Reviewerly/exHarmony huggingface dataset:
Segmentation of the Haematoxylin and Eosin Stained Muscle Cell Images—A Comparative Study

The effective detection of muscle cells, the accurate counting of their numbers and the analysis of their morphological features have great importance in biomedical research. At present, the quantification of muscle cell and the computation of their cross-sectional areas (CSA) are still manual or semi-automated, and with the increase of the image number, the manual or semi-automated methods might become intractable. Hence, the automatic methods are very desirable, which motivated the developments of many muscle cell segmentation methods. In this paper, three methods, SDDM, CELLSEGM and SMASH are compared and evaluated with 100 images with over 6000 cells. The Dices computed by SDDM, CELLSEGM and SMASH are 97.38%, 89.85% and 90.08% respectively. The average differences between the calculated cross-sectional areas and the ground truths by SDDM, CELLSEGM and SMASH are 5.14%, 10.76% and 7.97% respectively.
Verso un modello glottogeragogico Lo <i>scaffolding</i> cognitivo-emozionale

Language learning experiences seem to lead to advantages for the elderly in the social, psychological and cognitive spheres. Learning languages is not only useful for the quality of life of the elderly and for their active participation in society, but it also has an important role in stimulating and maintaining cognitive resources, increasing cognitive reserves and countering their decline favouring the development of compensation processes. From this point of view, learning languages for the elderly is not only possible but it is also desirable. This justifies a FL geragogic model, called cognitive-emotional scaffolding, which can become the basis of a project to teach foreign languages to elderly learners.
An Approach for Identifying Malicious Domain Names Generated by Dictionary-Based DGA Bots

Computer networks are facing serious threats from the emergence of sophisticated new DGA bots. These DGA bots have their own dictionary, from which they concatenate words to dynamically generate domain names that are difficult to distinguish from human-generated domain names. In this letter, we propose an approach for identifying the callback communications of DGA bots based on relations among the words that constitute the character string of each domain name. Our evaluation indicates high performance, with a recall of 0.9977 and a precision of 0.9869.
『표준국어대사전』의 ‘형태론’ 관련 전문용어의 사전 기술에 대한 단견

In this paper, I critically review some descriptions of the morphological terminology in the Standard Korean Dictionary (henceforth, SKD). The findings are as follows. First, there are 3,480 linguistics terms and 225 morphology terms in the SKD, and most of the morphology terms are the expressions of word-formation. Second, the definitions of some morphology terms in the SKD(English equivalents shown), such as variant, allomorph, nominal compound, verbal compound, adjectival compound, acronym, collocation, blend, conjugation form, differ from those of descriptive grammar. Third, some words not listed in the SKD are presented as the definition and the immediate constituents of the headword. Fourth, some related morphology terms in the SKD, such as simple word, complex word, immediate constituent, are not presented consistently.
MODERN TRENDS OF EDUCATIONAL ACTIVITIES IN INSTITUTIONS OF HIGHER EDUCATION

The concept of the educational process is revealed and the main features of its implementation in the conditions of higher professional education are indicated. An approximate plan of &#x0D; the mechanism for the implementation of the educational component in educational activities is given.
Detecting Wake-word Repeats in Voice-activated Systems

In this paper we propose a practical method to improve the false rejection rate of a voice-activated system. The users of such systems tend to repeat the activation word in the case of the false rejection. In our method we modify a decoder of a phoneme-based voice-activated system for detecting such repeats which allows to catch 14.6% of previously undetected cases, while not increasing the false alarm rate, and 79% while adding less than 1% of false alarm rate. We use the fact that the repeat of the activation word consists of twice as many phonemes than the single pronunciation which allows more reliable detection.
Implementation of “AR + Books” Production Process Based on Virtual Simulation Experiment

The aim is to enable students to master the production process of “AR + Books”. By setting up the implementation process of children’s AR books, including traditional book production, multimedia resource design and AR content implementation, students can experience the realization process of “AR + book” production process in the virtual simulation system, so that they can make AR books by themselves according to the overall process. Using unity 3D, vuforia and other design software to complete the development and production of AR books and mobile terminals. Based on virtual simulation laboratory and “AR + book” technology, the realization process of “AR + book” is fully displayed by virtual simulation experiment, so that students can learn and master the whole process of “AR + book” production on the basis of immersing in virtual reality.
Neural network approach to recognition of visible constellations by sky photo image

