Reviewerly/exHarmony huggingface dataset:
The Research Data Alliance in Norway

The Research Data Alliance (RDA) is a neutral international network aiming at promoting data sharing and data-driven research. The efforts of RDA are organized in a number of groups, including national nodes, where contributors work together to develop and adopt approaches that foster the uptake of standards and good practice of research data management through all stages of the data lifecycle. Since 2019, Norway has had its national RDA group.&#x0D; This article gives a short introduction to the Norwegian RDA group. In section 1 we provide some background information about RDA. Section 2 describes the Norwegian RDA group, including its background and organisational structure, as well as past and future activities.
The Power of Technological Social Learning in Times of Crisis

All countries, regardless of their level of development and position in the world economy and relations, try to adopt similar survival behaviors in the management of educational institutions since the first signs of the corona outbreak. Numerous people are contextually forced to learn from each other in this new adapted environment while not everyone is pessimistic about the pandemic situation and its unexpected obligations. Many people believe that the pandemic crisis is an opportunity to warm up the foundation of family relationships and the integration of high standard virtual education at global and inclusive levels. The unexpected change in social and educational intuitions reminds humanity of the vital and bright side of technology in this critical period. This chapter examines and models how virtual education can save money and resources to provide well-designed and purposeful learning opportunities for students to learn without fear when proper plans are implemented to overcome the impact of crises through the power of technological social learning.
Exploring Misjudgments in IoT Analytics

In the industrial internet of things (IIoT), raw data are collected from thousands of sensors. Such a huge mass of data rises to the challenge of performing analytics. In the previous study, Pai et al. utilized varied methods to analyze the real-world paper breaks dataset. The preliminary test shows the difficulty in identifying anomalies, such as “non-linear relationship”, “normal instances with extreme value” and “identical patterns between normal and anomalous instances”. In this paper, for further study, we applied the non-linear support vector machines (SVM) method to classify anomalies. Given complete instances and class labels; however, the result of analytics is not 100% accuracy. In fact, there are 7 misjudgments. We considered that the class labels of raw data may be incorrect owing to certain error. According to PTS, the breaks should happen one after the other during a continuous period. However, in the dataset certain breaks are separate and their next instances are marked as normal operation. In sum, the problem of misjudgments is much more complicated. It's worth further studying from perspectives on both the quality of data and effectiveness of method.
Morphological and Characteristic Analysis of Upper Aero-Digestive Tract Tumour: Revealing Uncovered Facts in Digital Pathology

Upper Aero Digestive Tract cancer is treated as the primary cancer type compared to other different cancers. Exploring the morphological behaviour and characteristics of biopsy tissue sample is very significant in tumour grade analysis for proper diagnosis. After all, the manual microscopic tissue analysis process is considered as the golden standard. Traditional pathological study is still challenging and tough to overcome the manual tissue analytical barriers. To develop an efficient automated computer aided system for 1. cancer tissue analysis, 2. tumour grade classification and 3. survival prediction of cancer patients. The combination of different image vision techniques and microscopic image analysis tools are used to develop the state-of-the-art frameworks which will be efficient to extract different morphological features from different UADT tumours. The extracted biopsy tissue morphological features will be taken for automatic tumour grade classification that helps in assisting the pathologists to overcome the manual microscopic cancer grade classification problems. The state-of-the-art automated tissue analysis framework is developed to extract the features from the tissue samples within the short period of time. The proposed framework will be efficient for automated tissue characteristic analysis from UADT biopsy tissue samples and that can assist the pathologists to solve the inter observer variability problems.
Research on Tennis Recognition Technology Based on Computer Vision

Visual attention mechanism is one of the important means for human beings to perceive the external world. Using mathematical models to introduce visual attention mechanism into computer vision to simulate human visual perception system is a hot research topic in the field of computer vision. The research of visual attention model is not only helpful for human beings to better explore the working mechanism of human visual attention, but also has very important significance for solving large-scale data screening and improving image processing efficiency, which has important application value in moving object detection, machine vision, image information matching, image compression and other fields. The visual attention model is used to preprocess the video sequence, and the region of interest is found as the candidate region for target detection. Then the color and shape matching algorithm is used to match the candidate regions. Experiments on tennis video show that the algorithm can recognize the target well when the color and shape of the target are relatively single and the significance in the background is high.
Algebraic construction of near-bent function with application to cryptography

