Reviewerly/exHarmony huggingface dataset:
FUZZY IDENTITY-BASED DATA INTEGRITY AUDITING FOR DEPENDABLE CLOUD STORAGE SYSTEMS

Information honesty, a center security issue in solid distributed storage, has gotten a lot of consideration. Information inspecting conventions empower a verifier to productively check the trustworthiness of the re-appropriated information without downloading the information. A key exploration challenge related with existing plans of information reviewing conventions is the intricacy in key administration. In this paper, we look to address the unpredictable key administration challenge in cloud information uprightness checking by presenting fluffy personality based examining, the first in such a methodology, as far as we could possibly know. All the more explicitly, we present the crude of fluffy character based information examining, where a client's personality can be seen as a lot of spellbinding qualities. We formalize the framework model and the security model for this new crude. We at that point present a solid development of fluffy personality based inspecting convention by using biometrics as the fluffy character. The new convention offers the property of mistake resistance, in particular, it ties with private key to one personality which can be utilized to confirm the rightness of a reaction created with another character, if and just if the two characters are adequately close. We demonstrate the security of our convention dependent on the computational Diffie-Hellman suspicion and the discrete logarithm supposition in the particular ID security model. At long last, we build up a model usage of the convention which shows the common sense of the proposition.
Sophisticated Resource Orchestration and Incorporating in ATOM

The rise of Infrastructure as a Service system brings new chances that conjointly go with newdifficulties in auto-scaling, asset allotment, and security. A basic test supporting these issues is that the consistent following and observing of asset utilization inside the framework. This paper, we will in general present ATOM, A productive and compelling structure to naturally track, screen, and arrange asset utilization in an Infrastructure as a Service (IaaS) framework that is wide utilized in cloud foundation. we will in general utilize novel trailing strategy to constantly follow fundamental framework use measurements with low overhead and build up a Principal part Analysis (PCA) essentially based way to deal with perpetually screen and naturally see inconsistencies bolstered the approximated trailing results. we will in general demonstrate an approach to powerfully set the trailing limit upheld the discovery results, and that's only the tip of the iceberg, an approach to manage trailing standard to affirm its optimality underneath unique outstanding tasks at hand. In conclusion, when potential oddities square measure known, we will in general use thoughtfulness instruments to perform memory legal sciences on VMs guided by dissected outcomes from trailing and checking to spot vindictive conduct inside a VM. we will in general exhibit the extensibility of ATOM through virtual machine (VM) bundle. The execution of our structure is assessed in AN open supply IaaS framework.
ANALISIS SISTEM PENCEGAHAN BAHAYA KEBAKARAN PADA GEDUNG MUSEUM PENERANGAN DI KAWASAN WISATA TAMAN MINI INDONESIA INDAH

Museum Penerangan is one of tourist attractions which is visited by tourist to increase the knowledge of lighting development in Indonesia. Museum Penerangan has fire protection system, but the reliability of building safety system of potential fire hazard needs to be known. This research is conducted to acknowledge Museum Penerangan reliability of building safety system. Reliability variables used in this research are the completeness of treads, safety tools, active fire protective systems, and passive fire protective systems. The research obtains the value of reliability of Museum Penerangan building safety system for potential fire hazard is 68,9895, which means Moderate. This value is inadequate to minimum requirements of reliability of building safety system that has value of 80 or Good. This is due to safety tools component’s variable that has value of 58,57. It is recommended for the Museum Penerangan management to increase or develop the safety tools’ components that have been lacking of function so that the building could have a better reliability level of potential fire hazard.
Implementing School Literacy Movement through JuMaDi

This research aims to examine the implementation of one of the school literacy programs implemented by SD Al-Khodijah Merauke which had been proposed by the researcher team that is JuMaDi. JuMaDi stands for Jumat Majalah Dinding or Friday Wall Magazine. The research was conducted in SD Al-Khodijah Merauke with the research object was all students at third graders in the number of 23 students and 3 in responsible teachers for wall magazine management. The technical triangulation was used by combining disguised observation and semi-structured interview and documentation in collecting the data. Then, the data were analysed by the technique of Miles and Hubermans three steps; reduction, display, and verification. The result of the research shows that JuMaDi has been managed well by the teachers and responded enthusiastically by the students. Three points observed from the program; 1) The Form; Thematic (recount, retold narrative, poem, and letter). 2) The Procedure; theme announcement, deadline submission, selection, and publication. 3) The Result; The students participation of Recount at 1st Friday got 61% with the most topic œI Went to, Retold Narrative at 2nd Friday got 74% with the most topic œProphets Story, Poem at 3rd Friday got 82% with the most topic œMother, and Letter for Teacher got 87% with the most topic œGratitude. JuMaDi does not only encourage the spirit of literacy but also accustom students to be positively competitive.Keywords: elementary school, implementation, literacy, wall magazine, school literacy
PathAI’s Series B to Fund AI Platform for Pathology

