minority class in imbalanced data due to the dominant class’s influence. Boosting algorithms combine basic learners to improve their performance. AdaBoost, a popular ensemble learning system, can classify general datasets well. But this algorithm will be limited misclassified samples only. The minority-classified samples are not fit for this algorithm and as it alone not readies for imbalanced data classification. This paper introduced multi-level strategy to solve imbalanced data, where combined SMOTE with AdaBoost to process unbalanced data. AdaBoost and SMOTE optimize synthetic samples, implicitly modifying update weights and adjusting for skewed distributions. The typical AdaBoost technique uses too many system resources to prevent redundant or useless weak classifiers. To make process simple applied Adaptive PSO (APSO) to the SMOTE_AdaBoost results re-initialize of strategy to the optimize AdaBoost weak classifier coefficients. Four real imbalanced datasets on six classifiers—Naïve Bayes (NB), Random Forest (RF), Multi-layer Perception (MLP), Decision Tree (DT), and K-Nearest Neighbor (KNN)—verify the proposed multi-level strategy. The proposed strategy (APSO_SMOTE_AdaBoost) is applied to six classifiers’ and compared to SMOTE-PSO. The multi-level proposed strategy outperforms with standard approach in accuracy, precision, recall, sensitivity, and F-score.
Artificial Intelligence and Academic Journals: For Better and for Worse

This editorial summarizes a talk presented during the 47th EnANPAD Annual Meeting in São Paulo, Brazil, exploring the role of artificial intelligence (AI) in academic journals. It highlights the impact perspectives of AI, recognizing its effects in various social and academic areas. Although the effects are extensive and their full ramifications are unknown and uncertain, the editorial highlights elements of opportunities and threats of this impact, as well as some ways to mitigate the impact. Journals could become smarter and more accessible with AI, providing a personalized experience for researchers and readers. However, significant challenges, such as the risk of inequality between journals with and without AI capabilities, are also highlighted. To seize opportunities and mitigate challenges, the author advocates for investment in AI, the creation of clear editorial guidelines, and an institutional commitment to responsibility and ethics.
A Sequential News Capture and Summarization Model (SNCSM)

Automatically extracting specific information of interest from daily news articles is a crucial and critical challenge. Despite the abundance of work on domain-specific news summarization, most existing methods do not selectively capture and summarize important news only. To bridge this gap, we propose a Sequential News Capture and Summarization Model (SNCSM) that can automate the process of capturing domain-specific important news in addition to the task of news summarization. Carried out in two phases, this study developed SNCSM and tested the model accuracy using a weighted accuracy technique on a Civil Services Examination (CSE) dataset. Phase-I dealt with the construction of dataset and Phase-II involved the model development and model accuracy testing. CSE syllabus, previous years QnA, and news articles were web-scraped from the Internet. Tokenization of scraped data using spaCy, data pre-processing, feature engineering, and clustering were performed to construct the dataset. Word-similarity and ML models were implemented to capture and predict the importance (weight) of tokens, respectively, hence news articles. RMSE and R2 score along with other two metrics were used to evaluate model performances. MLP regression resulted the least RMSE of 0.311 and highest R2 score of 0.537. However, average token weight predicted by the models on the Prediction Day (P-Day, 01.01.2022) dataset were used for the calculation of importance of news articles. Highly weighted P-Day ( $$\le $$ $$\mu $$ + 0.25 $$\times \sigma $$ ) news articles ( 25%) were summarized using the BERT Extractive Summarizer (BERT-ES) with the mean ROUGE-L F1-score of 0.475. The SNCSM achieved a weighted accuracy, linear combination of R2 score and ROUGE-L F1-score, of 0.566. However, further study is needed to enhance the model performance.
Effective Image Captioning Using Multi-layer LSTM with Attention Mechanism

Image captioning has become one of the more pressing problems in machine learning with a lot of headway being made in generating descriptive image captions in the English language. This is due to the ease of availability of the English dataset but that should not restrict any work in native languages; therefore to overcome this problem, we have used the Flickr8K dataset with Google Cloud Translator as done in Ankit Rathi et al. to obtain Hindi captions. In this study, we proposed an image captioning model that utilizes InceptionV3 as a feature extractor and a stacked LSTM model with Bahdanau attention to generate captions for given images. The performance of the proposed model was evaluated on the Flickr8k Hindi dataset using the BLEU metric. The results showed a BLEU-1 score of 0.64294, BLEU-2 score of 0.480917, BLEU-3 score of 0.36554, and a BLEU-4 score of 0.2191, which is considered as the most significant score.
Image Restoration Using ResNet–VGG Autoencoder Model