Near-bent functions that occur in odd dimensions are the important class of Boolean functions, which are useful functions for cryptography. In this paper, we construct near-bent function with trace term using well known Welch function exponent in polynomial form and various other forms of near-bent functions. We have also investigated some important cryptographical properties of near-bent functions.
Teaching Design Exploration of the Course Based on the “6+2” Basic Principle--Take “Computer Foundation” Course as an Example

Abstract Under the background of the new normal of education, the state puts forward new requirements on vigorously developing modern vocational education where new technologies, new techniques and new specifications should be incorporated into the teaching standards and teaching contents in time to strengthen students’ hands-on ability. As the key to the reform of vocational education, curriculum design must be combined with the principle of “6+2” to realize the integration of teaching and doing. As a public basic course, “Computer Foundation” always implements the “6+2” principle in teaching, and takes the basic office software operation as the system, which is fully in line with the training objectives of the basic courses of vocational education.
Detection and Automatic Correction of Bengali Misspelled Words using N-Gram Model

This paper presents an approach for Detection and Automatic Correction of Bengali misspelled words using N-Gram model. Most of the works are established in this domain based on minimum edit distance between the words. But, in some of the cases, the distance based error correction method could not provide correct answer. This situation occurs when more than one suggestive word, de-rived by the distance based approach has the same distance value with respect to the misspelled word. The proposed model can handle this situation based on the local context of the misspelled word. This context analysis task is performed using N-Gram model. In the overall experiment, first of all, error correction is performed using minimum edit distance on 200 sentences containing some misspelled word in each of it. Around 40 instants occurred where more than one suggestive word had the same edit distance with respect to the misspelled word. The proposed strategy successfully finds the context wise most relevant word from the suggestions in most of the cases. Experimentally, tri-gram is used for handling the local context of the misspelled word.
MSB/LSB Prediction Based Reversible Data Hiding in Encrypted Images: A Survey

Encryption provides security to authentic data that is transmitted from sender to receiver for a particular task. Reversible Data hiding in Encrypted Images (RDHEI) has attained interest as per its assured reliability. The technique make use of secret key to encrypt original image for embedding secret information in it and recuperate original image perfectly with unaltered secret information when extracted. A sort of trade-off persists between embedding capacity and recuperation of original image. Many of the techniques existing in literature use Least Significant Bit (LSB) or Most Significant Bit (MSB) to embed secret information. In plaintext domain, LSB embedding is easy since there is minute change in pixel value of original image which attains less distortion in original image after embedding. Encrypted domain is less restricted than plaintext domain where embedding of secret information can be done either in LSB or MSB. Reason for the previous statement is that after encryption, original image pixel gets evolved as pseudo random numbers and any modification in MSB or LSB are insignificant. This paper reviews prediction error based reversible data hiding (RDH) techniques that too dealing with encrypted domain utilizing MSB or LSB prediction, which enhance the embedding capacity of secret information within original media alongwith it’s full recovery. The encryption of secret information is done either at bit plane level or pixel level.
Uncertain Knowledge Graph Embedding: a Natural and Effective Approach

Abstract Uncertain Knowledge Graph (UKG) models uncertainty of the knowledge, which usually models the inherent uncertainty of relation facts in a knowledge base with a confidence value. Embedding such uncertain knowledge represents is still an unresolved challenge, although deterministic Knowledge Graph embedding has been extensively studied recently, which aims at representing entities and relations as vectors in a continuous vector space and preserving semantic information as much as possible. For capturing both structural and uncertainty information of relation facts in the continuous vector space, we propose a simple but effective two-steps approach to Uncertain Knowledge Graph Embedding (UKGE) based on the skip-gram/CBOW model and learning confidence score by Long Short-Term Memories (LSTM) neural network. We show that the embedding techniques achieve results comparing with state-of-the-art approaches on uncertain knowledge graph embedding. The approach achieves the best tradeoff between efficiency and accuracy of UKGE. Because of the simplicity, the method can also handle large size graphs in lower time consumption.
Inter-Frame Based Interpolation for Top–Bottom Packed Frame of 3D Video

The frame-compatible packing for 3D contents is the feasible approach to archive the compatibility with the existing monocular broadcasting system. To perceive better 3D quality, the packed 3D frames are expanded to the full size at the decoder. In this paper, an interpolation technique enhancing and comparing the quality of enlarged halt vertical left and right stereo video in the top–bottom frame-compatible packing is presented. To this end, the appropriate interpolation modes from fourteen available modes for each row segment, which exploit the correlation between left and right stereoscopic as well as current and adjacent frames of individual view, are estimated at the encoder. Based on the information received from the encoder, at the decoder, the interpolation scheme can select the most appropriate available original data to find the missing values of to-be-discarded row segments. The proposed method outperformed than the state-of-the-art interpolation methods in terms of subjective visualization and numerical PSNRs and SSMI about 11%, with an execution time of about 12% comparisons.
Empirical Evaluation of various ML algorithms for classification of online Restaurant reviews