Clinical OMICsVol. 6, No. 3 NewsPathAI’s Series B to Fund AI Platform for PathologyPublished Online:22 May 2019https://doi.org/10.1089/clinomi.06.03.12AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"PathAI’s Series B to Fund AI Platform for Pathology." Clinical OMICs, 6(3), p. 7FiguresReferencesRelatedDetails Volume 6Issue 3May 2019 InformationCopyright © GEN PublishingTo cite this article:PathAI’s Series B to Fund AI Platform for Pathology.Clinical OMICs.May 2019.7-7.http://doi.org/10.1089/clinomi.06.03.12Published in Volume: 6 Issue 3: May 22, 2019PDF download
Foreign Practices of Licensing and Authorisation in the Field of Education

Introduction. This article examines the legislative provisions of several countries regulating the right of organisations to carry out educational activities. This study aims to examine the international practices of licensing and authorisation in the field of education. Methods. The research involved uses the formal law method, systemic structural method, comparative law method, sociological/specific sociological method, and the law interpretation method. Results and Discussion. The materials collected during the study offer insights into the models of educational management used worldwide at the stage of establishing educational institutions. Licencing mechanisms in the field of education can be centralised or decentralised and can vary across the levels of education. The requirements established in a number of countries have much in common and generally apply to the staff of the educational institution, the premises, curricula, equipment, and financing. Conclusion. The findings of this study can be used by lawyers and researchers in their respective professional and research activities in the area of legislation regulating the education system.
Extractive Text Summarization for Sports Articles using Statistical Method

The past decade has endorsed a great rise in Artificial Intelligence. Text summarization which comes under AI has been an important research area that identifies the relevant sentences from a piece of text. By Text Summarization, we can get short and precise information by preserving the contents of the text. This paper presents an approach for generating a short and precise extractive summary for the given document of text. A statistical method for extractive text summarization of sports articles using extraction of various features is discussed in this paper. The features taken are TF-ISF, Sentence Length, Sentence Position, Sentence to Sentence cohesion, Proper noun, Pronoun. Each sentence is given a score known as the predictive score is calculated and the summary for the given document of text is given based on the predictive score or also known as the rank of the sentence. The accuracy is checked using the BBC Sports Article dataset and sports articles of various newspapers like the New York Times, CNN. The precision of 73% is acquired when compared with System Generated Summary (SGS) and manual summary, on an average.
Effectiveness of Using Appropriate Blended Learning Tools for Teaching and Learning Computer Programming Related Courses

With the advent of technology there are plenty of blended learning tools available for us to use in teaching and training activity. Selecting appropriate tools for a particular category of students and the nature of the subject being taught is important to achieve better academic results. Hence, the objective of this research is to assess effectiveness of various blended learning tools and to find the appropriate tool for teaching a computer -based campus in Malaysia. This subject was delivered using four selected blended learning digital tools; ED puzzle virtual classroom videos, Home works on MMLS, MMLS online Quiz and MMLS discussion board, at the beginning of Trimester 2, 2018/2019. They were asked in the middle of the trimester to rate the usefulness of the four selected blended learning digital tools in a 5-point scale using an online survey. The analysis feedback shows that D puzzle virtual the trimester. The effectivenes when the achievements of the students in terms of their academic performance were compared with previous year. It clearly shows that the academic performance of the students of year 2019 is better than of students towards student centred learning.
An online financial accounting system for Chinese colleges and universities in the Internet environment

The current backward financial management system of Chinese colleges and universities is in urgent need of transformation. An online financial accounting system for Chinese colleges and universities in the Internet environment is proposed in this paper. The system model includes user layer, front-end business layer, core business layer, business support layer and data server layer. Each layer is composed of different business modules. On this basis, a new and efficient business process has been established. Application example shows that the system is effective and helpful
Supplemental Material for Instance Theory Predicts Information Theory: Episodic Uncertainty as a Determinant of Keystroke Dynamics

a URL, doi, or other permanent path for accessing the specified information in a public, openaccess repository, and the specified information must be provided in a format that is time-stamped, immutable, and permanent.Qualifying public, open-access repositories are committed to preserving data, materials, and/or registered analysis plans and keeping them publicly accessible via the web in perpetuity.Examples include the Open Science Framework (OSF) and the various Dataverse networks.
The effect of using Scrabble Game on students’ vocabulary mastery through blended learning at the eleventh grade of SMAN 14 Bandar Lampung

