Reviewerly/exHarmony huggingface dataset:
P.105 The “Comprehensive 3D Skull Base Lab”-- enhancing resident education with virtual/augmented reality and 3D printing at Northwestern University

Background: Due to increasingly limited access to cadavers and first-hand operative experience, there is an increasing need for innovative modalities in neurosurgical education. Recent developments in computer modelling, virtual/augmented reality, and video game technology have created new opportunities for novel teaching tools. We set out to develop a library of 3D models of normal anatomy and pathologic states for use in conjunction with an interactive simulated environment and 3D printing for teaching of neurosurgical residents. Methods: Anatomically accurate 3D models were developed using CT/MRI data from multiple patients using open source segmentation and 3D animation software. An interactive simulated environment was then created using a 3D game engine and used in conjunction with a virtual/augmented reality system. 3D models were also used to for 3D printing. Results: 3D models and an interactive simulated environment were used in conjunction with various viewing modalities, including 3D video, 360 video, and virtual reality headsets, as well as 3D printing. These teaching tools were successfully implemented in neurosurgery didactic teaching sessions and in the skull base lab. Additional benefits were seen with patient engagement and marketing. Conclusions: 3D modeling and animation show considerable promise for neurosurgical education, with additional benefits for patient engagement, marketing, and social media.
Statistical Model Based Digital Image Retrieval and Fragile Watermarking

The objective of this thesis is to acquire abstract image features through statistical modelling in the wavelet domain and then based on the extracted image features, develop an effective content-based image retreival (CBIR) system and a fragile watermarking scheme. In this thesis, we first present a statistical modelling of images in the wavelet domain through a Gaussian mixture model (GMM) and a generalized Gaussian mixture model (GGMM). An Expectation Maximization (EM) algorithm is developed to help estimate the model parameters. A novel similarity measure based on the Kullback-Leibler divergence is also developed to calculate the distance of two distinct model distributions. We then apply the statistical modelling to two application areas: image retrieval and fragile watermarking. In image retrieval, the model parameters are employed as image features to compose the indexing feature space, while the feature distance of two compared images is computed using the novel similarity measure. The new image retrieval method has a better retrieval performance than most conventional methods. In fragile watermarking, the model parameters are utilized for the watermark embedding. The new watermarking scheme achieves a virtually imperceptible embedding of watermarks because it modifies only a few image data and embeds watermarks at image texture edges. A multiscale embedding of fragile watermarks is given to enhance the embeddability rate and on the other hand, to constitute a semi-fragile approach.
Feature Extraction and Processing Analysis in Speech Recognition

The difficulties with automated identification and synthesis of various speech patterns have become significant research issues in recent years. Stressinduced speech characteristics were compared to normal speech in a feature analysis. Due to stress, the performance of Stressed speech recognition decreases substantially. In the speech communication system, the voice signal is transmitted, stored, and processed in a variety of ways. The speech signal must be delivered in such a way that the information content may be easily extracted from human listeners or machine automation. To enhance speech recognition performance, a stressed compensation method is employed to compensate for stress distortion. To identify different moods in speech signals, these features are collected and assessed in English. The variations in glottal excitement of common speaking patterns are examined in depth in this article. The sinusoidal model effectively describes the different stress classes in a speech signal, according to the results. When it comes to detecting emotions in a pressured speaker, sinusoidal features outperform linear prediction features.
it is necessary to iterate the classifiers at least 10 times and report the mean and standard deviation values to avoid any biases induced in the training data.

Thank for this comment which allowed to improve the statistical significance of the work.For the process to achieve statistical confidence, classifiers are now iterated 100 times in 10 separate sub-sample data sets.We report the mean values and standard deviation in the manuscript (see section 3.-result) for each of the 10 training sample subgroups (see tables en figure 1 and2).To evaluate the effect of training sample sizes as well as the performance of the classification algorithms on classification accuracies, we randomly divided the training sample data into 10 different data sets.The procedure
Sistem Pergerakan Robot Kiper Beroda Menggunakan Metode Wall Follower Berbasis Image Processing

