Reviewerly/exHarmony huggingface dataset:
Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction

Medical imaging research has long suffered problems getting access to large collections of images due to privacy constraints and to high costs that annotating images by physicians causes. With public scientific challenges and funding agencies fostering data sharing, repositories, particularly on cancer research in the US, are becoming available. Still, data and annotations are most often available on narrow domains and specific tasks. The medical literature (particularly articles contained in MedLine) has been used for research for many years as it contains a large amount of medical knowledge. Most analyses have focused on text, for example creating semi-automated systematic reviews, aggregating content on specific genes and their functions, or allowing for information retrieval to access specific content. The amount of research on images from the medical literature has been more limited, as MedLine abstracts are available publicly but no images are included. With PubMed Central, all the biomedical open access literature has become accessible for analysis, with images and text in structured format. This makes the use of such data easier than extracting it from PDF. This article reviews existing work on analyzing images from the biomedical literature and develops ideas on how such images can become useful and usable for a variety of tasks, including finding visual evidence for rare or unusual cases. These resources offer possibilities to train machine learning tools, increasing the diversity of available data and thus possibly the robustness of the classifiers. Examples with histopathology data available on Twitter already show promising possibilities. This article adds links to other sources that are accessible, for example via the ImageCLEF challenges.
Tibetan Case Grammar Error Correction Method Based on Neural Networks

Grammar Error Correction (GEC) is an important researching subject among Nature Language Processing tasks. In this work, aiming at tackling with genitive and ergative grammatical errors in Tibetan formal text, we collect 1793563 consecutive sentence pairs as training set and 5000 sentence pairs with the same distribution as well as 1159 sentence pairs in different distributions as testing sets. In our approach, we firstly preprocess Tibetan text data with compositional rules and then build a neural network architecture which is a combination of BERT and Bi-LSTM, to estimate the probability of given token being genitive or ergative. In experiments, 98.38% and 86.16% in terms of accuracy are observed respectively in testing the proposed model on two different testing sets.
An Enhance Mechanism for Secure Data Sharing with Identity Management in Cloud Computing

Today, the increasing demand of Cloud service access from Individuals as well as organizations has been increased due to advantages like availability of resources at low price, zero maintenance of infrastructure, latest updated applications, on the go resource provisioning. Along with advantages, there are various drawbacks associated with Cloud Computing like high-energy consumption, nontransparency of location of infrastructure, data security and whom to allocate resources. In this research, identity management is the most used for organizational process. The main goal of user identity management for identifying, authenticating, and authorizing for individual or groups of people. For secure data sharing to have access to application system by associating user rights and restrictions with established identities. Data sharing with identity management in cloud computing have main role of share data with privacy among identified users or groups. In this approach, Users have to first authenticate in specific IDM Server. The Service Provider will communicate to global server by privacy token approach which is also called dynamic generation along with user Id with timestamp key. In this approach consider User Privacy Preference via Token Concept The benefit of this system user can securely login and share on data to another site. Group sharing mechanism can be done as per as single User via Token Key.
Electron microscopic sequential images stitching based on belief propagation

The precise stitching of microscopic images of large-scale biological sequence slices is of great significance for the study of biological structure and function, but the slight scale changes of microscopic images and the blank areas in the images seriously affect the accuracy of mosaic. In this paper, we propose a electron microscope sequence image stitching based on belief propagation algorithm, which basically solves this problem. Firstly, the relative scale of adjacent images is calculated by extracting the sift feature points of the images. Then the global optimization model is used to obtain the absolute scale of each image, and the image is scaled to obtain the microscopic image with consistent scale. Secondly, obtain the relative displacement relationship of adjacent images by template matching method, and then the global positions of all images are optimized by Belief Propagation (BP) algorithm to eliminate the influence of blank regions and repetitive structures on the stitching results. In the case study, the proposed method demonstrates high quality.
A Fast Approach of Graph Embedding Using Broad Learning System

In this paper, traditional DeepWalk method and broad learning system (BLS) are used to classify network nodes in graph embedding, and results of classification are compared. When categorizing, DeepWalk adopts one vs rest (OvR) logistic regression method, and BLS is applied after the production of vector representations. In order to obviously compare results of the two classification methods, Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are employed to carry out the experiment on multi-label classification of BlogCatalog. The experimental result shows that F1 score of BLS is obviously higher than DeepWalk and other methods, and training time of BLS is much less than other methods. These performances make our method suitable to graph embedding.
Actor-centric Short-clip Generation System