Abstract The current paper demonstrates the effective capabilities of deep neural networks in solving the problem of identification of constellations from a photo of the sky in conditions of a priori uncertainty, incomplete observability and stochastic disturbances. The quality of solution 0,927 by metric F1 is obtained. In order to achieve the result, the original ResNet-like architecture of the convolution neural network was synthesized; statistical analysis of the dataset was carried out, the function of losses and strategy of neural network training were developed, and an accurate criterion of constellation observability in the image was formed. The observation of noise influence on the quality and stability of solutions was carried out.
A Metacognitive Perspective: Keys to Effective Active Learning and Potential Barriers

This manual has been created to ease the burden of teachers in medical education as they attempt to fulfill the LCME accreditation standards that ensure self-directed experiences leading to life-long learning [1]. We have documented large group active learning experiences that are effective in meeting this standard because they provide the feedback from experiential learning that students need to become self-directed in their individual study. It can also be argued that regular involvement in active learning is a behavior that contributes to the development of the newly published Core Entrustable Professional Activities For Entering Residency [2]. After setting the stage with an introductory chapter that provides a history of the concept of active learning, each chapter describes those modalities that are currently being used in large group settings. A standardized format describes how to best implement each modality with examples of objectives, suggestions for assessment, and an evaluation of the pros and cons of the method. It is our hope that this manual has also provided a stimulus for discovery of other possibilities for large group active learning experiences.
Dimensionality Reduction for Face Recognition Using Principal Component Analysis Based Big Bang–Big Crunch Optimization Algorithm

Dimensionality reduction is a significant step for various multimedia-based applications, where transmission bandwidth and storage space requirements are prodigious. In this paper, a new hybrid principal component analysis (PCA) based technique in combination with big bang–big crunch (BB-BC) optimization algorithm has been proposed. In this approach, PCA method is used for feature extraction and subsequently BB-BC algorithm performs feature selection. The purpose of this hybrid approach is to investigate the potential of dimensionality reduction, while retaining the maximum information. The recognition rate is considered to be the key performance indicator. The implementation of this method has been carried out using Olivetti Research Laboratory (ORL) face database. In the analysed scenario, this approach outperforms solely applied PCA technique with identical number of features selected.
Data Sparsity and Cold-Start Problems in $$M-CCF$$ Recommender System

Precise identification of users’ neighbourhood has a direct impact on the performance of recommender systems. Clustering techniques are the solution that is often used for this purpose, however, they negatively affect the accuracy of recommendations.In this article, a new version of the algorithm based on multi-clustering, \(M-CCF\), is described. Instead of one clustering scheme, it works on a set of multi-clusters, therefore it selects the most appropriate one that models the neighbourhood of a target user most precisely. This article confirms the robustness of \(M-CCF\) over the traditional methods against data sparsity and cold-start problems due to its ability to generate more accurate recommendations even if the number of ratings is insufficient.
A Column Study on The Efficiency and Reusability of Kapok Fibre as Car Wash Wastewater Treatment

Abstract The car washing industry which consumes large amount of water produces Car Wash Wastewater (CWW) which were likely to exceeds the standard set in both Malaysia’s and Singapore’s regulation. Kapok Fibre (KF) is a natural adsorbing material which have large lumen and low density is believed to be able to act as an adsorbent to manage the CWW. This study was done to determine the characteristic of KF and CWW and investigate the efficiency of KF in treating CWW along with the reusability of KF through a column study with Organic Loading Rate (OLR) of 12.41g COD/L.day. The CWW contains a certain concentration of Chemical Oxygen Demand (COD, Oil and Grease (O&amp;G), and Anionic Surfactant (AS) though the interaction between KF and AS is novel. KF found out to has the capability in removing COD, O&amp;G, and AS up to 88.37%, 100%, and 83.8% respectively. However, in reusability experiment there is limitation to KF in which by exceeding the operation time to 600 minutes of treatment, the efficiency in removing COD, O&amp;G, and AS were found to drop to 62.02%, 75%, and 55% respectively. This shows that KF could be an avant-garde, low-cost sustainable treatment to treat CWW instead of the conventional high cost and complex operating system.
Development of controlled drip irrigation with lock time system

Abstract The utilization of controlled systems in drip irrigation can increase land productivity and can minimize water usage in the dry season. This irrigation system can reduce water loss and can increase the effectiveness and efficiency of agricultural activity. The purposes of this study were to develop and to analyze the ability of controlled drip irrigation with lock time system for watering crops on time and according to the needs. The research method of this study was research and development (R &amp; D) method, starting from designing a control system, prototyping, functional testing, field testing, and data analysis. Based on the test results, the controlled drip irrigation system that had been developed had high time accuracy, with an error percentage of 0.9%. The response of the solenoid valve function had high sensitivity, with an R2 value of 0.99%. This system had high spray consistency with an average spraying error rate of 1.3 ml per emitter. Based on these results, it can be concluded that the controlled drip irrigation device with a lock time system that had been developed can function properly and is suitable for watering crops on time and according to crop needs.
Knowledge-Based Economy: Enhancing Economic Growth and Development of Human Capital Through Information and Communications Technology Education

