Reviewerly/exHarmony huggingface dataset:
Text Recognition in Complex Images Using Deep Learning Models: A Survey

Text recognition in images is a very challenging task. It is used for guiding visually impaired persons and also in semantic meaning. In this survey, we are going to explore different techniques implemented in the previous years on complex background images. It gives the analysis of related works and also emphasizes the performance of the image recognition. Image complexity cannot be defined in a precise manner but we can describe its parameters such as backgrounds, noise, illuminating conditions, textures, fonts, etc. This survey also highlights few benchmark datasets that are used in this review. Through this review, we can analyze various problems present in this field by contrasting their features.
PROPRIEDADE INTELECTUAL: ENSAIO SOBRE O PERCURSO DE UMA PESQUISA

O presente ensaio debruca-se sobre o processo de pesquisa da obra literaria de Torquato Neto, abordando tanto questoes do cotidiano da autora como questoes teoricas que guiam a relacao estabelecida com o objeto de pesquisa. Ao longo do ensaio, os mundos da pesquisa academica e do mundo do trabalho de traducao patentes se cruzam, delineando o campo de possibilidades da escrita e do desenvolvimento de uma tese de doutorado. A partir deste cruzamento, busca-se encontrar uma metodologia que possibilite abordar a obra de um autor que nunca publicou nenhum livro em vida e cuja insercao dentro do campo da literatura de se deu anos apos a sua morte. Assim, explora-se como a ideia de incompletude e fracasso podem ser utilizadas dentro de um campo conceitual, ao mesmo tempo em que questoes relacionadas a autobiografia, ao percurso de realizacao de uma pesquisa e a relacao que um pesquisador estabelece com seu objeto de pesquisa sao examinadas. Intellectual Property: An essay about a ressearch's pathway The current essay dwells on Torquato Netos’s literary work research process, addressing issues concerning the author’s daily life as well as the theoretical inquiries that guide the relationship established with the research object. Throughout the essay, the fields of academic research and the work of translating patent applications cross, establishing the field of possibilities of writing and development of a PhD thesis. From this crossing, it tries to find a methodology that makes it possible to address the work of an author who never published a book during his lifetime and whose inclusion within the literary field happened after his death. Thus, this essay explores how the idea of incompleteness and failure may be used in a conceptual field at the same time that examines issues concerning autobiography, the path of conducting a research and the relationship a researcher establishes with its object.
Creation of Intellectual Property Based on Natural Language Generation Model

Privacy concern in individual and public or private organizational levels is a crucial point. The increase in such a matter is highly evident nowadays, with the development of high technology. This study proposes a system for text mining analyzing characteristics related to language. Quantitative and qualitative evaluations verify the usefulness of the system.
Now is the time to move past traditional 3-2-1 back-ups

Strangely enough, we have a professional photographer to thank for the 3-2-1 back-up strategy that many organisations around the world employ today. Peter Krogh originally shared the concept in his 2009 book, The DAM Book: Digital Asset Management for Photographers . 1 This back-up strategy has helped to prevent data losses for an untold number of organisations. However, in the world of digital, 11 years is a very long time and, arguably, the strategy is now ripe for an update, particularly in the face of the ongoing and growing scourge of ransomware attacks.
Pattern Recognition of Development Stage of Creepage Discharge of Oil–Paper Insulation under AC–DC Combined Voltage Based on OS-ELM

The recognition of the creepage discharge development process of oil–paper insulation under AC–DC combined voltage is the basis for fault monitoring and diagnosis of converter transformers; however, only a few related studies are available. In this study, the AC–DC combined voltage with a ratio of 1:1 was used to develop a recognition method for the creepage discharge development process of an oil–paper insulation under a cylinder–plate electrode structure. First, the pulse current method was used to collect the discharge signals in the creepage discharge development process. Then, 24 characteristic parameters were extracted from four types of creepage discharge characteristic spectra after dimensionality reduction. Finally, based on the online sequential extreme learning machine (OS-ELM) algorithm, these characteristic parameters were used to recognize the development stage of the creepage discharge of the oil–paper insulation. The results showed that when the size of the sample training set used in the OS-ELM algorithm is close to the number of hidden layer neurons, a high recognition accuracy can be obtained, and the type of activation function has little influence on the recognition accuracy. Four stages of the creepage discharge development process were recognized using the OS-ELM algorithm; the trend was the same as that of the characteristic parameters of the entire creepage discharge development process. The recognition accuracy was 91.4%. The algorithm has a high computing speed and high accuracy and can train data in batches. Therefore, it can be widely used in the field of online monitoring and evaluation of electrical equipment status.
Evaluating QualiCO: an ontology to facilitate qualitative methods sharing to support open science