Image degradation is a problem that frequently arises in computer vision. Several types of degradation, such as noise, blur, and haze, can significantly impact the image’s quality and make it challenging to analyze. For example, haze, where light is scattered by the environment and lowers contrast and color saturation, is a prevalent issue with outdoor photos. Dehazing techniques have been created to solve this issue, to remove the haze from photos and regain their original quality. Conventional dehazing techniques frequently rely on improvised elements and presumptions regarding the scene, which might restrict their efficacy. Convolutional neural networks (CNNs) are trained on big datasets, and deep learning-based techniques have recently demonstrated promising results in dehazing to understand the fundamental connections between unclear and clear visuals. Dehazing algorithms can perform better since they can automatically pick up more complicated features and adapt to various scenarios when employing CNNs. This abstract emphasizes the need for deep learning-based dehazing in this situation since it provides a more reliable and accurate means of restoring damaged photos than more conventional ones. Moreover, deep learning-based dehazing algorithms can be used in a variety of applications, including autonomous driving, surveillance, and remote sensing, where the accuracy of analyses and decisions depends on the quality of the images.
Selection of Robust Text-Based CAPTCHA Using TensorFlow Object Detection Method

Nowadays, websites are facing multiple security issues. Today, web pages are protected by specified security alerts to overcome these issues, in that CAPTCHA security is still in use. The logic behind designing the robust text-based CAPTCHA is 'an increase in the confusion rate indicates the high quality of the CAPTCHA.' By studying the cracking techniques, the CAPTCHA developer can revise the design of text-based CAPTCHA. So that the cracking rate can be minimized and the confusion rate can be maximized. Open-source text-based CAPTCHAs are used for launching the chosen plain text attack. The choice of open-source CAPTCHA library in generating the unlimited CAPTCHA dataset can reduce the cost. To select the robust text-based CAPTCHA from the open-source CAPTCHA library, a framework is developed with TensorFlow object detection and convolutional neural network. The proposed method is tested using 40 numeric and 12 alphanumeric CAPTCHA along with special character.
Application of inferences in ontological modeling

Abstract Data modeling supported by ontologies favors the organization of knowledge and the use of inference machines to obtain new knowledge about data and support expert decision-making. However, it is necessary to discuss the techniques to promote inferences and explore the semantics between the data. The research problem is to verify how the use of semantic technologies would represent an advantageous alternative, in relation to the state of the art, to analyze, systematize and organize the information of a domain, accelerating a decision-making process. It presents the concepts behind the development of an artifact that employs semantic technologies and a domain ontology to integrate data from Building Information Modeling projects and other tabular data external to the project to automate Leadership in Energy certification tasks and Environmental Design. To demonstrate the artifact, data is semantically annotated and a knowledge graph is generated in Resource Description Framework format. Once integrated into the graph, inferences are applied to evaluate the certification criteria through queries. The results suggest that the application of these technologies promotes semantic extension, facilitates integration with other knowledge bases, and conceptually organizes data to better retrieve information and generate new knowledge.
Automatic Test Paper Generation Technology for Mandarin Based on Hilbert Huang Algorithm

With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%.
Implementing Cloud Security through AWS for Blood Bank Application

In today’s time the technologies are seeing a huge shift of work from the on-center to over the internet, which means that a huge number of online services is required, these services could be backup and the data recovery should be available over the internet. Cloud computing is the platform over the internet which provide the services of networking, sharing, storing, etc. on the internet. Not only this shift of data is being ob-served in IT sectors, but also this shift is being observed in the healthcare sector. With the increase in the average age limit of the human beings and new diseases increasing the medical needs. The traditional healthcare is now being replaced by modern and more progressive healthcare. Thus, cloud is providing with several healthcare solutions. How-ever, even after have a lot of benefits of using cloud services it also has some of the risks. The major risk being of the security of the patient’s data. This paper analyses the security issues and their possible countermeasures for the same. The security measures presented in the paper are based on patient data in relation to data storing, access and security of data. In the paper I have worked towards the development of the android application of blood bank, the application uses the Android studio as IDE and the AWS Amplify as the cloud service to host and deploy the application over the cloud. In this paper we will also see the benefits of using the cloud computing, its security and also what are the future works that need to be done in the field.
Can Authorship Representation Learning Capture Stylistic Features?