This paper proposes a system which recognizes faces in videos and automatically generates an actor-centric short clip. First, deep learning based face recognition is performed with movies and dramas, which recognizes 1,000 Korean actors. Secondly, temporal unit segments about actors' appearance time in a video are made. Next neighboring unit segments in which the same actors appeared are merged. Finally, each actor's shortclips are generated based on the merged segment information.
Peran Guru Dalam Membimbing Kesulitan Belajar Menulis pada Siswa Kelas I di SD Negeri 1 Glagahwangi Tahun 2018/2019

This study aims to describe the role of teacher in guilding learning difficulties, the types of learning difficulties, obstacles in guilding learning difficulties in writing and solutions to overcome problems in guilding learning difficulties in writing in Glagahwangi 1 Elementary School. This type of research is qualitative research. This research was conducted on principals and first class teacher in Glagahwangi 1 Elementary School. Data collection techniques used are interviews, observatoin, and documentation. Data analysis techniques in research are carried out through steps: data reduction steps, data presentation, conclusion drawing and verification. The inquest the validity of the data is done by triangulation of techniques and resources. The results of the study indicate that: (1) The role of the teacher in guiding writing learning difficulties includes: learning resource, facilitator, manager, demonstrator, mentor, activator, and evaluator. (2) Learning difficulties in writing students include: writing beginning and spelling. (3) Barriers faced by teacher include: student attitudes in learning and paretal attitudes. (4) The solution made by the teacher to overcome problems in guilding learning difficulties in writing that is to provide additional hours of guidance and cooperate with parents.
Multi-layer Neural Network Perception for Large Data Prediction using R Programming

This review article primarily focus on neural network perceptionitechniques for large data analysis using r programming on secondary data set. Although there is a large confusion while selctioning an appropriate analysis tools for the newer researchersr when there was a large set of associative variables like CTG data sets having 2126 observations in 22 attributing variables of numeric types. The last variable NSP with categorical type 1,2 and 3 stores best, mediam and least properties. This article attempts to explore the detail analysis using the concept of multi-ilayered neural networks procedures for sample CTG data sets where each step to reach conclusion were sufficiently explained. Its purpose is to explain the simplest form of analysis when researcher meets large number of dependent and independent varialbes research data structure using software R whose results are summarized and inferenced with sufficiently with interemediate results and conclusions were drawn using appropriate  graphical interpretation were analyze using 8 hidden layers with activation of relu of 21 independent variables and loss is calculated with different  epoch  of categorical crossentropy . The each model value is evaluated with confusion matrixed prediction.  The model is considered as best fit when the two-line graphs were in constant in more iterations. The tenser flow package is widely used in image recognition, computer vision, speech / sound recognition and time series analysis and many more.
Completud débil y Post completud en la escuela de Hilbert

The aim of this paper is to clarify why propositional logic is Post complete and its weak completeness was almost unnoticed by Hilbert and Bernays, while first-order logic is Post incomplete and its weak completeness was seen as an open problem by Hilbert and Ackermman. Thus, I will compare propositional and first-order logic in the Prinzipien der Mathematik, Bernays’s second Habilitationsschrift and the Grundzüge der Theoretischen Logik. The so called “arithmetical interpretation”, the conjunctive and disjunctive normal forms and the soundness of the propositional rules of inference deserve special emphasis.
Multiview graph kernel based on popular methods

Graph classification is a challenging problem that assigns the structured data into several categories. The success of kernel methods in graph classification has aroused many designs of novel graph kernel. In this paper, we present a multiview graph kernel, a new method to combine the advantages of multiple kernel functions (Graphlet kernel and Weisfeiler-Lehman kernel). Experiments on several benchmark datasets show that multiview graph kernel could achieve significant improvements compared with the original graph kernels.
THE POTENCIAL OF USING SMARTPHONE IN TEACHING AND LEARNING OF ENGLISH LANGUAGE IN SECUNDARY SCHOOLS

