Reviewerly/exHarmony huggingface dataset:
Table Recognition for Sensitive Data Perception in an IoT Vision Environment

Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely.
The Quantum Codes over F_q and Quantum Quasi-cyclic Codes over F_p

In this paper, the quantum codes over F q are constructed by using the cyclic codes over the finite ring R = F q + vF q + ... + v m-1 F q , where p is prime, q = p s , m -1|p -1 and v m = v.The parameters of quantum error correcting codes over F q are obtained.Some examples are given.Morever, the quantum quasi-cyclic codes over F p are obtained, by using the self dual basis for F p s over F p .
Hermitian Laplacians and a Cheeger Inequality for the Max-2-Lin Problem

We study spectral approaches for the MAX-2-LIN(k) problem, in which we are given a system of m linear equations of the form x_i - x_j is equivalent to c_{ij} mod k, and required to find an assignment to the n variables {x_i} that maximises the total number of satisfied equations. We consider Hermitian Laplacians related to this problem, and prove a Cheeger inequality that relates the smallest eigenvalue of a Hermitian Laplacian to the maximum number of satisfied equations of a MAX-2-LIN(k) instance I. We develop an O~(kn^2) time algorithm that, for any (1-epsilon)-satisfiable instance, produces an assignment satisfying a (1 - O(k)sqrt{epsilon})-fraction of equations. We also present a subquadratic-time algorithm that, when the graph associated with I is an expander, produces an assignment satisfying a (1- O(k^2)epsilon)-fraction of the equations. Our Cheeger inequality and first algorithm can be seen as generalisations of the Cheeger inequality and algorithm for MAX-CUT developed by Trevisan.
ANN-assisted forecasting of adsorption efficiency to remove heavy metals

In wastewater treatment, scientific and practical models utilizing numerical computational techniques such as artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems. In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which is determined by mean square error (MSE) and coefficient of determination (R$^{2})$. According to the results, the model can forecast adsorption efficiency percent (AE%) with a tangent sigmoid transfer function (tansig) in the hidden layer with 10 neurons and a linear transfer function (purelin) in the output layer. Furthermore, the Levenberg--Marquardt algorithm is seen to be most ideal for training the algorithm for the case studies, with the lowest MSE and high R$^{2}$. In addition, the experimental results and the results predicted by the model with the ANN were found to be highly compatible with each other.
A Pearson Correlation Analysis of the SoftwareEngineering Practice in Micro and Small-SizedSoftware Industry of Sinaloa, Mexico

Is well-known that software development process fordesktop, web or mobile applications in industry needs the adoptionof best practices of Software Engineering. Nevertheless, notmuch contemporary information with regard to current practicesin micro and small-sized software industry exists especially inSinaloa, Mexico. This work presents an exploratory study whichprovides insight into industrial practices in the software industryof Sinaloa. A combination of both qualitative and quantitativedata is collected, using semi-structured interviews and a detailedquestionnaire from sixteen software factories. A Pearson correlationanalysis was performed independently between the variablesCompany location, Scope of coverage, Number of workers, Timeto live in the market, Projects completed, Time dedicated toactivities related to the project, Outdated projects completed inorder to determine the degree of relationship between each ofthe variables mentioned, with all. A correlation analysis and ananalysis of variance were performed. The quantitative resultswill serve to obtain opportunities for further interpretation andcomparison.
Data Representations for Segmentation of Vascular Structures Using Convolutional Neural Networks with U-Net Architecture

Convolutional neural networks (CNNs) produce promising results when applied to a wide range of medical imaging tasks including the segmentation of tissue structures. However, segmentation is particularly challenging when the target structures are small with respect to the complete image data and exhibit substantial curvature as in the case of coronary arteries in computed tomography angiography (CTA). Therefore, we evaluated the impact of data representation of tubular structures on the segmentation performance of CNNs with U-Net architecture in terms of the resulting Dice coefficients and Hausdorff distances. For this purpose, we considered 2D and 3D input data in cross-sectional and Cartesian representations. We found that the data representation can have a substantial impact on segmentation results with Dice coefficients ranging from 60% to 82% reaching values similar to those of human expert annotations used for training and Hausdorff distances ranging from 1.38 mm to 5.90 mm. Our results indicate that a 3D cross-sectional data representation is preferable for segmentation of thin tubular structures.
Sparse Embedding for Interpretable Hospital Admission Prediction

