Reviewerly/exHarmony huggingface dataset:
Construction of the Assessment Model of the Information Security System of the Enterprise

Within this paper, the integrated quality assessment model of the information security system of the enterprise is proposed, based on which it is possible to obtain quantitative characteristics of the information protection system and, accordingly, access its adequacy and effectiveness. Assessing the quality of the information security system is a rather complex organizational and technological task, the process of which crystallizes an evaluative judgment regarding the suitability, adequacy, and expediency of the applied information security measures to ensure a sufficient level of IS. The purpose of the evaluation of the company's information security system is to create information prerequisites for improving information security. At the moment, there is no single approach to the assessment of the quality of ISS. Both qualitative and quantitative methods are used to assess the effectiveness of ISS. In our opinion, quantitative methods are more objective and informative, however, for their use, it is necessary to develop a certain set of evaluation criteria. In the work, general principles of the construction of the quality assessment model of the information security system is analyzed, and based on it, a corresponding model was built that combines the principles of the risk-oriented assessment and the assessment oriented on economic indicators. The proposed assessment allows, depending on the customer’s needs of the assessment, to emphasize the protection reliability or, accordingly, economic indicators.
Spatial planning layout design of national land in the context of rural revitalization based on MOLA model

Abstract In the context of the comprehensive implementation of rural revitalization strategy, ecological protection and sustainable territorial spatial pattern are important elements of spatial planning and ecological civilization construction. With this as the starting point, this paper can use spatial analysis, hierarchical analysis, entropy method, integrated learning and other technical methods to divide the national spatial evaluation units under the MOLA model. Carry out spatial suitability evaluation of national land based on double constraints as well as spatial planning and design. The test results show that the villagers’ trust in the implementation of rural revitalization policies reached 78%, and most of them agree more with various policies on rural construction and development, which illustrates the necessity of territorial spatial planning in rural revitalization. The research data have good score validity when the observed variable of the questionnaire under the experimental test is greater than 0.4, which indicates the spatial planning layout under the proposed model. On the basis of mastering the current situation and problems facing rural revitalization, it not only proposes an effective path, but also scientifically adjusts and optimizes the space of rural areas, providing a boost to promote the construction and sustainable and healthy development of new rural areas.
Improving the Categorization of Intent of a Chatbot in Portuguese with Data Augmentation Obtained by Reverse Translation

Generally, to start the operation of a chatbot based on information retrieval, real data generated by a domain expert is needed so that the chatbot can start its interaction and continue improving from it. It takes a lot of time and cost to create knowledge bases with all the linguistic variations. This work proposes and evaluates a data augmentation methodology for a Portuguese language corpus, based on paraphrase and reverse translation, focused on generating data to categorize intentions in conversational agents in order to reduce the time to obtain and prepare quality data. The experiments carried out in the data augmentation task and evaluated in an NLU model showed that the method is effective, presenting an average gain in accuracy of 6%.
An Improved GAN-Based Method for Low Resolution Face Recognition

The Low Resolution Face Recognition (LR FR) issue focuses on the resolution variation problem mainly displayed in surveillance systems where faces are captured in an unconstrained environment. To address this challenging problem, we introduced a new method to recognize faces under Low and Very Low Resolution conditions. The proposed method consists of two phases : an off-line phase and an inference phase. In the off-line phase, we generate a model to transform Low-Resolution (LR) face images into High-Resolution (HR) face images via a Generative Adversarial Network (GAN). As for the inference phase, we make use of the already generated model to improve the face resolution then we extract deep features to identify the face. An experimental study was carried out on the famous ORL and FERET databases and the obtained results proved the efficiency of the proposed method to deal with Low Resolution (LR) and Very Low Resolution (VLR) Face Recognition problem.
Automatic Multi-document Summarization for Bangla News Text Using a Novel Unsupervised Approach

Digitalization made the world remarkably agile; therefore, plenty of news is published on a related topic in different newspapers. An automatic text summarizer can contain all the primary information on a particular topic to understand the main essence of news or an article quickly and efficiently. We have developed a novel extractive-based multi-document summarizer for Bengali news. A very few summarizers have been introduced for the Bangla language. To the best of our knowledge, the word embedding technique has not been used in any of the prior summarizers. The selected sentences of the summaries’ semantic and syntactic relationship are maintained through the utility of word embedding. In this paper, we have used predictive-based embedding, which is continuous bag-of-words-based word2vec algorithm. Since it is a multi-document summarizer, redundant sentences are present in the dataset and are removed by a clustering technique. The proposed model gives a statistically significant result in terms of ROUGE-L F1-measure, which outperforms the baseline system on the same scale. The proposed model achieves state-of-the-art performance and can do a better readable and informative synopsis, which might help the reader gain vital information reliably fast.
Multi-level Image Segmentation Using Kapur Entropy Based Dragonfly Algorithm