This article systematically reviews the literature on the Potential of using Smartphone in teaching and learning of English Language in secondary schools. It focuses on the benefits, perceptions, challenges, and solutions towards incorporating smartphones in secondary school education. Through online research, 9 empirical and theoretical articles, case studies, and surveys defending the use of a smartphone in teaching and learning of English language in secondary schools were analyzed. The results from the study show that smartphones do have potential at improving teaching and learning of English amongst the teachers and learners in secondary schools. However, it is revealed also that there is need to establish effective pedagogical ways of using the smartphones in order to reduce indiscipline, distraction, and cases of cyberbullying.
The Correlation of E-Learning Courses toward Nursing Students’ Cognitive

B ackground : E-learning is one of the new learning methods in the Indonesian education system. This system has been used to increase students’ understanding levels. The researcher has noticed that students had a lack of cognitive level due to the misinterpretation and mis-extrapolation of the materials. E-learning shall improve students’ cognitive level in courses. The researcher is interested in exploring the correlation between the effectiveness of online classes and nursing students’ cognitive level. Objective : This study aims to identify the relationship between the effectiveness of online courses through the e-learning system and students’ understanding level at Universitas Muhammadiyah Yogyakarta. Method : This study implemented a quantitative method with a cross-sectional design along with The Pearson product-moment correlation to measure the correlation strength. Results : The researcher found E-learning adequate to be applied to nursing students of Universitas Muhammadiyah Yogyakarta (85.8% out of total respondents, N=288). The average score of the seven components measured (productivity, quality, efficiency, flexibility, eminence, development, and satisfaction) was 68.54, while the average rating of students’ cognitive level was 53.92. Conclusion : There was a significant correlation between e-learning as an education system and nursing students’ cognitive level.
Robust Guaranteed Cost Measurement Fusion Kalman Predictors with Uncertain Noise Variances

For system with uncertain noise variances, two kinds of guaranteed cost (GC) robust centralized fusion (CF) and weighted measurement fusion (WMF) Kalman predictors are presented based on minimax robust estimation principle and parameterization of perturbances of uncertain noise variances. Both the maximal lower bound (MLB) and minimal upper bound (MUB) of accuracy deviations are given. Based on the information filter, the equivalence between robust WMF and CF Kalman predictors are proved. Applying the Lyapunov equation (LE) approach, the GC robustness is proved. A simulation example uesed to tracking system shows the effectiveness of the proposed results.
Application of the Two-Frequency Radioholographic Method for Determinating the Location of Geoelectric Inhomogeneities in the Earth’s Crust

The radioholographic method is the promising tool for solving problems of ore geophysics. Holographic reconstruction of geoelectric inhomogeneities in the earth’s crust allows to effectively localize in space anomalous regions with high electrical conductivity, associated with local ore bodies, by using areal surface observations of the electromagnetic field. However, the analysis of previous experimental and model results demonstrated the possibility of “false” anomalies in the holographic reconstruction of the distribution of inhomogeneities. It was suggested to conduct observations at several frequencies as one of the ways of rejection of such “false” anomalies. So in 2017 extended field works were conducted at the site Loipishnjun in the Monchegorsk ore region by using the two-frequency radioholographic method. Results of field experimental studies have shown that the two-frequency modification of radioholographic method greatly improves the reliability of the holographic reconstruction of distribution of inhomogeneities in the earth’s crust. The comparison of results of the holographic reconstruction at different frequencies allows one to reject “false” anomalies, and to identify anomalous zones, which can be associated with ore bodies.
Consistency in Coupled Semiconductor Laser Network

We extend the concept of consistency in single laser system to homogeneous semiconductor laser network. We show that the consistency level of network is related to the intensity of drive signal and the coupling strength.
Text field extraction from camera-captured express images

With the development of domestic express business in China, there is a potential and huge need for automatic information extraction from express mail images, which however is challenging due to the skewness/folds of the captured express mail images. This paper presents a robust approach to text extraction from camera captured express mail images. Firstly, the proposed approach use a deep direct regression neural network to predict both the express bill region and type; then the form frame lines and corners are detected, with which the Coherent Point Drift (CPD) based point matching is adopted to obtain the mapping between the template and the input image. Finally, based on the mapping, accurate writing fields can be extracted. The Experiments on realistic express mail images demonstrate the effectiveness of the proposed approach.
Arquivistas, cientistas e a preservação dos arquivos produzidos pelas atividades em ciência e tecnologia

