Reviewerly/exHarmony huggingface dataset:
WATERMARK TECHNIQUE FOR AUTHENTICATION OF VISIBLY WATERMARK IMAGES

With the rapid spread of computer networks and the wide use of multimedia technologies, many watermarking techniques are now under development and investigation for protecting owner's intellectual rights. Watermarks can be divided into two types, visible and invisible watermarks, physical visible watermarks have been used for centuries. Now days it is used in digital library, in video broad casting, and other multimedia services. The visible watermark may face several problems. Among these problems, watermark removal and unauthorized insertion are two main concern. This paper proposes a method of invisible watermark to overcome these problems. The watermarking process based on hiding a selected watermark object, that holds a little a mount of information relative to the source object, inside the cover image which has to be authenticated. The processes are based on using invisible watermarks to protect visibly watermarked images which are done by using different techniques. The experiments have shown that the proposed algorithm can provide a very effective protection for watermarked images.
Research on the Factors of the Innovative Talents in Colleges and Universities

From the perspective of cultivating innovative talents in colleges and universities, university construction and talent training models are repositioned based on the theory of innovative talents.First of all, construction must be conducted in universities to ensure the sustainable development; then, universities should create reform of the new talent training models to provide a good talents base for knowledge-based organization and university construction and to improving the competitiveness in the social and employment markets for talents, which can lay a good foundation for students' future career choices and the personal career development of their own.To solve these problems
Development of End – to – End Encoder - Decoder Model Applying Voice Recognition System in Different Channels

the dissimilarity in recognizing the word sequence and their ground truth in different channels can be absorbed by implementing Automatic Speech Recognition which is the standard evaluation metric and is encountered with the phenomena of Word Error Rate for various measures. In the model of 1ch, the track is trained without any preprocessing and study on multichannel end-to-end Automatic Speech Recognition envisaged that the function can be integrated into (Deep Neural network) – based system and lead to multiple experimental results. More so, when the Word Error Rate (WER) is not directly differentiable, it is pertinent to adopt Encoder – Decoder gradient objective function which has been clear in CHiME-4 system. In this study, we examine that the sequence level evaluation metric is a fair choice for optimizing Encoder – Decoder model for which many training algorithms is designed to reduce sequence level error. The study incorporates the scoring of multiple hypotheses in decoding stage for improving the decoding result to optimum. By this, the mismatch between the objectives is resulted in a feasible form to the maxim. Hence, the study finds the result of voice recognition which is most effective for adaptation.
Improved Privacy Protecting in Distributed Grid Data Resource using Multiplicative Perturbation Based on Frequent Decision Classifier

The enormous amount of data process in distributed grid resource holds sensitive information on centralized access. The common privacy standard needs advancement to protect the sensitive data in various sectors like sharing, legal privacy and policies to access the data. The privacy standards depend the Distributed Data Mining (DDM) approaches like Association Rule Mining algorithms (ARM), clustering, and classification methods to preserve the data from unauthorized access. But the security standards have lacked privacy-preserving rules due to high dimensionality problems of data access leads more time complexity. To overcome the problem, to propose a Multiplicative Perturbation Swapping method based on Frequent Decision Classifier (MPS-FDC). This method is adaptive to data publishing secrecy to hold the privacy standards better than association rule prediction. This optimization resolves the forecasting leakages based on Persuasive Privacy Preserving Data Mining (P2PDM) to secure the data. Which this technique initially does the sanitization to reduce the dimensionality to remove un-variant the outliers. The data perturbation keeps the original data to modify using supportive noise delimiters with state matrix distortion (SMD). So the original data keep safe without effect from the outliers. The frequent rule prediction decides to classify the recurrentdata from unauthorized access to disclosing crypto- privacy policy. The proposed system improves the privacy standard compared to the ARM specification rules.
Monipay: Food Consumption and Money Outlay Monitoring System

Supervision regarding the wellness and security of the students of NBCA International School is insufficient.As for short break times and only one concessionaire, negligence in the school's canteen is prominent.To administer and enrich the wellbeing of each student, MoniPay intends to monitor the consumption and expenditures of food purchased using NFC or Near-Field Communication cards where parents are notified of the student's procuring activities.Reports of student's daily and weekly expenditures are accessible in the parent application.This study emphasizes the need for the system to fulfill what is lacking in administering the wellness of the students in the school.This study is quantitative research wherein data is collected by way of surveys answered by respondents chosen through a purposive sampling technique to improve the system.The results and objectives have been met in line with the gathered information from research, interview, and surveys.
Performance Management in Saudi Arabia: The Effects of Employee Engagement, Physical Fitness and Healthy Lifestyle on Job Performance towards Absenteeism