This paper introduces a sparse embedding for electronic health record (EHR) data in order to predict hospital admission. We use a k-sparse autoencoder to embed the original registry data into a much lower dimension, with sparsity as a goal. Then, t-SNE is used to show the embedding of each patient's data in a 2D plot. We then demonstrate the predictive accuracy in different existing machine learning algorithms. Our sparse embedding performs competitively against the original data and traditional embedding vectors with an AUROC of 0.878. In addition, we demonstrate the expressive power of our sparse embedding, i.e. interpretability. Sparse embedding can discover more phenotypes in t-SNE visualization than original data or traditional embedding. The discovered phenotypes can be regarded as different risk groups, through which we can study the driving risk factors for each patient phenotype.
Infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning

Accurate image registration is important for quantifying dynamic brain development in the first year of life. However, it is challenging to deformable registration of infant brain magnetic resonance (MR) images because: (1) there are large anatomical and appearance variations in these longitudinal images; (2) there is a one-to-many correspondence in appearance between global anatomical regions and local small therein regions. In this paper, we apply a deformable registration scheme based on the global and local label-driven learning with convolution neural networks (CNN). Two to-be-registered patches are fed into an U-Net-like regression network. Then a dense displacement field (DDF) is obtained by optimizing the loss function between many pairs of label patches. Global and local label patch pairs are only leveraged to drive registration during training stage. During inference, the resulting 3D DDF is obtained by inputting two new MR images to the trained network. The highlight is that the global tissues, i.e. white matter (GM), gray matter (GM), cerebrospinal fluid (CSF), and the local hippocampi are well aligned at the same time without any priori ground-truth deformation. Especially for the local hippocampi, their Dice ratios between two aligned images are highly improved. Experiment results are given based on intra-subject and inter-subject registration of infant brain MR images between different time points, yielding higher accuracy in both global and local tissues compared with state-of-the-art registration methods.
COGNITIVE MODELING OF THE FISHERIES EFFECT ON THE STANDARD OF LIVING IN THE WHITE SEA AREA

A dynamic system that includes not only fishing, but also a complex of demographic, environmental and social changes taking place in the White Sea are discussed in the paper. Cognitive modeling is used in order to examine the possibilities of improving the living standards of the population using fisheries in the context of climate change, a market economy, anthropogenic impacts, pollution, eutrophication of the sea and other factors affecting the reproduction conditions, and living conditions population. The cognitive modeling is need to apply due to the fuzziness and fuzziness of the initial data of the system. The current problems of the fishing industry in the region, mainly due to largely various kinds of administrative barriers, features of the market economy and the imperfection of fisheries legislation briefly are described. In this work, the objective function of managing a complex socio-ecological-economic system is aimed at achieving a high standard of living standards of the population using fisheries - one of the most important activities of the local population. Conceptual modeling and the mathematical apparatus of continuous or fuzzy logic for creating a cognitive model were used.
Early Detection of Mild Cognitive Impairment using Pervasive Sensing

Aging in place and independent living for the elderly has gained importance, and so has instrumented homes for ambient assisted living (AAL). In this paper we explore the feasibility of using passive sensors to provide insights into the cognitive and physical well-being of the subject. We derive a novel clustering based tactics to check heterogeneity in terms of movement behaviour among patients, and then provide our feasibility study on detection of mild cognitive impairment based on the results of the clustering.
A Framework for Interpretable Full-Body Kinematic Description Using Geometric and Functional Analysis