The gaining popularity and availability of ecommerce websites led many users to express their views and opinions on various products or services that are available online. Due to the extensive presence of data which is increasing everyday over the internet, it shows that the reviews or opinions provided by the users on the e- commerce sites are very large and in unstructured form. Therefore, it is highly challenging for the online users, customers and manufacturers to take appropriate decision about the opinions on these reviews. Hence there is a need to analyze the opinions present in the reviews to know if the review stated is positive or negative. Opinion Mining aims at analyzing the user opinions in the text. This work mainly aims at binary classification of reviews using different ML techniques thereby identifying the best model suitable for classifying the online restaurant reviews.
Mining approximate frequent subgraph with sampling techniques

Graph Mining has gained importance due to its wide range of applications. As graph mining is a form of data mining where data is depicted in the form of graphs. The data when represented in the form of graphs helps to model the real scenario and relationship well. Discovery of interesting pattern from real relationship is of utmost beneficial. Frequent Subgraph Mining (FSM) is a significant area of graph mining. We accompany our approach in the field of FSM by adding a graph sampling function. This sampling function takes the single graph and a sampling percentage as input and produces a sampled graph which is similar in properties with original graph. This sampled graph is then taken as input while implementing the GraMi framework. Our sampling approach successfully mines the subgraphs with lower frequency threshold thus significantly improving the efficiency of the original GraMi framework.
FEATURES OF TEACHING CHILDREN WITH MENTAL DEVELOPMENT DELAY IN THE SYSTEM OF CORRECTIVE - LOGOPEDIC WORK

&#x0D; &#x0D; &#x0D; &#x0D; The article talks about children with mental retardation, the peculiarities of the development of their speech, about the ongoing correctional work with this group of children, about improving the education system, taking into account the characteristics of children with mental retardation.&#x0D; &#x0D; &#x0D; &#x0D;
A two-stage algorithm for estimation of unknown parameters using nonlinear measurements

A suboptimal two-stage algorithm has been proposed to solve nonlinear estimation problems consist in comparison of measured and reference samples. The new algorithm consists of preliminary processing of measurements, subsampling and simplification of the errors model in nonlinear algorithm. A significant increase in computational performance determines the novelty of the presented algorithm. The effective application of the two-stage suboptimal algorithm is illustrated by an example of gravity-aided navigation.
Desain Sarana Informasi Di Taman Samarendah Samarinda

Samarendah Park is one of the new icons in Samarinda City. Its function has been felt by the people of Samarinda since it opened. This park can be used as a place to exercise, play children, or just relax. Now the park which is in the center of Samarinda City preening becomes increasingly beautiful. Taman Samarendah is reorganized so that it can be enjoyed more and more by Samarinda residents and tourists. Therefore, a signage is needed that is used to make information that guides the place and appeals. The purpose of this design is to make an information board that can provide information about Samarendah Park and facilities around the park. In this design process using the Vinod Ghoel method which includes Preliminary Design, Design Development, Final Design &amp; Prototype. This design produces a product that is the Samarendah park information board. With the existence of the Samarendah park information board, it is hoped that it can facilitate access to information on places and appeals on location for visitors
Continuous Experiments for Elderly Person's Exercise Sensing and Evaluation

To motivate elderly people to continue exercises and support busy staffs in care facilities, a sensing and evaluating system is developed for the elderly people exercises. The system consists of a Kinect sensor and a personal computer and detects joint movements of the elderly persons. 5 days continuous experiments have been conducted to evaluate the developed system with the cooperation of a care facility. A male in his 70's and a female in her 90's were the experimental subjects. In the experiments, the weather condition data such as temperature and humidity were collected to evaluate the relationship between the weather condition and the joint movement. Analysis of the experimental results reveals tendency of each person's exercise. Moreover, it is found that atmospheric pressure may affect joint movements.
Development of Measuring Instrument for Inner and Outer Diameters of Bearing Seal Rings Based on Machine Vision

In order to accurately and efficiently measure the inner and outer diameters of bearing seal rings, a monocular measurement instrument was developed. The mathematical principles of camera calibration and distortion correction were studied. After the figures of rings were distortion corrected and preprocessed, we calculate the inner and outer diameters based on Otsu threshold segmentation and RANSAC algorithm. Operating the monocular measuring instrument to measure eight different types of sealing rings. According to the measurement results, it is found that the average time to measure the two diameters of a sealing ring is 0.79 seconds and the relative error of the measurement is 0.12%, which proves that the monocular measuring instrument can meet actual needs for sampling measurement of rings in the factory laboratory.
A Practical Hybrid Active Learning Approach for Human Pose Estimation