Globalization and the rapid development of Information and Communications Technology (ICT) have advantaged economies which have invested in (re)skilling their human capital in technical knowledge. Similarly, ICT have spearheaded the growth and development of industrial and service sectors in emerging economies. This accounts for the progress of some Asian economies and the lagging behind of many sub-Saharan African countries, including Ghana. Primarily, reorientation of economic development strategy and human development policy in tandem with the demands of knowledge-based economy (KBE) and related development in ICT explain the differences among the world's economies. Our chapter discusses the extent to which ICT has been incorporated into the educational system to transform it from traditional education in order to reskill human capital in postprimary schools to support the creation and growth of KBE in Ghana. Moreover, the chapter assesses whether ICT infrastructure and syllabuses at postprimary schools meet the challenges of a KBE and an enhanced growth development in Ghana. Using the theories of evolutionary economic change, new growth, technology and knowledge gap, we intend to analyze the progress and challenges faced by Ghana as it strives to build a KBE that thrives on innovation and creativity, which in turn drive economic growth and development. In this respect, we examine postprimary schools in Ghana.
Improved result for fuzzy systems with time invariant delay

It is shown in [20] that the HPMFD is an efficient method to deal with fuzzy system. In this paper, we extend this method to fuzzy systems with time invariant delay. The matrices in the Lyapunov-Krasovskii functional are designed as HPMFD and the time derivative are also analyzed by applying a switching method. In the end, an example is presented to compare with other method and less conservative results can be obtained by increasing the degree of HPMFD matrices.
A Convolutional Neural Network-Based Triplet-Mining Metric Learning Schema for Measuring Patient Similarity Addressing Personalized Healthcare

Prediction of the development risk of some diseases is an important area of Health Care Research. When exploring the personalized care of the patients, precise identification and classification of similarity in patients from their past report is an important process. Electronically stored health information EHRs that has been sampled unevenly as well as which has variable appointment durations, is considered to be unsuitable for measuring the similarity among patients directly, as there is no proper representation that are fitting. In addition, a technique is required that is efficient to evaluate similarities in patient. We propose two new similarities learning environments using deep learning that learn simultaneously the representations of the patients as well as measurement of similarity in pairs. A Convolutional Neural Network (CNN) is used to understand EHRs that contains crucial information which are local thereby providing scholastic illumination in the triplet loss otherwise entropy loss. When the training is completed, distances are calculated as well as similarities scores. Using this similarity information, disease predictions along with patient grouping is performed. Experimentally the results gives an idea that CNN can represent the EHR sequences in a better way and the schema offered are more efficient than the modern metric distance learning.
E-book limitation with library portable document format java script for accessing digital library

The function of the Adisutjipto-Aerospace Institute of Technology (ITDA) library is to provide borrowing and reimbursement services for books insured by members of the library. The books which increase in number each year occupy a lot of space in the library, it is therefore necessary to study it so that the form of this book is no longer physical but digital or paperless to be able to be stored on a server computer. This form of library is known as a digital library or e-library. The electronic library is a modern library as a place which provides a source of educational information with more convenience and security. The flow in the application is not very different from the flow in the manual library. Security becomes an important part of an application, by limiting network access, accessing PDF documents and implementing PDF Viewer using the PDF.js library by rendering PDF documents on a canvas capable of blocking illegal downloads. Based on the test results, the electronic library application can work properly. The speed of opening a PDF document is influenced by the speed of the server, the content contained in the PDF and the size of the PDF file.
Developing the Persian Wordnet of Verbs Using Supervised Learning

Nowadays, wordnets are extensively used as a major resource in natural language processing and information retrieval tasks. Therefore, the accuracy of wordnets has a direct influence on the performance of the involved applications. This paper presents a fully-automated method for extending a previously developed Persian wordnet to cover more comprehensive and accurate verbal entries. At first, by using a bilingual dictionary, some Persian verbs are linked to Princeton WordNet synsets. A feature set related to the semantic behavior of compound verbs as the majority of Persian verbs is proposed. This feature set is employed in a supervised classification system to select the proper links for inclusion in the wordnet. We also benefit from a pre-existing Persian wordnet, FarsNet, and a similarity-based method to produce a training set. This is the largest automatically developed Persian wordnet with more than 27,000 words, 28,000 PWN synsets and 67,000 word-sense pairs that substantially outperforms the previous Persian wordnet with about 16,000 words, 22,000 PWN synsets and 38,000 word-sense pairs.
Contatto linguistico e influenza translinguistica Un’indagine in una scuola internazionale a Napoli