Abstract Automatically disentangling an author’s style from the content of their writing is a longstanding and possibly insurmountable problem in computational linguistics. At the same time, the availability of large text corpora furnished with author labels has recently enabled learning authorship representations in a purely data-driven manner for authorship attribution, a task that ostensibly depends to a greater extent on encoding writing style than encoding content. However, success on this surrogate task does not ensure that such representations capture writing style since authorship could also be correlated with other latent variables, such as topic. In an effort to better understand the nature of the information these representations convey, and specifically to validate the hypothesis that they chiefly encode writing style, we systematically probe these representations through a series of targeted experiments. The results of these experiments suggest that representations learned for the surrogate authorship prediction task are indeed sensitive to writing style. As a consequence, authorship representations may be expected to be robust to certain kinds of data shift, such as topic drift over time. Additionally, our findings may open the door to downstream applications that require stylistic representations, such as style transfer.
<scp>Multi</scp> 3 <scp>WOZ</scp>: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems

Abstract Creating high-quality annotated data for task-oriented dialog (ToD) is known to be notoriously difficult, and the challenges are amplified when the goal is to create equitable, culturally adapted, and large-scale ToD datasets for multiple languages. Therefore, the current datasets are still very scarce and suffer from limitations such as translation-based non-native dialogs with translation artefacts, small scale, or lack of cultural adaptation, among others. In this work, we first take stock of the current landscape of multilingual ToD datasets, offering a systematic overview of their properties and limitations. Aiming to reduce all the detected limitations, we then introduce Multi3WOZ, a novel multilingual, multi-domain, multi-parallel ToD dataset. It is large-scale and offers culturally adapted dialogs in 4 languages to enable training and evaluation of multilingual and cross-lingual ToD systems. We describe a complex bottom–up data collection process that yielded the final dataset, and offer the first sets of baseline scores across different ToD-related tasks for future reference, also highlighting its challenging nature.
The Process and Algorithm Analysis of Text Mining System Based on Artificial Intelligence

The rapid development of the Internet leads to the rapid growth of network information, we call it information explosion. The Internet is full of information, and it is difficult for users to find this information and useful knowledge of the ocean. The Web has become the world's largest information repository, and there is an urgent need for efficient access to the valuable knowledge of vast amounts of web information. The purpose of this paper is to study the process and algorithm analysis of text mining system based on artificial intelligence. This paper presents an algorithm of document feature acquisition based on genetic algorithm. Selecting suitable features is an important task in specific text classification and information retrieval. Finding appropriate feature vectors to represent the text will undoubtedly help with subsequent sorting and grouping. Based on the genetic algorithm of variable length chromosome, this paper improves the crossover, mutation and selection operations, and proposes an algorithm to obtain text feature vectors. This method has a wide range of applications and good results.
Research on Traffic sign recognition based on CNN Deep Learning Network

With the continuous development of social economy, the road traffic system as one of the basis of national economy is also increasingly developed, which is followed by the traffic safety problems are increasingly prominent. Among them, driving violations such as not obeying traffic signs are the main cause of traffic accidents. In this research, Traffic Sign Recognition (TSR) has become a hot research topic. The research status and application field of traffic sign detection and recognition technology are introduced and analyzed in detail, and the improvement of traffic sign database is introduced. After that, the application of CNN in image processing and traffic sign recognition is introduced, and the existing problems in traffic sign recognition are analyzed. The CNN deep learning network used in this paper can conduct training while identifying targets, effectively improve the recognition accuracy of traffic signs under complex natural conditions and reduce the time consumption.
Quantization Method Integrated with Progressive Quantization and Distillation Learning

This paper proposed a quantization method based on the integration of progressive quantization and distillation learning, aiming to address the shortcomings of traditional quantization methods in maintaining model accuracy while reducing model size. This method converts the weight from floating point number to smaller integer number through progressive quantization, thus reducing the storage space required by the model. At the same time, distillation learning technology is used to integrate the gradually quantified model with the original floating-point model to improve the accuracy of the model. The experimental results show that the proposed method can maintain high accuracy of the model while reducing its size, and has better performance compared to traditional quantization methods. This method has a broad application prospect in model compression, and can be widely used in scenarios such as Edge device and cloud servers.
Construction of Supply Chain Security Management Model for Information and Communication Technology Based on the Internet of Things and Cloud Computing

At the level of enterprise supply chain security management, with the rise of network technology big data technology methods such as the Internet of Things (IoT) and cloud computing, it has had a multi-level impact on its supply chain management mode, including operation mode, operation mode, and security management mode. The traditional supply chain management model is based on old-fashioned communication methods and manual intervention for judgment and processing. Information and communication technology has advantages such as immediacy and strong correlation. Based on this, this article applies the data interconnection technology of the Internet of Things to the construction of supply chain management models and combines it with efficient algorithms of cloud computing. This article also explores the construction of supply chain security management models from the perspectives of participants, activity modes, module operations, and conducts data comparisons based on them. The experimental results show that in the running tests of the model, its average accuracy is about 89.60%, and the prediction accuracy of benefits is as high as 98.48%. In stability testing, it is also evident that the average stability of the model after multiple iterations is about 96.02%. Research has shown that the Internet of Things and cloud computing technology have great application potential in the construction of supply chain security management, which can provide enterprises with more effective supply chain security management tools.