Through the results of the studies of many authors this article proposes a reflection on the characteristics of the archives produced during the activities of science and technology researches. These activities produce a great variety of documents. All these documents are considered archives. These authors demonstrate that the custody and preservation of these documents are under the responsibility of the scientists who, in general, only preserve them if they are important for the progress of the research. According to the theoretical references, the preservation of these documents is important also for the history of science and for the history of the institution. The presence of the archivist in laboratories becomes very important so that the custody and preservation processes of these documents it’s carried out according with the principle of provenance. Science and technology teaching activities also benefit from preserving the documentation produced for preparing and conducting classes. The proposal studied emphasized the need for a joint action of archivist and researches in laboratories. Studies, projects and results achieved for the preservation of the practice in science and technology for the memory of science in Brazil are also pointed out.
Analysis of Different Font Types and Sizes for High Quality Watermarked, Extracted Watermark and Medical Images Using HS Based Reversible Watermarking

This paper presents a reversible watermarking technique using gray scale medical images based on histogram shifting. The watermark image used is the medical report of the patient. To utilize the maximum embedding capacity provided by histogram shift technique, the font size and type of the medical report text can be varied. This paper explores the effect of varying font types and sizes to observe the impact on the quality of watermarked image, retrieved watermark and extracted cover image. The performance assessment is done on the basis of MSE, SSIM and PSNR. This paper compares four most commonly used fonts to discover the best suited font to achieve highest PSNR for embedded watermarked image, extracted host medical image and extracted watermark. Also the effect of salt and pepper noise on extracted medical image for different fonts is discussed.
A Novel Face Identification Implementation for Class Attendance Monitoring

Face identification has become more significant and relevant in the recent years. It is widely used for security purposes in enterprises and state-owned business since it has many advantages and benefits compared to other state of the art security applications. Previous face identification implementations inherited many different approaches and algorithms in order to overcome the challenges of recognizing an individual from a variety of angles and heights, but none of them were completely successful. The main goal of this research is to demonstrate a novel face identification framework for an autonomous class attendance monitoring system implementing SIFT (Scale Invariant Feature Transform) algorithm. An image dataset, generated with the participation of 20 volunteers that were photographed from a variety of different angles and heights, was tested with the proposed system and achieved successful results in general with reasonable accuracy rates.
LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning

The current study considers an unconventional framework of unsupervised feature selection for supervised learning. We provide a new unsupervised algorithm which we call LIA, for Label-Independent Algorithm, which combines information theory and network science techniques. In addition, we present the results of an empirical study comparing LIA with a standard supervised algorithm (MRMR). The two algorithms are similar in that both minimize redundancy, but MRMR uses the labels of the instances in the input data set to maximize the relevance of the selected features. This is an advantage when such labels are available, but a shortcoming when they are not. We used cross-validation to evaluate the effectiveness of selected features for generating different well-known classifiers for a variety of publicly available data sets. The results show that the performance of classifiers using the features selected by our proposed label-independent algorithm is very close to or in some cases better than their performance when using the features selected by a common label-dependent algorithm (MRMR). Thus, LIA has potential to be useful for a wide variety of applications where dimension reduction is needed and the instances in the data set are unlabeled.
3D object reconstruction from a single 2D image: performance of two novel frameworks based on lightweight CNN architectures and free-form deformation of meshes

In computer vision, object reconstruction is the task of inferring the 3D shape of an object based on a single or multiple 2D images. For such purpose, most common frameworks use voxel grids and point clouds. However, both of these approaches have strong limitations. On one hand, the computational cost of using voxels grows cubically as the resolution of the voxels increases. Therefore, 3D object reconstructions are usually set to low resolution. On the other hand, point clouds are unstructured in nature and the proper definition of surfaces and contours is complex. In this study, 3D object reconstruction is carried out applying free-form deformations on pre-existent 3D meshes, through two basic learning processes: template selection and template deformation. From this approach, it is possible to generate high-quality 3D object reconstructions with a lower computational cost. Concretely, two novel lightweight CNNs models are developed and tested: a multi-target learner (Model A) and depth information learner (Model B). According to the results, the performance of the multi-target learner regarding the template selection was around three times better (lower error) than in the baseline architecture, which improved the quality of the 3D reconstructions, whereas the depth-information learner showed promising results in the reconstruction of objects with complex geometry. The inherent issue of using chamfer distance as a loss measure is also examined.