Rapid advances in cost-effective and non-invasive depth sensors, and the development of reliable and real-time 3D skeletal data estimation algorithms, have opened up a new application area in computer vision - statistical analysis of human kinematic data for fast, automated assessment of body movements. These assessments can play important roles in sports, medical diagnosis, physical therapy, elderly monitoring and related applications. This paper develops a comprehensive geometric framework for quantification and statistical evaluation of kinematic features. The key idea is to avoid analysis of individual joints, as is the current paradigm, and represent movements as temporal evolutions, or trajectories, on shape space of full body skeletons. This allows metrics with appropriate invariance properties to be imposed on these trajectories and leads to definitions of higher-level features, such as spatial symmetry(sS), temporal symmetry(tS), action's velocity(Vl) and body's balance(Bl), during performance of an action. These features exploit skeletal symmetries in space and time, and capture motion cadence to naturally quantify motions of individual subjects. The study of these features as functional data allows us to formulate certain hypothesis tests in feature space. This, in turn, leads to validation of existing assumptions and discoveries of new relationships between kinematics and demographic factors, such as age, gender, and athletic training. We use the clinically validated K3Da kinect dataset to illustrate these ideas, and hope these tools will lead to discovery of new relationships between full-body kinematic features and demographic, health, and wellness factors that are clinically relevant.
Breast Calcifications Detection Based on Radiofrequency Signals by Quantitative Ultrasound Multi-parameter Fusion

Breast calcifications indicate the high possibility of malignancy in the radiological assessment of breast lesions. However, it is difficult to detect them from traditional B-mode ultrasound images due to the resolution limit and speckle noise. In this paper, we proposed a novel automatic calcification detection method based on ultrasound radio frequency (RF) signals by quantitative multi-parameter fusion. The proposed method consists of four steps: selecting the region of interest (ROI), extracting multiple features on sliding windows that traverse the entire ROI, classifying the window with or without calcifications using the Adaptive Boosting classifier, and obtaining the detection result by a threshold filter. Experiments were conducted on a database of 130 experienced doctor-proven breast tumors with calcifications. Compared to manual annotation, the proposed method achieved an average accuracy of 88%. The experiments demonstrated that our computerized RF signals feature system was capable of helping radiologists detect tumor calcifications more accurately and provided more guidance for the final decision.
Blockchain Technology in Sub-Saharan Africa: Where does it fit in Healthcare Systems: A case of Tanzania

Background: Blockchain technology is a distributed electronic ledger containing digital records, transactions or events that are protected with advanced encryptions, extremely hard to tamper, and updateable through a consensus algorithm agreeable to all connected network nodes. In Sub-Sahara Africa, the technology has started to be adopted in real estate, supply chain, agriculture, and financial sector. Unfortunately, there is a lack of effort in introducing this technology in the healthcare sector. Therefore, this study aims to explore the issues facing electronic healthcare systems in Sub-Sahara Africa taking Tanzania as a case study and introduce blockchain-based solutions for the discovered issues.Methods: The study used qualitative methods for data collection and analysis. Data were collected through interviews, observation and documentary analysis. Interviews were done with the sample size of 50 participants who were selected from groups of healthcare facility leaders, ICT experts, government representatives, doctors, nurses, laboratory technicians, pharmacists, accountants, and receptionists. Direct observation and participatory observation were used to assess different electronic healthcare records systems’ functions. Moreover, researchers used document analysis to collect data from public records (like policy manuals), personal documents (like incident reports), and physical evidence (like training materials and handbooks). NVivo 11 software was applied in managing and organizing data analysis.Results: Out of 710 healthcare facilities involved in this study, 34.5% fully implemented electronic healthcare records systems and 78% installed Mfumo wa Taarifa za Uendeshaji Huduma za Afya (MTUHA) also known as (District Health Information Software (DHIS) II). The findings showed that the issues facing electronic healthcare records are; difficulties in taking care of the patients’ private information, problems in safely sharing medical information between healthcare facilities, bandwidth issues, and improper handling of data integrity.Conclusion: The existing issues facing electronic healthcare records systems can be tackled through blockchain based solutions like self-sovereign identity and sharing and storing patients’ medical data securely in blockchain ledgers and interplanetary file system. Therefore, healthcare facilities owners are recommended to use the findings of this study to elucidate the existing issues. In the future, this study will develop the recommended systems for developing countries environment.
An Attribute Associated Isolation Forest Algorithm for Detecting Anomalous Electro-data

