Reviewerly/exHarmony huggingface dataset:
Improving Cloud Security and Privacy Using Blockchain

In recent years, securely sharing personal information between two parties involves high risk. Most of the medical health records and financial transactions includes huge uncertainty while storing and retrieving from cloud for query processing. Blockchain is an open platform where each transaction is tampered proof. Various methods like zero knowledge proof or hashing methods used to hide the sensitive information from the real world. When associating blockchain with cloud this uncertainty is reduced. The user information is segregated into two categories sensitive and non-sensitive using linear regression method before processing in cloud. To improve security and increase privacy from various attacks, the sensitive part of data is encrypted using ECC and non- sensitive part of data is encrypted using RSA algorithm. Using Ethereum blockchain the policy of the user is verified and query processing is done. The performance of the model is compared with the existing techniques and results are evaluated using the classification error rate and performance of security against manual attacks.
Privacy-preserving data deduplication in edge-assisted mobile crowdsensing

Mobile crowdsensing enables the collaborative data collection between mobile workers and centralised cloud server. When sensing data from the surrounding environment, workers in the same location may generate the identical data report. Although edge intelligence is integrated to remove the redundant data by comparing the report content, disclosing the sensing data to the edge nodes results in severe privacy leakage. To detect and remove duplicated data without revealing the content, encryption-based data deduplication schemes are the main solutions. However, the existing schemes have high computational cost due to heavy cryptographic primitives. In this work, we propose a pairing-based data deduplication scheme with lower computational cost. The proposed scheme guarantees both secure data deduplication and secure contributor identification. In addition, by deploying proxy re-encryption, the privacy of task location is preserved. The experimental results demonstrate that the proposed scheme achieves better computational efficiency than the other schemes.
German Professional Vocabulary Memory Big Data Intelligent Classification Method Under the Background of Cross-Border E-Commerce

With the existing big data intelligent classification algorithm for German professional vocabulary, the vocabulary classifier constructed has difficulty distinguishing the sample set in the hyperplane, resulting in large errors in the vocabulary classification results. To improve the accuracy of German professional vocabulary classification results, an intelligent classification method for German professional vocabulary memory big data is designed based on the cross-border e-commerce background. Calculate the data structure similarity of German professional vocabulary, extract the normalized information of German professional vocabulary, establish a vocabulary classifier based on memory big data, and design an intelligent classification algorithm of memory big data. The experimental results show that the accuracies of vocabulary classification obtained by this method in the four datasets are 98.7%, 99.1%, 97.8% and 99.4%, which are higher than those of the other three algorithms. From the above data, it can be seen that the German professional vocabulary intelligent classification algorithm has higher accuracy and achieves the optimization goal.
Implementasi Metode Multi-Objective Optimization On The Basic Of Ratio Analysis (MOORA) Dalam Menentukan Penilaian Kinerja Guru Pada Sekolah Menengah Atas

This study aims to determine the assessment of teacher performance using a decision support system and to implement the Multi-Objective Optimization Method On The Basic Of Ratio Analysis (MOORA), in completing teacher performance assessments in high schools. This data was obtained through field tests and literature. The process of direct data collection in the form of interviews and documentation. The system development method used in this research is the Rapid Application Development (RAD) method. The RAD method is a system development method with a relatively short processing time. The results of the research from the implementation of the MOORA method in determining the performance assessment of high school teachers can be applied with the results of the questionnaire assessment of 81.54% or including very good.
Effects of Short Messaging Services' Language on the Academic Writing Skills of Graduate Learners at University Level

Short messaging service (SMS) language, text speak or text language is used with internet-based communication such as text messaging or e-mail and instant messaging. The current study aims at determining the reasons for short messaging service (SMS) language usage and its effects on the academic writing of the learners. Eighty (40 male and 40 female) learners and ten teachers were purposive a selected as participants of the study. The data were amassed from two public sector universities of the Punjab, Pakistan. Two questionnaires, one for teachers and the other for learners,were administered as research tools for the data collection. The collected data were analyzed through SPSS. The majority of the teachers viewed that the SMS language usage had an undesirable effect on the pedagogic inscriptions of the university learners. Learners opined that the SMS language was an easy and fast mode of writing through they committed errors of punctuation, spelling, and syntax.
Peer review declaration