Multilevel thresholding divides an image into different objects based on multiple threshold values. A good multi-level segmentation scheme finds the best threshold values for optimizing between-class variance or entropy criterion. Nature-inspired algorithms are frequently employed to address such issues. This paper presents the dragonfly algorithm which uses Kapur’s entropy as an objective function to predict the optimized values of multilevel thresholds. The dragonfly algorithm is based on the static and dynamic movements of dragonflies. It has produced better results due to improved exploration-exploitation trade-off. The experiments were carried out using benchmark images for various threshold settings (ranging from 2–6). The proposed method has been assessed using well-known evaluation metrics namely PSNR, VIF, SSIM, RMSE and FSIM The performance is compared with recent works like the crow search algorithm, and traditional methods like Kapur’s and Otsu’s multi-threshold techniques. Experimental results show that the proposed method dragonfly algorithm with kapur’s entropy performed better compared to recent algorithms and traditional methods.
A Deep Learning Based Natural Language Processing Approach for Detecting SQL Injection Attack

A drastic increase in cyber-attacks affects internet reliability in different fields. The website Structured Query Language (SQL) Injection is still a loophole that attackers frequently use. Four algorithms have been implemented and tested in this study: Logistic Regression, Naive Bayes, Random Forest and Convolutional Neural Network (CNN). Natural Language Processing (NLP) is also applied to increase the text processing's detection accuracy. The study is made using two datasets for preparing the model. The data for the model that is used for training are contained in the first dataset. The validation records used to test and evaluate the model are contained in the second dataset. The primary dataset is turned into a corpus to help the model comprehend the input more quickly and easily. Based on the performance of the models, CNN has given the highest level of accuracy. The response time of the output is less, and the validation accuracy is 99.29%. Compared to machine learning methods, the Deep Learning (DL) algorithm performed far better. The accuracy of Machine Learning (ML) approaches is 0.942857 for Logistic Regression, 0.978576 for Naive Bayes, and 0.902380 for Random Forest.
A Word-Based Moroccan Sign Language Dataset for Emergency Situations: Use Case COVID-19

As a direct result of the COVID-19 epidemic, persons who are hard of hearing or deaf have lately been confronted with new obstacles. The health of this population is negatively impacted in a variety of ways, including restricted access to vital health care services and information, social and emotional difficulties, and poor health outcomes, amongst other things. The purpose of this study is to present a revolutionary word-based Moroccan Sign Language Database for Emergency Situations: Use case COVID-19 (MSL-COVID19). Twenty one individuals, ranging in age from 5 to 30 years old (9 females and 12 men), have recorded films of 15 Moroccan words being used in real-world contexts, with each individual contributing 10 examples. A video dataset such as this one is essential in order to support members of the deaf community who rely on sign language as their primary method of communication in crisis situations. Researchers in academia who are working on vision-based sign language identification as well as hand gesture recognition will find the dataset to be very useful. Since the database was based on an experimental investigation, it was also discovered that it may be exploited as a benchmark dataset for the creation of new and improved methods for recognizing the hand movements of words in Moroccan sign language.
PU Matrix Completion Based Multi-label Classification with Missing Labels

Multi-label classification has attracted significant interests in various domains. However, only few positive labels are labeled. Treating unknown labels as negative labels brings the one-side noise, which causes the severe performance degradation. Motivated by this, we present the positive and unlabeled (PU) matrix completion based Multi-Label Classification with Missing Labels called PUMCML, that focus on finding all positive labels and construct a classifier based on completion matrix under instance and label dependencies. More specifically, an asymmetrical PU learning loss function is firstly used to solve the one-side noise from missing labels. Secondly, an instance dependencies assumption is proposed that similar instances in multi-label data will share similar labels. Furthermore, considering the label dependencies, the completion matrix consisting of the predicted labels and origin labels is constrained by a nuclear norm. Finally, the proposed algorithm is optimization problem which is convex but not smooth, so Alternating Direction Multiplier Method is design for handling the optimization problem by dividing the problem into multiple subproblems. Experimental results on four benchmarks demonstrate our method achieves a huge improvement in performance and robustness to missing labels compared to other advanced algorithms.
Semantic Segmentation Using MSRF-NET for Ultrasound Breast Cancer