One of Indonesian Robot Contest divisions is the Indonesia wheeled soccer robot contest. There are three players called the striker, defense and goalkeeper robot, which is drived by wheels that controlled based on three aferomentioned positions. This study aims build the goalkeeper robot equipped with image processing to detect the ball using a camera sensor that installed in the the robot system. The Image processing contructed using the python programming language with OpenCV library. The results of image processing are used as input data that controlled by Arduino Mega 2560, which is connected serially to the PC's USB port. The results shows the maximum linear velocity that can be achieved is 1.59 m/s. Furthermore, the efficiency ratio of analysis data to the actual distance is 86.77 %
Effective Feature Selection Strategy using Manova Test

Feature selection is the most important step to develop any latest learning model. As the complexity of the leaning models increases day by day there is an increasing demand, in selecting the right features to build the model. There are many methods for feature selection. A new feature selection based on the Manova statistical test is implemented. Using the Manova test, we select attributes from academic datasets. Using the selected attributes, we build a classification model. Accuracy of the model with feature selection is compared with a model with all attributes. Results are discussed. It is proved that the classification model build with features selected by Manova test achieves more accuracy than a model built with all features.
Pathology of Students' Cell Phone Text Messages (Case study: Saghez's Payame Noor University)

Modern communications technologies have opened widespread and diverse communication channels for people, and cell phones are among the phenomena, which in the late twentieth century and via transforming the human life, have had diverse individual and social impacts.Text message as one of the applications of cell phones as well as their growing trend among student users within an academic population requires to be explored.In this regard, the current research sought to analyze students' cell phone text messages.In order to arrange and configure the theoretical framework, theories of Satisfaction and Use by Engelhard, Habermas, Maccquail, Windall and Humens were used in a combined form.The research was conducted through a random sampling method and via a survey technique and by using a researcher made questionnaire among Payame Noor University students.The validity of the research's questionnaire was confirmed by the nominal and construct validity while its reliability was supported by Cronbach's alpha.The sampling method was simple random and the sample volume was 269 people.Data gathered were provided in two descriptive and inferential parts.Descriptive data indicated that in the sample population, the single, unemployed and Humanities group accounted for the highest percentage.Text messages, taking photos, Bluetooth, and filming were among the applications that cell phones would offer to students.The highest percentage of the sample population were found to send 150 messages and receive 41-60 messages per week.Non-academic friends were among the major receptors of messages.According to inferential results of the research, hypotheses pertaining to age, marriage status, job, motivation and reciprocal reaction, identity and satisfaction as well as sending messages were confirmed while other hypotheses were rejected.
Accomplishing Data Integrity and Confidentiality in Data Markets

As a noteworthy business worldview, a few on-line information stages have developed to fulfill society's wants for individual explicit learning, any place a service provider assembles raw data from data givers, at that point offers data services to data clients. Notwithstanding, inside the data exchanging level, the data customers face a squeezing issue, i.e., an approach to confirm whether the service provider has actually gathered and handled data. During this paper, we propose TPDM, that effectively compose truthfulness and Privacy protection in data Markets. TPDM is structured inside in partner degree Encrypt-then-Sign way; utilize mostly homomorphism encryption and identity-based signature. It along encourage bunch confirmation, processing, and result check, though giving identity protection and data confidentiality. We used dataset and 2015 RECS dataset, severally. Our examination and investigation results that TPDM accomplishes numerous alluring properties, though obtaining low calculation and correspondence overheads once sustaining huge size data markets.
APLIKASI TASIBAR (PENDAFTARAN SISWA BARU) PADA SD NEGERI CIKAMPEK SELATAN I-KARAWANG

Abstract&#x0D; The era of global competition in education is required to develop rapidly, actively and dynamically.We face the challenges needed fundamental changes in the world of education itself so that the humanresources generated will become human resources that are able to adapt to the times. Realizing that,SD Negeri Cikampek Selatan I as primary school felt the need to implement a new computerizedstudent enrollment system as part of the change that replaced the new student registration system(conventional), because the computerized system can accelerate and simplify the process andminimize the mistakes that there, for that the author designed the New Student EnrollmentApplication Program on State Elementary School Cikampek Selatan I-Karawang, so that theprogram is made in accordance with the needs of the new student registration system that alreadyexist, the authors make the design of this program ranging from input documents containing theprocess of filling the form of prospective students, the process of entering student data, the processof entering the data Parents, the process of entering user data and output documents containing newstudent cards and student reports.
Conclusions