The 1 th International Conference Science Physics and Education (ICSPE 2021) All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind Review • Review Criteria: All submitted papers received by the Conference Committee will be to determine whether they are properly prepared and meet the scope of conference. Paper that do not fit the meet the standards of the conference will be rejected before peer-review. Once a paper passes the initial checks, it will be continued to similarity check (turnitin) and assigned to at least two independent experts for peer-review by single-blind review. Peer review comments are sent to the author for revision or accepted/decline the article with reviewer’s name is confidental. All the papers go through the peer-review process and receive at least two reviews by three main criteria (Technical Criteria, Quality Criteria, and Presentation Criteria); then the decision which will be one of the following: Accepted: The paper is accepted and ready to publish without any flawed Accept after Minor Revisions: The paper is in principle accepted after revision based on the reviewer’s comments. Authors are given one week for minor revisions. Reconsider after Major Revisions: The acceptance of the paper would depend on the revisions. The author needs to provide a point by point response or provide a rebuttal if some of the reviewer’s comments cannot be revised. Authors will be asked to resubmit the revised paper within a suitable time frame, and the revised version will be returned to the reviewer for further comments. Reject: The paper has serious flaws, and/or makes no original significant contribution. No offer of re-submission to the journal is provided. • Conference submission management system: Submission conference using the website that is https://icspe.unram.ac.id/registration/ • Number of submissions received: 74 • Number of submissions sent for review: 72 • Number of submissions accepted: 53 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 72% • Average number of reviews per paper : 2 reviews per paper • Total number of reviewers involved: 24 reviewers • Any additional info on review process (eg Plagiarism check system): All submissions were checked for plagiarism with Turnitin and Grammar checking by Grammarly • Contact person for queries (Full name, affiliation, institutional email address) Assoc. Prof. Drs. Aris Doyan, M.Si., Ph.D University of Mataram aris_doyan@unram.ac.id
Penerapan Binary Particle Swarm Optimization Support Vector Machine untuk Klasifikasi Komentar Cyberbullying di Instagram

Freedom of speech on social media is sometimes inappropriate with the ethics of communicating and has led to cyberbullying. Instagram is the most commonly used social media in cyberbullying. Cyberbullying needs to be minimized because it has many adverse effects. One way that can be done is by identifying cyberbullying comments so those comments can be deleted automatically. The method used in this study is text classification using Support Vector Machine (SVM) algorithm with the application of Binary Particle Swarm Optimization (BPSO) optimization method as features selection. The study aims to build a cyberbullying comments classification model and compare the classification model performance with and without the application of features selection. The experimental results showed that modeling with SVM produces a reasonably accurate classification performance over 72% for all classification performance on each C. The application of BPSO for features selection can improve classification performance by increasing accuracy and specificity. However, the model without features selection on C = 0,1 is chosen in this study case because it has the highest sensitivity with good accuracy and specificity that can detect cyberbullying comments more accurately.
An Ensemble Model for Face Mask Detection Using Faster RCNN with ResNet50

The computer vision which is an important aspect of Artificial Intelligence. The object detection is the most researchable area with deep learning algorithms. Now in the current COVID – 19 pandemics, the social distancing is a mandatory factor to prevent this transmission of this deadly virus. The government is struggling to handle the persons without wearing masks in public places. Our work concentrates on the object detection of face masks using the state-of-the-art methodologies like YOLO, SSD, RCNN, Fast RCNN and Faster RCNN with different backbone architectures like ResNet, MobileNet, etc. This paper brings out various ensemble methods by combining the state of art methodologies and compare those combinations to identify the best performance, in choice of the dataset of the application. We have obtained the highest performance benchmark with the usage of Faster RCNN – ResNet50 among the other ensemble methods. All the performance evaluation metrics are compared with one other with the same face mask detection image dataset. In this paper, we present a balancing collation of the ensemble methods of object detection algorithms.
Ubiquitous Power Internet of Things and IPv6 Technologies in Wireless Access Terminals and Intelligent Meter Reading Applications

Under the background of the rapid development and innovation of modern information technologies such as communication technology, artificial intelligence and Internet of Things technology, power grid form is also accelerating towards information generalization and intelligentization. The ubiquitous power Internet of things and IPv6 technology, which can better adapt to the era of intelligence, have become the solid technical foundation of smart grid. Ubiquitous power Internet of Things and IPv6 technology play a crucial role in the whole power system. The combination of the two can effectively solve the problems of slow response time and heavy workload of personnel inspection caused by various production systems in the current substation due to their respective independence. Combining the two into wireless access terminal and intelligent meter reading is an important milestone for power communication network to realize Internet intelligence. This research through analyzing the characteristics of the power of things and IPv6 technology, is proposed based on the combination of low voltage electrical equipment monitoring method, by improving their communication unit hardware circuit, expand the network communication protocol, and discusses the typical application scenarios analysis, case study, according to the results of the test data capacity, freeze the success rate reached 99.93%, crosstalk between each meter is strong, and 100% success rate, verify the advancement and the effectiveness of the argument in our study. This study also gives Suggestions on the future networking application of the ubiquitous power Internet of Things, advocates the promotion of intelligent terminals, and strives to promote the deep integration of power network and information technology, which again shows that this study is forward-looking and advanced.
Research on Sbstation Method based on Intelligent Evaluation Technology