Abstract Qualitative science methods have largely been omitted from discussions of open science. Platforms focused on qualitative science that support open science data and method sharing are rare. Sharing and exchanging coding schemas has great potential for supporting traceability in qualitative research as well as for facilitating the reuse of coding schemas. In this study, we present and evaluate QualiCO, an ontology to describe qualitative coding schemas. Twenty qualitative researchers used QualiCO to complete two coding tasks. In our findings, we present task performance and interview data that focus participants’ attention on the ontology. Participants used QualiCO to complete the coding tasks, decreasing time on task, while improving accuracy, signifying that QualiCO enabled the reuse of qualitative coding schemas. Our discussion elaborates some issues that participants had and highlights how conceptual and prior practice frames their interpretation of how QualiCO can be used.
Applying the Online Intelligent Grading System to College English Writing Teaching: An Empirical Study

Currently, much attention has been paid to the online intelligent grading system widely used in college English writing teaching. In an effort to make the best use of the system, the article conducted an empirical study of applying the system to college English writing teaching. The findings are: 1) the intelligent grading system does work efficiently in college English writing teaching, however, it needs to be optimized in assessing the students’ writing texts and providing related feedback; 2) it is necessary to combine with teacher-assisted feedback and supervision on the basis of the intelligent grading system.
Tire damage image recognition based on improved convolutional neural network

In the application of tire damage image recognition, the use of convolutional neural network to train the model needs to extract the feature information of the tire image, and when judging the feature information, it is prone to overfitting, which reduces the training accuracy of the algorithm. To solve this problem, an image recognition algorithm based on convolutional neural network and ridge regression analysis is proposed. A new regular term is introduced into the original loss function of the algorithm, in order to change the ratio of the two parts in the new loss function, and reduce the jitter of the fitted curve of the feature information. Finally, it is verified through experiments that the improved convolutional neural network algorithm can improve the training accuracy and image recognition rate of tire damaged images.
Blockchain Compliance with Federal Cryptographic Information Processing Standards

Under current Federal Information Security Management Act of 2002 (FISMA) requirements, all new Federal information technology (IT) programs and modernization efforts using blockchain must meet National Institute of Standards and Technology (NIST) cryptographic standards. This report evaluates four major blockchain technologies – Ethereum, Hyperledger Fabric, R3's Corda, and Multichain – to determine their level of NIST compliance. The intent is to provide Federal IT managers with the critical information they need to determine which blockchain platforms may be used in their projects.
A Resume Generator with Augmented Reality Features

A resume is an essential tool for job seekers when it comes to job hunting. This paper is intended to develop a web-based resume generator alongside with augmented reality features, known as AResume. The web-based application is built for job applicants who have difficulty in creating a professional resume from scratch, as well as trying to attempt the 'one-size-fits-all' approach. AR.js and A-Frame are the main libraries or web AR frameworks employed in the development of AResume to enrich the experience of augmented reality. A web-based AR is developed over mobile AR because of its lightweight, cross-platform support and no installation required. A generated resume is embedded with a QR code and AR markers. The QR code could be scanned using a smartphone to direct users to the AR scanner website. Users are able to move the scanner from marker to marker to view different contents such as videos, photos, and documents. AResume not only enables job applicants to create a resume with augmented features but also provides a better user experience for hiring managers when reviewing resumes.
Augmented Reality in Sports Analysis Using HDM Representation of Players’ Data

Augmented reality acts as a digital add-on to the user’s world. The augmented reality is a mixture of computer-generated or mediated perceptions and the real world. In this paper, the augmented reality advancement will help in the development of sports and performances of players are shown. How this technology will help in the broadcasting of sports and games and also data representation for real-time performance analysis. Hierarchically distributed data matrix (HDM) representation is used.
Development of model of optimal control of water supply system

This paper considers the problem of optimal control of branched water supply systems. To control the system, the problem of optimal distribution of products is developed whereas non-linear programming problems are applied. We consider a system for providing products, consisting of magistral and distribution pipelines, taking products from the magistral pipeline. Each distribution line has many warehouses. Products are taken into the system using the main intake facility and transferred between the warehouses using intermediate distribution facilities. To eliminate the deficiencies in management, tasks are set to determine the necessary intensities of product supply in the facilities, allowing timely provision of consumers with the necessary volume of products, to minimize losses, product discharges of facilities in the system during a certain control period.
Micro-class Task Design