In this book, the fundamentals of cryptography are discussed. It provides a comprehensive study of the three critical aspects of security: confidentiality, integrity, and authentication. As it is known, cryptography plays a vital and critical role in achieving the primary aims of security goals, such as authentication, integrity, confidentiality, and no-repudiation. Cryptographic algorithms are developed in order to achieve these goals as discussed during this book. Moreover, this book discusses the fundamentals of private and public key cryptography. Moreover, it explains the details of the main building blocks of these cryptographic systems. Besides, this chapter explores the different crypto-analysis techniques. It addresses stream ciphers, DES and 3DES, AES, block ciphers, the RSA cryptosystem, and public key cryptosystems based on the discrete logarithm problem, ECC, key exchange algorithms, and so many other algorithms. Moreover, this chapter provides a comparison between different encryption algorithms in terms of speed encryption, decoding, complexity, the length of the key, structure, and flexibility. This book explores different cryptography concepts such as authentication, integrity, availability, access control, and non-repudiation. It presents concepts of digital signatures, hash functions, and MACs. Finally, this book discusses the cutting-edge cryptographic techniques such as quantum cryptography, DNA cryptography, chaotic cryptography, lightweight cryptography, and blockchain cryptography. All these cryptography techniques are promising and rapid emerging fields in data security.
Developing a consequence management assessment framework

This research developed an assessment process framework and tool to assess national consequence management capacity.The research applied a value focused thinking and the Systems Design Process (SDP) to create a framework that effectively analyses a nation's capacity to handle extreme crisis situations to include Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE) events.The research conducted extensive stakeholder analysis to identify critical metrics in identifying a nation's capacity and capabilities.Weighting and scoring of the value metrics was applied to develop scoring assessments.The results provided a usable assessment model that tracks capacity progress over time.The model provides a value contribution to the development of an effective assessment tool and achieves the goal of providing a practical tool for assessment.This research takes assessing a very difficult and complex system and makes it relatively easy and simple, while providing solid evidence of capacity improvement or deterioration over time.
DEVELOPING TRADITIONAL GAME BASED ENGLISH LEARNING MEDIA TO GROW LEARNING INTEREST OF JUNIOR HIGH SCHOOL STUDENTS

Abstract: The background of conducting this reseach began with the intention to develope an approprite English learning media for Junior High School students in the rural area by utilizing the traditional games. The seventh grade students of SMP N 1 Mendobarat, Bangka Regency academic year of 2018/2019 were involved as the population with 30 students as the samples taken by using purposive random sampling. In this Research and Development Research (R&amp;D), the data were obtained from the distributed pre-test and post-test questionnaires. The procedures applied in this research were the one adopted from Lee and Owen (2004) with the following steps; analyzing students’ need by distributing the need analysis questionnaire, designing English learning media according to the data of need analysis, developing the first draft of the learning media, asking experts’ judgement, and validating it. Two kinds of test which were Alpha testing that involved material and content experts, and Beta testing that involved students were applied in the validation phase. Based on the finding of the research, it was found that the traditional games based English learning media was feasible to use by the seventh grade students of SMP N 1 Mendobarat, Bangka Regency. Therefore, this research leads to the recommendation to the students and teachers to consider traditional games as a feasible media to learn English.Key words: Traditional Game, Learning Media, English
IAG Membership Information

Geostandards and Geoanalytical ResearchVolume 44, Issue 2 p. 399-399 IAG Membership InformationFree Access IAG Membership Information First published: 11 May 2020 https://doi.org/10.1111/ggr.12273AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume44, Issue2June 2020Pages 399-399 RelatedInformation
Recommendation System

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collab- orative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommen- dations. The novelty of our proposed approach is that it provides personalized recommen- dations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.
Privacy Preserving In Data Mining with No Data Loss with a Combinational Scheme

Large amounts of data collected by many organizations under-goes data mining for various purposes like analysis and prediction. During data mining tasks, the sensitive information may be losing its privacy. Hence, Privacyprotection or preservation is becomes major issue for the organizations. Publishing data or sharing information for mining with Privacypreservation is possible through Privacypreserve data mining technique (PPDM). Existing techniques are not able to withstand for some attacks and some suffers with data misfortune. In our paper we conventional an effective and combinational approach for security safeguarding in information mining. Our approach with can withstand from different kinds of assaults and limits data misfortune and increases data re-usability with data reconstruction capability
THE USING OF WHATSAPP IN TEACHING PRONUNCIATION PRACTICE TO THE THIRD SEMESTER STUDENTS OF TADRIS ENGLISH STUDY PROGRAM IN IAIN PALU