Deep learning stands out as one of the most performant modalities in the search for reliable methods to classify or segment breast cancer. The classical approach employs an expert to detect the type of tumour and segment the area of interest. Nevertheless, it takes considerable time and resources. Researchers around the world invent new deep learning architectures to improve quality and reduce segmentation time. One of these architectures demonstrates promising results, e.g., Dice Coefficient (DSC) of 0.9420, 0.9217, on CVC-ClinicDB, and Kvasir-SEG datasets respectively, this network named MSRF-NET (Multi-Scale Residual Fusion Network). The main novelty resides in exchanging variable scale features using the Dual-Scale Dense Fusion (DSDF) block, which gives the model a strong capability in segmenting objects of numerous sizes. This paper proposes an implementation of MSRF-NET for segmenting ultrasound breast cancer. We will investigate the influence of batch size, epochs, input size, learning rate on training and testing. Best performing model achieved a 93.95% in Dice Coefficient, 96.1% in specificity, 94.2% in precision, and 93.7% in recall. Lastly, we compare the performance of MSRF-NET with that of U-net and its variants, U-net++ and V-net.
Introduction to Augmented Reality

Congratulations on embarking on an augmented reality (AR) journey! I look forward to accompanying you in developing your very own AR creations.
Case-Based Reasoning Approach, Integrating Deep Learning for Patient Diagnosis Combined X-Ray with Symptoms

Case based reasoning (CBR) is one of the most widely used reasoning approaches in expert knowledge-centered domains such as the medical sector, due to the risk that a false diagnosis may generate the requirement for models to be explainable. However, these models depend considerably on the user input (symptoms) and the input of the patient’s radiology image. Deep learning approaches based on convolutional neural networks have been proved in several papers to be relevant in imaging processing. This work proposes a hybrid framework of case-based reasoning and deep learning to be applied to a diagnostic support system. In the paper, we propose to couple the power of CNNs in radiology image analysis with the user-centered approach of case-based reasoning models. In addition, the framework on which we based is modular and adapts to a wide variety of tasks, data and uses, in fact, the system will receive as input the radiological image of the patient combined with the different symptoms, and will propose to the experts one or more predicted diagnoses with a probability score for each. The expert can validate or not a choice, his decisions will be taken in charge by the system to be added to the knowledge base in the case of validation or stored in another separate base in the case of non-validation. An implementation of the approach for diagnostic support is provided.
Analysis of the Effectiveness of the Security Incident Response Team Under Intensity Cyber-Attack Increasing

Recently social groups become a target for the realization of negative information or psychological influence on them, the formation of their intentions to commit harmful behavior to society and so on. Thus, there is a need to study the peculiarities of the Information Security Incident Response Team (SIRT) in the new environment. Paper builds the model that took into account the increase in the intensity of individual incidents in the process of cyber-attacks and the need to restore the efficiency of personnel. The apparatus of queuing systems is used. The methods for managing the effectiveness of countering cyberattacks have been developed, as a result of which it is possible to select those SIRTs that have the desired effectiveness. It is also possible to manage the staff of SIRT, taking into account the individual characteristics of individual employees (for example, the level of efficiency of their work, features of recovery during work, features of their work in high stress with increasing cyberattacks, etc.). A method for managing the effectiveness of SIRT with the use of computer simulation has been developed, which allows to take into account real statistical features already in the process of cyber-attack deployment.
A Hybrid Image Steganography Method Based on Spectral and Spatial Domain with High Hiding Ratio

The internet's global data communication has made data transfer both simple and dangerous. The security of data transferred over the Internet is a major concern nowadays. Steganography is a popular method for preventing unauthorized access to data. This paper proposes a three-level secured hybrid image steganography method with increased payload by embedding secret data bits in both the spectral and spatial domains. First, the cover and secret image are encrypted using the permutation order (PO) generated by the initial keys (First Level security), and then the cover image is converted using DCT and random DCT blocks are selected using key values to embed the secret data (Second Level security). Finally, the remaining secret data bits are embedded at random locations specified by the key values in the LSB planes (Third Level security). The method achieves a PSNR of 35.86 dB and a hiding ratio of 50% (4bpp), according to the performance results.
Extracting the Distinctive Features of Opinion Spams Using Logistic Regression

Given the growing proliferation of social networks in the last couple of years, these online platforms have turned into a virtual space where users disseminate a massive amounts of information in a very short time. However, a considerable amount of such information is false. In fact, spammers usually target these online platforms to spread opinion spams and, thus, affect the decisions of other internet users. To overcome this problem, our objective in this paper is to propose an approach that identifies the distinctive characteristics of opinion spams through the use of a word annotation dictionary that we constructed together with logistic regression.
The Impact of Covid 19 on Recommendation Platforms