The concept of wisdom education emphasizes the generation of students’ wisdom, which happens in teaching practice. Teachers arouse the original thoughts and knowledge base. Then students start the learning process of conception, discovery, choice, induction, and evaluation, and as a result, achieve the ability enhancement of creative thinking ability and problem-solving. Under the guidance of wisdom education, the micro-course design ought to effectively promote students’ experience in one or several stages of the intelligent learning process. It is suggested that students’ participate in and complete specific tasks to encourage students to experience the entire learning process. This article used various case explanation, mainly discussed how to design smart micro-course tasks that promote students’ wisdom generation and develop students’ thinking.
Computerized Solution Verification Systems Applied for On-Line Programming Training

The purpose of the article is to study the potential of automated solution verification systems for training Programming online. The automated solution verification is based on a system for developing a virtual machine/container that allows isolating the process of checking the program code within a time limit and with a memory limit available without compromising the security or stability of the installed system. To test the effectiveness of the research the authors organized the Python language training by using the developed system for teaching programming Steach.Space and the R programming language by using the online learning system Stepik. The authors found out that the use of automated solution verification systems can increase the effectiveness of online programming training. The research results show that the improvement rate was 29 per cent in the experimental group and only 17 per cent in the control group.
An efficient and novel data clustering and run length encoding approach to image compression

Abstract The paper explores the domain of lossy compression, specifically incorporating data mining techniques in the process of image encoding. Clustering is employed to group similar pixels in the image and henceforth use cluster labels in compressing the image. The proposed approach replaces the Discrete Cosine Transform phase of Conventional JPEG with a combination of clustering and Run Length Encoding so as to handle redundant data in the image effectively. Simulation with respect to benchmark data indicates improved compression (42.5%) in relation to existing solutions. Image quality metrices such as PSNR, structural similarity have also been tested and it is observed that the proposed approach achieves significant compression ratio with negligible loss in visual quality.
A Development Framework for Automated Facial Expression Recognition Systems

Automated facial expression recognition (AFER) has become an important research area with several computer vision (CV) applications. A robust AFER system requires sufficient good quality training and testing data for development and evaluation of a robust AFER model. There exist a number of AFER datasets and an increasing number of research works in AFER. However, research works in AFER have not matured to a stage that there are openly available platforms or toolsets to implement the pipeline of AFER system development. New comers to the field are faced with various challenges such as 1) images in the datasets are messy or with low resolutions; 2) the data are not organized into separate training and testing data for fair evaluation; 3) majority of the datasets are very small leading to insufficiency for training a model; 4) some datasets do not provide important facial features, 5) it is unclear which dataset to start with, and 6) no development framework and methodologies to systematically implement and test new models. In this paper, we present a framework with complete source code and algorithms to: 1) detect faces and crop face images in a given dataset for AFER; 2) extract facial landmark features from the face images and store as landmark images; 3) split the dataset into training and testing sets and stored into two CSV files consisting filename, emotion, landmarks, and features vectors for each image in its respective set; 4) train and evaluate the features vectors of the dataset using a deep neural network (DNN) model as the baseline; 5) train and evaluate a baseline convolutional neural network (CNN) on the face cropped images; 6) demonstrate the trained model on live videos and images. This study also outlines the necessary steps involved in developing an AFER system. The framework can help researchers to use a dataset to develop AFER systems and further improve the framework and benchmark the results.
Alternative Axiomatization for Logics of Agency in a G3 Calculus

Abstract In a recent paper, Negri and Pavlović (Studia Logica 1–35, 2020) have formulated a decidable sequent calculus for the logic of agency, specifically for a deliberative see-to-it-that modality, or dstit. In that paper the adequacy of the system is demonstrated by showing the derivability of the axiomatization of dstit from Belnap et al. (Facing the future: agents and choices in our indeterminist world. Oxford University Press, Oxford, 2001). And while the influence of the latter book on the study of logics of agency cannot be overstated, we note that this is not the only axiomatization of that modality available. In fact, an earlier (and arguably purer) one was offered in Xu (J Philosophical Logic 27(5):505–552, 1998). In this article we fill this lacuna by proving that this alternative axiomatization is likewise readily derivable in the system of Negri and Pavlović (Studia Logica 1–35, 2020).
Expert System Certainty Factor Method in Website-Based Pest Diagnosis of Eggplant Plants: Expert System Certainty Factor Method in Website-Based Pest Diagnosis of Eggplant Plants