Active learning (AL) has not received much attention in deep learning (DL) for human pose estimation. In this paper, a practical hybrid active learning strategy is proposed for training a human pose estimation model, and it is tested in an industrial online environment. The conducted experiments show that the active learning strategy to select diverse samples to be annotated outperforms the baseline method with random sampling. As a result, the strategy enables a significant improvement in the performance of pose estimation.
Implementation of Security in Login Page Using Salt and Pepper Algorithm

Secure secret phrase stockpiling is an indispensable angle in frameworks dependent on secret phrase validation, which is as yet the most broadly utilized verification method, in spite of its some security blemishes. In this paper, we propose a secret word validation structure that is intended for secure secret phrase stockpiling and effectively incorporated into existing confirmation frameworks. In our structure, first, the got plain secret phrase from a customer is waded through a cryptographic hash work (e.g., SHA-512, SHA-384, and MD5). At that point, the hashed secret phrase is including into a salt and pepper. At last, the salt and pepper key creates Encrypted Password. To further improve the security salt and pepper algorithm and multi-iteration encryption has been employed. The cryptographic hash capacity and encryption make it hard to split passwords from attacker, which makes pre-computation attacks infeasible. The pepper and salt algorithm provides stronger password protection under attack. Introduce extra elements (e.g., salt, pepper the principal secret phrase insurance conspire that joins the cryptographic hash work, the secret word and the salt and pepper key calculation, without the requirement for extra data aside from the plain secret phrase. The salt key regard cushioning the widely appealing position in the plaintext and the pepper key regard cushioning the begin & ending position in the plaintext. This structure can give the security to power individual.
Data Encryption Scheme Based On Adaptive System

World has evolved into digital hub, lots of digital data such as images, videos, text and audio signal are transmitted over the internet. Due to increase in this multimedia communication, there has been a rapid increase in requirement of encryption of these data. Various encryption scheme has been proposed to make these multimedia communications more and more secure. In this paper, a new approach of adaptive system has been presented. Adaptive system previously used for prediction, noise removal and system identification has been deployed to encrypt data and results have shown, adaptive system can be used to encrypt signal with high rate of randomness in encrypted signal. Furthermore, randomness can be enhanced by changing the desire signal.
Just-in-Time Sentiment Analysis for Streamed Data in Greek

The growth of social-media platforms has been remarkable in terms of both number of users and volume of content generated. Twitter now reports approximately 166M daily active users who generate in excess of 500M tweets. Such volumes pose major challenges when it comes to providing analytics services and sentiment analyses for specific issues. As citizens tend to freely express their sentiments on social platforms, Twitter has inherently become an indispensable source for the public discourse in a wide variety of topics. Carrying out sentiment analysis on a timely manner on streamed tweets is undoubtedly a demanding endeavor. In this paper, we propose a Spark-based Twitter sentiment analysis software architecture that receives online streamed messages and compiles analytics. We outline the main elements of our proposal and discuss how they collectively help address the challenges involved in this big-data processing task. In particular, our framework: i) exploits the Spark machine-learning library to classify Greek tweets in a timely-manner, ii) manages streamed tweets in synergy with contemporary queuing and in-memory data systems, and iii) determines with high accuracy whether a sentiment is expressed by a genuine account. We report on the findings while experimenting with a novel model for sentiment analysis we created for Greek and ascertain the effectiveness of our proposed architecture.
High Level Software Separation: Experience Report for e-Health and Legal Metrology

We give a practical example of transferring High Level Software Separation and Risk Analysis Techniques initially developed for measuring instruments in Legal Metrology, into the medical device domain. The concepts are used to separate medically sensitive, and hence regulated software, from the dynamic, unregulated software parts. In that way, the highly valuable patient data are securely separated from potential security threats, ensuring greater IT protection of the medical device and hence the patient data. Concretely, virtualization with the Xen hypervisor has been implemented, to show how IT security for Windows software can be enhanced by separating software parts and restricting access to hardware. The knowledge gained is presented as an experience report and compares the concept with the already known software separation mechanisms for Legal Metrology. Additionally, an experimental test was done, to analyze how a second graphics card, which is forwarded to the medically relevant software parts through PCI passthrough, can significantly improve performance.