With the popularization of the power information collection system, the vast amount of electricity data can be collected from distribution network. It is inevitable to have abnormal data which have an effect on data analysis. This paper proposes an attribute associated isolation forest algorithm for detecting anomalous electro-data. When constructing an isolation tree, attributes are regrouped on the basis of attribute association and the method of generating partitions is changed. Using data samples to traversal the isolation forest, the abnormal data can be identified by its anomaly score and corrected by the modified wavelet neural network method. The method detects the anomalous data quickly and has high accuracy for detecting and correcting anomalous data. A dataset of electricity consumption is used to verify this method. The experimental results indicate that the algorithm has high efficiency and accuracy for the anomaly identification of power data.
A MODEL OF FOREIGN LANGUAGE TEACHERS TRAINING IN THE INFORMATION-EDUCATIONAL ENVIRONMENT OF HIGHER EDUCATIONAL INSTITUTIONS

The article addresses the expediency of creating and maintaining information-educational environment at higher educational institutions. The views of scholars on the definition of the “information- educational environment of higher educational institutions” are analyzed. The information-educational environment of Khmelnytskyi National University is described and the peculiarities of the future foreign language teachers training in the conditions of the information-educational environment of higher educational institutions are revealed. It is proved that the information-educational environment has considerable potential for improving the quality of education of the future foreign language teachers. It promotes students’ awareness of the specifics of their future professional activities, develops personal and professional qualities necessary for their future career; gives the possibility to construct individual educational paths and develop students’ creative abilities. A model of the future foreign language teachers training system in the conditions of the information- educational environment of higher educational institutions is theoretically grounded. The model contains the following blocks: target; theoretical and methodological; content and technological; control-productive. The target block of the model reveals the purpose of training and social requirements for a competitive future foreign language teacher. The target block generates other elements of the model necessary to achieve the expected result. The theoretical and methodological block includes the fundamentals of the concept of the future foreign language teachers training for professional activities; methodological approaches, didactic and specific principles. The content and technological block contains organizational and pedagogical conditions describing the main methods and forms of professional training, means and technologies for its implementation. The educational content of the model includes: compulsory and elective courses of the curriculum and practical training sessions aimed at the professional training of the future foreign language teachers. The other element of the model is the identification of stages of its implementation: theoretical and practical, correction and stabilization, reflexive and fixing. The control-productive block determines the formation of components that ensure the readiness of the future foreign language teachers for professional activity (motivational and axiological, cognitive and communicative, pragmatic and practical, functional and personal, evaluative and reflexive), criteria (motivational, educational, operational, personal, reflexive) and levels(low (reproductive), middle (productive) or high (professional).
Application of fuzzy logic in order to plan and create software for converting sound to text

The challenge of software in the field of converting sound to text is that some words have different meanings. In these situations, the functions of each one is distinguished by the use of fuzzy logic and division of the working part of the work. In this regard, the words must first be classified according to the specialized discipline. In the second place, the principles of writing should be maintained. In this regard, the points and commands and the alignment of the sentence are all items that should be considered in the software. The next item is the volume and speed of word processing and the lack of restrictions in the number of languages used. But the time problem is very important. In this regard, the use of existing codes and the optimization and remedy of the problems is a suitable method.In this research, some issues including security and maintenance of software proprietary rights, and the speed of converting sound to text and reducing noise and optimal performance of software to convert audio to text based on software method and the principles of fuzzy logic and neural network were investigated. Results showed improvement algorithm Analytical and results outcomes.
“GIVE ME A JOKE, PLEASE!”: CREATING A FUN LEARNING BY TEACHERS’ JOKES

There are lot of phenomena that often happened in teaching and learning processes. One of them is the problems faced by the students in their learning processes. For instance, they are feeling demotivated, sleepy, and bored while the processes in classroom. In order to deal with the problems, some of teachers already had a strategy in their teaching process by doing jokes. However, not all of the jokes can resolve the students’ problem effectively. Furthermore, the impacts that the students got by the jokes were less investigated deeply by prior studies. Thus, the purposes of this study are knowing the certain jokes that affect the students’ learning process and exploring the impacts of the jokes towards the process. This study then narrows the learning process into a language learning process. A qualitative study is used as an approach of this study by conducting classroom observation and interview to collect the data. The data showed that there were several jokes that able to cope with the students’ problems, and several good impacts of the jokes found in the students’ language learning processes. To sum up, the teachers’ jokes can be a way to create a fun learning which encourages the students in their language learning processes.