With the rapid development of artificial intelligence technology, deep learning, data mining and other technologies are widely used in practical work such as text analysis and image processing, which makes the holographic whole process intelligent evaluation of substation operation technically feasible. This paper intends to apply the above advanced technologies to the automatic analysis of operation tickets, personnel safety, intelligent management of tools and instruments and personnel real-time positioning technology, so as to realize the identification and evaluation of personnel behavior. In order to judge and score the personnel's operation tasks, the standard process of the operation should be monitored, recorded and compared through multiple dimensions such as personnel operation code of conduct, equipment operation accuracy, cross-border operation, use of tools and instruments, on-site recovery after operation and so on.
THE LITHOLOGICAL AND STRUCTURAL FEATURES CONTROLLING THE RADIOACTIVE MINERALIZATIONS IN YOUNGER GRANITES OF UM GURUF AREA, NORTH EASTERN DESERT, EGYPT

Um Guruf area in the North Eastern Desert (NED) includes granodiorite, Dokhan volcanics, Hammamat sedimentary rocks, younger granites and post granite dykes.The study area is subjected to four main sets of joints trending NE-SW, ENE-WSW, E-W and N-S.All joints in the area are of tension stress.The main faults are of NW-SE, NNW-SSE and NE-SW trends based on their number proportion.All faults in the area are of compression stress.NE-SW trend is the master trend controlling the structural and tectonic framework of Um Guruf area followed by ENE-WSW trend.The younger granites show higher radioactivity than that of the other rock types.Radioactive anomaly is recorded in zoned pegmatite pocket in Hmrat El Sorwhyia alkali feldspar granites.It contains U and Thbearing minerals of uranophane, thorite and zircon in addition to apatite, sphene and iron oxides which capture U and/or Th elements.The radioactive anomaly is structurally controlled by two strongly hematized and silicified joints striking NNE-SSW and NW-SE and two faults trending NNW-SSE and WNW-ESE.
Machine Learning-Based Technique for Image Classification and Identification of Similarity for Commonness of Source

In India Every year RBI (Reserve bank of India) faces the issue of fake currency. Fake Currency has consistently been an issue that has made a lot of chaos in the market. The expanding mechanical progressions have made the opportunities for making progressively fake currency which is circled in the market which decreases the general economy of the nation. There are machines present at banks and other business regions to check the validness of the monetary forms. Be that as it may, a typical man doesn't approach such frameworks and henceforth a requirement for a product to distinguish counterfeit cash emerges, which can be utilized by average folks. This proposed framework utilizes Image Processing to identify whether the currency is real or fake. The framework is structured utilizing Python programming language and OpenCV. It comprises of the means, for example, grayscale detection, edge detection, Highlight Extraction, and so forth which are performed utilizing reasonable strategies. And which will be further implemented in the Framework for Classification and Identification of Similarity for Commonness of Source
Einführung in R und RStudio

No AccessMaschinelles Lernen mit RJan 2022Einführung in R und RStudioUli SchellUli SchellSearch for more papers by this authorhttps://doi.org/10.3139/9783446472440.002SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 11-46Print ISBN: 978-3-446-47165-8eISBN: 978-3-446-47244-0 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Reproduction of Human Resources of Academic Organizations as an Imperative of the Future Economy

The purpose of the article is to identify the main factors hindering the rejuvenation of the personnel potential of Russian academic organizations. The scientific novelty of the work lies in the systematic study of all motivational aspects that promote or hinder the attraction of promising young professionals to organizations operating in the academic labor market. The studies were carried out using a modified version of the author’s diagnostic technique “Motivation”. The respondents were young scientists, postgraduate students and university students. The results obtained showed the low efficiency of administrative measures aimed at rejuvenating the personnel potential of academic organizations in the Russian Federation, since most people entering the postgraduate studies only imitate their interest in the field of activity under consideration. Based on a comparison of the motivational profiles of employment behaviour, the basic scales of the MMPI test of successful young scientists and teachers of higher education and students who wish or do not wish to continue their studies in postgraduate school, the proposals were made for the representatives of talented and creative youth to increase the level of motivation to plan their professional development in science and higher education. They are also aimed at solving the problems of preventive professional selection of talented and creative young specialists in higher educational institutions of the Russian Federation and increasing employment guarantees in the academic sphere of labor.
Knowledge sharing &amp; co-operation