Eggplant crop cultivation is often influenced by various factors, factors that often occurs is pests and diseases. Lack of information and still rely on the experience of farmers to deal with pests and disease is a major cause. This problem is particularly serious because the can lead to crop failure. In this research, expert systems to diagnose pests and diseases eggplant created to help farmers cope with the problem - a problem that occurs in terngnya garden and provide solutions and suggestions prevention caused by pests and diseases. Certainty factor method is suitable for expert systems to diagnose disease eggplant crop is because these methods produce results of the highest percentage of belief an expert.
Practical analysis of searchable encryption strategies for financial architecture

The trend of storing information on servers in the cloud raises serious security concerns since the increasing number of breaches has resulted in the theft of private data from millions of users. In case of fields like finance, where data privacy is a priority, encryption techniques are analyzed to offer certainty to the customers. A solution for this problem may be searchable encryption, a concept that permits encrypting data locally, moving it to a remote server and then performing operations directly on the ciphertext without decryption. In this paper, we implement four prominent searchable encryption techniques and thoroughly evaluate their performances for various use case scenarios in finance.
Takagi-Sugeno fuzzy control for Multi-input Multi-output systems based on subspace state space identification

In this paper, we started from black box system without any prior knowledge of its dynamic behavior, we applied inputs and recording outputs data. From this data we ran three subspace state space algorithms which are: the Numerical algorithm for Subspace State Space System IDentification (N4SID) with its two versions 1, 2 and the Multivariable Output Error State (MOESP) algorithm to select the suited algorithm between them who reproduce with fidelity the real system dynamic with a minimum estimation error. Then, an improved control process developed for discrete-time Takagi-Sugeno system. This latter was formed by the selected identification algorithm.
Homomorphic Analysis of Privacy-Preserving Psychological Medical Records

The mental health data and diagnosis notes are available online and accessible to all users, such as investigators, statisticians, data analysts, enduring, and psychiatric consultants. Conversely, electronic health records’ accessibility, which contains hugely insightful patient data, threatens mentally diseased patients’ privacy and secrecy. The objective of this work is to inspect privacy concerns about the psychological feelings of patients. Many investigators developed privacy-preserving data analysis methods. In the proposed approach, drawbacks of the accessible privacy-preserving techniques and analysis of the privacy-preserving data method allow investigators to provide mental illness patients’ privacy permit the right of entry to psychological health records. In this work, we attain maximum privacy, preserving subjective data by applying the homomorphic encryption algorithm and our approach’s performance analysis with other existing techniques. The implementation results demonstrated a substantial influence on numerous applications.
Some Forms of Collectively Bringing About or ‘Seeing to it that’

Abstract One of the best known approaches to the logic of agency are the ‘stit’ (‘seeing to it that’) logics. Often, it is not the actions of an individual agent that bring about a certain outcome but the joint actions of a set of agents, collectively. Collective agency has received comparatively little attention in ‘stit’. The paper maps out several different forms, several different senses in which a particular set of agents, collectively, can be said to bring about a certain outcome, and examines how these forms can be expressed in ‘stit’ and stit-like logics. The outcome that is brought about may be unintentional, and perhaps even accidental; the account deliberately ignores aspects such as joint intention, communication between agents, awareness of other agents’ intentions and capabilities, even the awareness of another agent’s existence. The aim is to investigate what can be said about collective agency when all such considerations are ignored, besides mere consequences of joint actions. The account will be related to the ‘strictly stit’ of Belnap and Perloff ( Annals of Mathematics and Artificial Intelligence 9 (1–2), 25–48 1993) and their suggestions concerning ‘inessential members’ and ‘mere bystanders’. We will adjust some of those conjectures and distinguish further between ‘potentially contributing bystanders’ and ‘impotent bystanders’.
Data Science Applied to Crime Analysis Based on Brazilian Open Government Data

Context: The criminality phenomenon affects the quality of life, the economic growth and the reputation of a nation. Each year, governments spend millions of dollars fighting violence, and consequently, crime prevention and control are highly concerning issues to the public safety agencies.Objective: Applying Data Science fundamentals to analyze open government data on the crimes that occurred in the Brazilian States.Method: We have conducted a controlled experiment to discover the association rules (AR) between the crimes and the States. Additionally, we have developed a ranking of the most dangerous States.Results: From a general viewpoint, with weights for all available crimes, Paraná was the most dangerous local during all the assessed years, followed by Rio de Janeiro. From the single perspective of murders, in 2019, the States of Roraima, Rio Grande do Norte, Sergipe, Acre and Pernambuco were ranked as the ten most violent ones, being Pernambuco and Acre among the most dangerous States from the two perspectives (weighted average and murders).Conclusion: The Data Science enables the execution of more precise diagnoses. The year of 2019 presented a general drop in the crime rates, with special emphasis on Paraíba, Goiás, Rio Grande do Norte and Ceará.