This research focuses on the using of WhatsApp in teaching pronunciation practice to the third semester students of Tadris English Study Program in IAIN Palu. The research question is “How is the using of WhatsApp in teaching Pronunciation Practice to the third semester students of Tadris English Study Program in IAIN Palu?” This is a qualitative research by using two instruments of data collections namely questionnaire and interview. The participants consisted of 19 third semester students of Tadris English Study Program of State Institute for Islamic Studies (IAIN) Palu. The researcher conducted five meetings of Pronunciation Practice class to the participants via WhatsApp before distributing the questionnaire and interview sheet. The questionnaire consisted of five questions by applying Likert Scale, while the interview consisted of ten questions. From the questionnaire and interview, the researcher concludes that the using of WhatsApp in teaching Pronunciation Practice to the third semester students of Tadris English Study Program in IAIN Palu is practical. The students could understand the material shared via WhatsApp in the form of documents shared, videos shared, as well as voice recording shared. They could use the features in WhatsApp well such as sharing documents, voice notes, sharing videos, video call and so on in learning pronunciation practice. The students like Pronunciation Practice Class and enjoy studying it via WhatsApp. Learning Pronunciation Practice via WhatsApp has some advantages such as time flexibility as well as structured and easily accessed materials. However, it also has some limitations such as internet data availability and video call limitation. Besides, the participants faced several obstacles in learning Pronunication Practice via WhatsApp, such as the limitation of smartphone memory to save files, down internet server, and unavailability of internet data. Nevertheless, they could passed those obstacles well by deleting some unused files to give more space to the memory and finding out wifi connection to stay connected to WhatsApp when the server was down or they ran out of internet data. They could understand the teaching of Pronunciation Practice via WhatsApp by downloading and reading the documents shared, the videos shared, and the voice recording shared by the lecturer. When they found a difficulty, they asked their friends or the lecturer via WhatsApp group.
Homomorphic Encryption – Need of the Hour

Every common day the headlines of the newspaper are buzzed with data leaks and mobbing of data. The hackers are considering human just as a moving data. As the technology is advancing, we are more prone to cyber-attacks. With the ease of advancement there are certain cons which are approaching towards us. To protect the individuality of a human being, few governments of different countries have taken a step ahead to demoralize the illegal access to data hence inhibiting security. Homomorphic encryption is the trend or technique by which data can be protected as the encryption is done on the ciphertext. In this paper different types and techniques of homomorphic encryption is discussed. The techniques provide a secure environment for data to run on any application.
AN INTELLIGENT ROLLING BEARING FAULT DIAGNOSIS METHOD OF CNN BASED ON MNIST DATABASE OF HANDWRITTEN DIGITS

An intelligent rolling bearing fault diagnosis method of convolutional neural network (CNN) based on MNIST database of handwritten digits was proposed.The equal length interception of the vibration signal of the rolling bearing was performed, and the intercepted signal was reconstructed into two-dimensional pictures in sequence.In order to ensure the authentic and effective restoration of the fault characteristics of the vibration signal, no preprocessing was performed on the vibration signal, and direct interception was performed to reconstruct the twodimensional pictures.To realize intelligent fault diagnosis of rolling bearings, the failure pictures were input as feature maps, and a CNN classifier model based on MNIST database of handwritten digits was established.In order to verify the validity of the rolling bearing fault diagnosis method proposed in this paper, the open-end rolling bearing data from Case Western Reserve University and the rolling bearing data collected under laboratory conditions were experimentally verified respectively.The experimental results were then compared with the traditional BP neural network, BP neural network optimized by particle swarm optimization, wavelet energy entropy support vector machine optimized by particle swarm optimization, support vector machine based on variational mode decomposition mutual approximation entropy and BP neural network optimized by genetic algorithm on the effectiveness of fault pattern recognition.The results show that this method can identify effectively the fault type of the rolling bearing, and the diagnosis efficiency is higher.The method has strong feature extraction and recognition capabilities.