This study has been conducted to identify the effect of employees' engagement, physical fitness including their style (behaviour) on job performance and absenteeism from work in Saudi Arabia.This is a significant study due to the fact that absenteeism from the work has a direct effect on productivity and organisations' performance.Absenteeism is a continuous pattern of absence from duty without a reasonable excuse and leads to poor performance.The study encompasses an investigation on the impact of the above three independent variables through empirical research and data analysis.This study is carried out by the survey method and is empirical in nature.The tool used in this study for analysis is the regression model developed by Sir Francis Galton.The results show a positive relationship between independent variables (employee engagement and healthy behaviour) and the dependent variables (job performance).However, there is not a strong relationship between the third independent variable (physical health) and job performance.The second dependent variable which is absenteeism and result shows no relationship with any independent variables (employee engagement, physical fitness and healthy behaviour).
Parameterizing Queries

Not all data flows are rigid and predictable. There will, inevitably, be cases where you also want to shape the data ingestion process depending on aspects of the source data. This can mean parameterizing your queries to allow user interaction or adjusting the data flow dynamically. Adding parameters to queries enables you to define and apply specific criteria to certain aspects of query processing.
GAMIFICATION AND INTERDISCIPLINARY SCIENTIFIC RESEARCH: SCIENTIFIC TEXT MINING

This study investigates the issues of forming the terminological base of interdisciplinary areas of scientific research to discover new knowledge, to make the prediction, such as to identify the public discourse effects on the dynamics of scientific interests.The goals of this paper are to highlight gamification as interdisciplinary area and to represent the complex approach to contextual knowledge research with scientific text mining on the example of "The gamification in the main spheres of digital society" topic.In the work, we use the capabilities of contextual search and extraction of metadata from texts, describing the use of gamification in various fields of activity, followed by quantitative and qualitative processing of the textual arrays obtained.The knowledge gained was used to expand the capabilities of the synthetic method in the part of the text mining analysis as well as the construction and interpretation technique of subject-specific trends.The conclusion of the research consists in the establishment of the sustainable dependence amongst scientific and public discourse: socio-political initiatives form the modern public life which term-concepts identify not only applying technologies in the life of society as such but the scientific discourse directions.
Introduction

This chapter is an introduction to this Lecture Note. We briefly describe the contents of this book. Both parts are introduced, namely part A which deals with Bayesian modeling and part B which presents real-world case studies. The last part of the chapter details the organization of the various events related to the Jean-Morlet Chair. It ends with the issues and research directions identified by the participants of the Conference on Bayesian Statistics in the Big Data Era.
First International Conference on Virtual Education: Challenges and Opportunities

The first International Congress on Virtual Education, Challenges and Opportunities, ViEduc2018, was doing in Bucaramanga between 8 and 12th October of 2018, as part of the commemoration of the 70th anniversary of the "Universidad Industrial de Santander", UIS, with the participation of 230 participants. This Congress was articulated with different events around the theme of Education and ICT to discuss the positive impacts of the use and appropriation of technologies in education, the transformations they generate, their possibilities and challenges. The Institute for Regional Projection and Distance Education, iPred, the Academic Vice-Chancellorship and the School of Education and the Center for the Development of Teaching, CEDEDUIS, at the "Universidad Industrial de Santander," UIS, were the organizer of this Congress.
Authors’ Biographies/Index

index Share on Authors’ Biographies/IndexIntelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 433–455https://doi.org/10.1145/3447404.3447430Online:23 February 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
New data about crusts of weathering on kimberlites of diamondiferous territories

On ancient platforms, where kimberlite magmatism is established, both large and relatively small in size kimberlite pipes are revealed, with the developed residual crust of weathering on most of them.The analysis of geologic-mineralogical features and composition of crusts of weathering on kimberlite pipes of Yakutian diamondiferous province, which was carried out by us, has shown that intensively chemically refined kimberlites (with a very limited number of cases) are earmarked only in some regions of this territory.The pipe named after the 23d Congress of the CPSU is an example of such deeply chemichally refined crust of weathering of Yakutian kimberlites.In the section of this pipe one can trace clearly expressed zonal structure of eluvial products.Lower zone
EXAMINING THE EFFECTIVENESS OF GAMIFICATION AND GAME-BASED LEARNING IN THE CONTEXT OF BLOCKCHAIN