The GKD Group has always invested in global work distribution. Yet strong growth and the expanding complexity of processes revealed some limitations. To increase support processes across the group, structured systems were created connecting sites and promoting global knowledge sharing.
Looking beneath the surface of rhabdomyosarcoma: Artificial intelligence using deep learning can classify with 90% accuracy

Introduction: Artificial intelligence can make diagnoses from digital images of histology based on patterns. Deep learning uses convolutional neural networks (CNNs), which are established as an appropriate method of histopathology image classification. Rhabdomyosarcoma (RMS) is subclassified as embryonal (ERMS) and alveolar (ARMS) based on morphology and genetics. But there is overlap in morphological features whilst genetics require additional tests. Therefore, can AI extract information from histology images to inform RMS classification for diagnostic purposes? Aim: Develop CNNs to (1) classify ARMS and ERMS and (2) predict genetic mutation using digital images. Methods: From 144 patients from multiple institutions, we created models to classify RMS using H&E, trichrome, MyoD1 and Myogenin. Our CNN model automatically selected 0.2mm2 (256×256 pixels) patches from digital images. Results: Compared to the Pathologist’s diagnosis, models reached a performance (AUC) of 90% when classifying RMS subtypes whilst our model explanation strategy identified which histology features discriminated ARMS from ERMS. Our models reached an AUC of 78% when predicting genetic changes from H&E images. Discussion: Future work will combine ARMS/ERMS morphology classification with genetics prediction to increase the accuracy of both. We anticipate that Pathologists will one day work with AI systems to increase our diagnostic certainty.
Analysis of Possibilities of Using Game Statistics of the Cloud Quest in Assessment of Personality

The paper describes current trend of “Gamification” and its application in the context of a personality-centered approach to the educational process at the Peter the Great St. Petersburg Polytechnic University (SPbPU). SPbPU has been conducting a welcome quest for first-year students, developing and improving its substantive-technological concept for five years. The current version of the quest works to achieve several goals, such as helping first-year students in the process of adapting to the university, collecting data to develop the concept of building individual educational trajectories, developing the unique social student association “Institute of Adapters”. There are few key elements of the quest: stations, quiz and Instagram contest. Quiz and stations allow to collect data on correctness of answers, on the time of entering answer and on choices made. The player’s individual choice, the result of actions on the offline game station, as well as the speed/time of completing the task are recorded in software. The received data from the stations are compared, analyzed and ranked in the general and individual rating of the players during the game. The conclusions are accumulated, allowing to make an assumption about the orientation and the usual way of interaction of the player’s consciousness with the surrounding reality, about his behavior. The approach to the development of multi-station stations is based on the concept of psychological types proposed by C. Jung.
Old Principles for New Worlds

From videoconferencing to VR, the modern world is fraught with frustrations due to the design of technology. But these frustrations can be reduced or eliminated by "knowing the task" to be performed with the technology and "knowing the user" of the technology. This is the basis of human-centered design. Using the three Vs, videoconferencing, virtual reality, and video games, as examples, the reader is introduced to tools like task analysis. All of the lessons of the previous chapters apply to these three V systems, cementing the idea that understanding the human mind is the first critical piece of any evaluation or design.
Automatic Segmentation of Natural Color Images in CIE Lab Space using Possibilistic Fuzzy C Means Clustering

Clustering is the most significant assignment in image processing. This work performs the segmentation of natural color images in CIELab space based on the Possibilistic fuzzy c means clustering (PFCM).The basic principle of the proposed approach is the segmentation of natural color images based on the two-way approach of hill climbing (HC) and PFCM. In this work, RGB image is transformed into CIELab space for the efficient extraction of the secreted treasure in the images. The combined approach of local optimization search technique, HC and PFCM is applied for the segmentation of synthetic fiber images. This color histogram based technique works on the principle of identification of peaks in the color histogram of the natural color image. The identified peaks are considered as initial seed or clusters. These seeds are then applied to the PFCM to perform the final segmentation. Investigational outcomedemonstrates the competence of thetwo-way approach of HC and PFCMwhich presents the preeminentend result for less complexity color images.