This research was aimed to find out whether there is any significant improvement in students’ vocabulary mastery after using of scrabble games, and how are students’ response in learning vocabulary after using of scrabble games. The subjects of the research were 32 students of class XI at SMAN 14 Bandar Lampung. One group pre-test post-test design was used in this research. The researcher conducted the research in six meetings which consisted of try out test, pre-test, three times of treatment, and post-test. The data were analyzed by using Repeated Measure T-Test in which the significance was determined by p&lt;0.05 and hypothesis testing was computed using Statistical Package for Social Science (SPSS). Based on the calculation, the result of the research showed that the mean score of pre-tests is 77.625 and the post-test is 84.0625 in which the gain is 6.4375. The results of t-value (10.376) is higher than t-table (2.0395) and the value of two-tailed significance is 0.000 &lt; 0.05. It showed that the hypothesis is accepted that is, there is a significant improvement of students’ vocabulary mastery after the use of guessing games. The result also shows that the most improving aspect of vocabulary is verb with the mean score of pre-tests is 77.86 and the mean score of post-tests is 86.46. The improvement of the verb is 8.60 with the percentage is 33.74%. Moreover, the researcher used the open-ended questionnaire to find the students’ responses in learning vocabulary by using a scrabble game. The result of the questionnaire showed that the majority of the students work with no pressure, have some fun, and get some new information. Therefore, based on the result, it is concluded that scrabble game technique can be applied to improve the students’ vocabulary mastery.
Detection of Tumors in Ultra Sound Thyroid Images using Random Forest Classification Method

The thyroid gland is important for balancing the hormones in our body for our daily routine activity. This paper detects the tumor regions in ultrasound thyroid image using feature extractions based Random Forest (RF) classification approach. In this paper, Curvelet transform is used to transform the pixels associated with spatial into the pixels associated with frequency. In this paper, Random Forest (RF) classification algorithm is used for the classification of the computed features from the thyroid image.
IsoDAR

The Isotope Decay-At-Rest (IsoDAR) experiment is a short baseline neutrino experiment designed to measure neutrino oscillations by placing a high flux electron anti-neutrino source near a kiloton scale scintillator detector.A high current proton beam will be used to produce the large number of electron anti-neutrinos needed.Reaching the design goal of 10 mA of 60 MeV protons on target requires advancements in accelerator technology.This can be achieved using a high intensity H + 2 ion source followed by a radio-frequency quadrupole and spiral-inflector to axially inject ions into a compact cyclotron.Presented here are the results of our latest studies of this injection system.
LISSA Newsletter

News and events from SLIS student group: Library and Information Science Students Association (LISSA).
Enhanced Techniques for Opinion Mining

The exponential growth of online information sharing has led to the need to analyze them. Opinion Mining or Sentiment Analysis incorporates a systematic approach to favor analysis of the available online synthesized data .This paper includes a lexicon based approach towards analysis. The proposed SLN (Slang Negation) Algorithm follows a lexicon based approach to Opinion Mining. The slang lexicon provides the expansion of the acronyms and their scores. Three types of negations namely explicit, implicit and pseudo negation are handed. Slang and negation handling are necessary to support semantic orientation. The proposed SLN algorithm has shown significant improvement in the precision, recall, f-measure and accuracy values as compared to the use of sentiment lexicon approach.
A Brief Review of Automatic Rule Generation

Automatic rule generation can be regarded as rule generation techniques, and it can be decomposed into rule induction and rule-base optimization.As the aspect of the size of the system, it just need to work rule induction in a simple or a small system with a few variables in the relative database without processing the step of rule-base optimization, because the performance of rule-base optimization is almost achieved by the step of rule induction in a simple or a small system.So far as the size of work space, the family of shared partitions is suitable to work in a small size work space with a good convergence, and the family of clustering is appropriate to work in a large size work space with a good convergence.However, the family of shared partitions is easy to make the problem of curse of dimensionality when dealing with large systems.
An Imaging Based Lung Tissue and Spread Level Detection for Early Detection of Cancer