The paper investigates the oral linguistic productions of children of different ages in an international school based in Naples, in which English is used as the main language of communication for all the subjects taught. Spanish is also used but for a lesser number of hours. The purpose of this paper is to identify the possible crosslinguistic influence across languages that students master with reference to some syntactic and morphological phenomena.
Chatbot via Machine Learning and Deep Learning Hybrid

In present-day world, Conversational AI has become a part of our daily life while shopping, banking, or querying any customer service. These are commonly called as Chatbot nowadays, which are mostly used in web or android apps like Siri, Google Assistant, Alexa, etc. This paper introduces a chatbot that can be designed for any specific domain. In other words, it can handle multiple use cases according to the business requirements. The architecture of this chatbot is a multi-level model, consists of a machine learning classifier e.g. SVM, sentence similarities like cosine similarity, and the deep learning seq2seq model. Each level is trained on different datasets except the sentence similarity. We achieved a better result in terms of qualitative and quantitative terms i.e. classifier accuracy of 0.83 and bleu score of 0.31 compared to the state-of-the-art.
Reidentification Risk from Pseudonymized Customer Payment History

To assess accurately the risk of being compromised, anonymized data requires a balance between utility and security. This paper studies the risk of reidentification for long-term transaction records aggregated by pseudonym (pid). Given a set of transaction records with pseudonyms, an adversary attempts to identify individuals by maximizing the similarity between sets of goods that have been purchased using the pseudonyms. Assuming a uniform probability for the choice of goods and applying Zipf’s law to the behavior of the number of records per individual, we investigate the likelihood of an individual being correctly reidentified. Our model reveals that the risk of reidentification increases as the number of records associated with the pseudonym increases. A similar effect in terms of risk and the number of pseudonyms was found in a competition for data anonymization (PWS Cup 2017), where the Online Retail dataset comprising 40,000 records for 500 individuals was securely anonymized by each team and then reidentified each other. The competition results suggested that the reidentification rate increases the longer a pseudonym remains unchanged.
On privacy enhancement using u-indistinguishability to COVID-19 contact tracing approach in Korea

South Korea's COVID-19 contact tracing is unique because detailed and personally sensitive information has been disclosed. As a result, privacy concerns and controversies have been raised. As long as the Korean format of contact information is to be shared, a more sophisticated technique is required to mitigate the risk of privacy breaches. To meet the requirement of minimum privacy infringement, technical solutions for privacy enhancement are needed. In this paper, a u-indistinguishability concept is proposed and its effectiveness is shown. By mixing at least u patients' quasi-identifiers into a cluster and their associated movement information into another cluster, linkability is weakened significantly.
Synchronization control for completely unknown chaotic systems via nested back-propagation neural networks

To solve the problem of existing chaotic systems with unknown nonlinearities, enormous parameters and external disturbances, in this paper, a synchronization controller with parameter adaptive laws is proposed based on nested back-propagation neural networks and the adaptive method, where the nested back-propagation neural networks are used to approximate the unknown nonlinearities based on same experiences and the unknown parameters are estimated by the adaptive method. Then the asymptotical synchronization of the drive-response chaotic systems is synthesized via state feedback controllers and updated adaptive laws. Specifically, the nested back-propagation neural networks are developed by grouping and layering the hidden neurons using the principle of partition of unity and the state domain for modularizing the concealed layer. Finally, a numerical example is given to illustrate the effectiveness of this method.
Application of Artificial Intelligence in the prediction of breast cancer survival in Mexican women

In Mexico, the leading cause of death caused by malignant tumors in women is breast cancer and the general survival of five years treated in facilities of the Public Health System is between 75 and 80%. There are applications that determine the survival of patients with breast cancer, based on the use of drugs that are not prescribed in Mexico, so cancer specialists cannot consider the information offered by these programs for decision-making with patients Mexican. This article describes the development of an expert system that, by applying artificial intelligence techniques, allows the evaluation and prediction of patient survival, based on a model generated with data mining techniques. Rules were obtained from the patterns obtained with data collected from patients with breast cancer since 2006. The development of the system is governed by the Knowledge Discovery from Databases (KDD) methodology, supported by the WEKA tool for modeling data mining techniques. There is a data warehouse of 4,773 women with breast cancer provided by two tertiary hospitals in Mexico City: an INCan cohort of 4,300 patients diagnosed from 2006 to 2013 with a median follow-up of 40.5 months of survival and an INCMSZprovided cohort of 473 patients from 2011 to 2018 with a median of 39 months.