Recommender Systems that use artificial intelligence algorithms have achieved much lately, prompting exact expectations in numerous e-commerce areas. Nonetheless, this climate experienced unexpected changes with the beginning of the corona pandemic fixated on a remarkable expansion in the volume of clients and quick adjustments in client practices and profiles. This paper examines the effect of the corona pandemic on the recommender systems scene and spotlights new and abnormal customers. We detail how multicriteria, psychological, explainable methods, and online artificial intelligence can recognize and adjust to changes in purchaser practices and utilize profiles to give exact and convenient forecasts.
Path Planning of Autonomous Vehicles Based on Deep Learning Technology

With the continuous improvement of the intelligent level of unmanned vehicles, its operating range is becoming wider and wider, the road traffic environment it faces is becoming more and more complex, and the possibility of various uncertain events is also increasing. It is of great significance to study the global path planning that adapts to the real traffic network. The purpose of this paper is to study the simulation experiment of autonomous vehicle path planning under deep learning technology. The background and significance of the research on unmanned driving strategy, the current development status of autonomous driving technology in the world, and the current development status of deep learning technology are introduced. The requirements of the control cycle duration when the car is running at high speed. The BIT* algorithm is improved, and the NBIT* algorithm is proposed, which greatly improves the planning speed when dealing with a large number of path planning tasks. The path planning of the 200 images in the NBIT* algorithm test set takes 1.61 s in total.
Incongruity Detection Between Bangla News Headline and Body Content Through Graph Neural Network

Incongruity between news headlines and the body content is a common method of deception used to attract readers. Profitable headlines pique readers’ interest and encourage them to visit a specific website. This is usually done by adding an element of dishonesty, using enticements that do not precisely reflect the content being delivered. As a result, automatic detection of incongruent news between headline and body content using language analysis has gained the research community’s attention. However, various solutions are primarily being developed for English to address this problem, leaving low-resource languages out of the picture. Bangla is ranked 7th among the top 100 most widely spoken languages, which motivates us to pay special attention to the Bangla language. Furthermore, Bangla has a more complex syntactic structure and fewer natural language processing resources, so it becomes challenging to perform NLP tasks like incongruity detection and stance detection. To tackle this problem, for the Bangla language, we offer a graph-based hierarchical dual encoder (BGHDE) model that learns the content similarity and contradiction between Bangla news headlines and content paragraphs effectively. The experimental results show that the proposed Bangla graph-based neural network model achieves above 90% accuracy on various Bangla news datasets.
Construction of English Grammatical Error Correction Algorithm Model Based on Deep Learning Technology

Syntactic EC (GEC) is one of the most important research tasks in natural language processing (NLP). The purpose of GEC is to detect and correct grammatical mistakes in the text. With the development of deep learning and the explosive growth of data, translation mode has become the first choice for GEC tasks, and Seq2SEq (sequence to sequence) model has been widely used in GEC tasks. In this paper, the most common RNN (RNN) algorithms in the field of NLP are analyzed, but the RNN algorithms have the problems of gradient dispersion or gradient explosion and long-distance dependence. Transformer addresses these two issues by introducing self-attention. This paper uses the encoder structure of Transformer model to build an English grammar (EG) error correction (EC) model and compares it with the other two algorithm models. According to the test results, the proposed model is superior to the other two models in terms of article and verb form EC except for the preposition EC.
Ensemble of Classifiers for Multilabel Clinical Text Categorization in Portuguese

The widespread adoption of medical document management has generated a large volume of unstructured data containing abbreviations, ambiguous terms, and typing errors. These factors make manual categorization an expensive, time-consuming, and error-prone task. Thus, the automatic classification of medical data into informative clinical categories can substantially reduce the cost of this task. In this context, this work aims to evaluate the use of an ensemble of classifiers of clinical texts to differentiate them into prescriptions, clinical notes, and exam requests. For this, we used the combination of N_gram+TF-IDF and BERTimbau to vectorize the text. Then, we used the classifiers Random Forest, Multilayer Perceptron, and Support Vector Machine to create the ensemble. After that, we predict the final ensemble label through a voting approach. The results are promising, reaching an accuracy of 0.99, kappa of 0.99, and F1-score of 0.99. Our approach allows automatic and accurate classification of clinical texts, achieving better categorization results than individual approaches.