A tissues in the human organs is mainly due to the disease. The spread of tissue indicates the increase in disease level. The tissues and level of tissue in the organ may require for physician assessment. The proposed work is used to detect the tissue level in lungs. An early detection of tissue may leads to detect the cancer cell. The RGB color input image is converted into gray scale image. The gaussian noise is applied on the gray image. The median filter is applied to project the tissue pixels. The canny edge detection is applied on the filtered image to detect the boundary regions. The gradient magnitude operation is performed to project the edges of tissue. A watershed transform is applied on the gradient image to perform the morphological operation. A morphological area open and area close operation is performed along with reconstruction which highlights the tissue area from the lungs. Super imposing morphology with the regional maximum pixel operation is performed to differentiate tissue pixels. The tissue area is retained and all other area pixels are replaced by logic ‘0’ pixels using Otsu’s global thresholding method. The tissue portion is cropped using the OAD regions.
Research on Privacy Preserving Models for Efficient Healthcare Big Data Sharing in Cloud

Healthcare data is highly confidential and thus sharing of those data is complex. But to diagnose a patient, the professionals need to access their healthcare data. Those data will be in the form of Electronic Medical Record (EMR) which includes multimedia data like X-ray, Scan and ECG. Size of the EMR is rapidly growing thus it is to be stored in format of Big Data. Major issue in Big Data is privacy, as EMR is taken into account a tiny change in data could create a larger impact. Data theft attack is considered to be the serious security breaches of Big Data. On the other hand, limiting the access of EMR must not restrict the data flow within the authorized users.
SMS Spam Detection using Tokenization and Feature Engineering

The enormous development of innovation and mobiles, the clients have been exposed to more spam messages than any other time in recent memory ever. SMS spam separating is a nearly an exceptionally ongoing answer for arrangement with such a significant issue.. This paper moves us to chip away at the assignment of separating versatile spam messages as whether it is Ham or Spam for the clients by adding messages to the worldwide accessible SMS dataset. The paper plans to break down various AI classifiers on huge corpus of SMS messages for the individuals around the globe. This paper also informs or tells the readers about the existing algorithms and it’s inefficiency in filtering the ham messages from spam messages. This paper makes use of tokenization to create tokens which are then fed into the feature engineering model to extract features and then to predict the outcome.
Computational Mining on IBICT BDTD’s Thesis and Dissertation Metadata for Supporting Social Science Research

The Brazilian Digital Library of Thesis and Dissertations (BDTD) provides essential data to support many social sciences investigations. Nevertheless, there is still a lack of computation tools tailored for helping extract and analyze the necessary information from the BDTD library. In this paper, we discuss the development of computational solutions to answer questions from a particular social sciences research using metadata from BDTD. The solutions involve the integration of data processing and presentation techniques, such as string-processing algorithms, knowledge graphs and information visualizations. All programming codes implemented at the scope of the project are available for helping other researchers. The paper also highlights the importance of having researchers from Social Science and Computer Science working together, what motivates future collaborations in these areas.
Optimization with ADAM and RMSprop in Convolution neural Network (CNN): A Case study for Telugu Handwritten Characters

Handwritten character recognition is the prime and challenging research area in the area of image processing.This recognition can be defined as the ability of a computer to take the input as handwritten input from various primary sources like paper documents, photographs, touch screens and other devices which are both online and offline.Although a large extent of work has been done in many languages to name a few English and Asian languages such as Japanese, Chinese etc. for handwriting recognition and less amount of work was done on Indian languages like Hindi, Tamil, Telugu, and Kannada etc .This paper explains the methodology for telugu handwritten character recognition algorithm using two optimizers namely Adaptive Moment Estimation (ADAM) and Root Mean Square Propagation (RMSprop) implemented in Convolution neural network (CNN) which has high recognition, accuracy and minimum training and classification time.CNN is dynamic tool to overcome the limitations which had come across in the basic machine learning approaches.To construct the proposed model, we have built our own data set for telugu character (Ka) taking its gunintham as different samples of input.It is observed that RMSprop optimizer outperforms with 89% accuracy compared with ADAM optimizer..
From wet planet to red planet

Current and future exploration is shaping our understanding of how the climate of Mars changed. Joel Davis deciphers the planet’s ancient, drying climate. Davis, J., From wet planet to red planet. Geoscientist 30 (11), 14-20, 2020 https://doi.org/10.1144/geosci2020-121; Download the pdf here