In the world of gaming, there has been a shift from games being used solely for entertainment, to games being used as a medium to educate.For this matter, there are two methods of making a game which are gamification or game-based learning.The former is the use of game elements, such as health points or leader boards, and they are applied to a non-gaming platform.The latter, game-based learning, includes developing a full-fledged game where the means towards the end, that is to say victory are set in a world where the player has to apply the teachings given to advance.Since this is an IT-related research, the topic chosen for this study is Blockchain, an ever-expanding sector over the past decade.Different parts of Blockchain's makeup have been dissected into small, comprehensible pieces of information in the applications created, which a brand new student to the form can take in at their own pace.Should this study go beyond this research, it would be beneficial for the experiments to have two games designed and created by someone who has the artistic and technical capabilities of creating their own assets.This would leave a better impression with the test subjects and ideally receive a better data set for examination.
Verification of a labor market domain using an academic crowdsourcing system

Students desiring to become a valuable good in the labor market are willing to pay a considerable monetary cost to obtain knowledge about their prospective job opportunities, nowadays with a diminishing interest in the obtainment of a diploma. Considering the behavior of the labor market as a domain theory under uncertainty, it is straightforward to expect the presence of contradictions, in the form of salaries unable to be classified due to high inconsistency and variation. We provide an algorithm to verify a labor market domain theory based on a crowdsourcing academic system, in which feedback about possible contradictions is generated as a result of consultations with experts inside of the market and clustered into different contexts. We found that the verification process can be repeated iteratively as long as the students overall tuition is equal or greater than a quantity partially defined by the number of different profiles of the students.
#8712;

The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, ∈ktelo, for implementing both existing and new privacy algorithms. For the task of answering linear counting queries, we show that nearly all existing algorithms can be composed from operators, each conforming to one of a small number of operator classes. While past programming frameworks have helped to ensure the privacy of programs, the novelty of our framework is its significant support for authoring accurate and efficient (as well as private) programs. We describe the design and architecture of the ∈ktelo system and show that ∈ktelo is expressive enough to describe many algorithms from the privacy literature. ∈ktelo allows for safer implementations through code reuse and allows both privacy novices and experts to more easily design new algorithms. We demonstrate the use of ∈ktelo by designing new algorithms offering state-of-the-art accuracy and runtime.
De-Centralized Hash URI Procedure for Making Digital Artifacts as Immutable, Reliable, Verifiable on Web

Main vision behind semantic web data maintenance is to make the text or content of web is interpretable, among different things, and sophisticated search procedures over huge amount linked web data. In this case different human beings perform fraudulent text which can be found on internet web. So that main research concentrated on automated approaches autonomously identified and analyzed content present in web. Making digital artifacts on web in terms of text, code and others verifiable and reliable, because of general nature of digital artifacts i.e immutability, it is a serious concept to produce reproduces the updated results of processes that display modified content in web resources. So that in this paper, we propose De-centralized Hash URI approach (DHURI) to solve the immutable concept of digital artifact generation in nano-publication with unique hash based cryptography value. In this approach, we provide Decentralized secure URI for identification of digital artifacts with parallel structure representation of data such as nano-publication. Basic concept of this approach is to handle digital artifact immutable concept with evaluation of existing approaches, and also describe the performance of proposed approach in de-centralized framework.
A Suggested Vision for Conducting of Electronic Supervision in the General Directorate of Education, Dhi Qar Governorate

The tremendous scientific and technological progress in various fields of knowledge is one of the most important features of the era in which we live in its present and future, as the progress of this era requires preparing a generation capable of dealing with this progress and changes in the modern technologies as well as the rapid flow of information have left clear implications for education.That the information technology, such as computers, the Internet and the attached multimedia, are among the most successful means of providing a rich learning environment, as for teachers, by contacting to the global network enables the teacher to access educational and general experiences that are difficult to access in other ways.So the importance of the Internet lies in its ability to connect among people across enormous distances and between different information sources.The use of this technology increases educational opportunities, extending them to more than the range of departments, as this is known as the e-learning that distinguishes the school of the future.
Zero-knowledge identification scheme with companion matrices of primitive polynomials

This paper proposes the matrix power problem, that is, to find x given CxDx, where C and D are the companion matrices of primitive polynomials over finite field. And a new zero-knowledge identification scheme based on matrix power problem is proposed. It is perfect zero-knowledge for honest verifiers. Due to its simplicity, and low-memory and low-computation costs, the proposed scheme is suitable for using in computationally limited devices for identification such as smart cards.
In Case You Haven't Heard…

Microsoft embedded Teams with a series of “wellness” tools to address the mental health crisis, and it is pitching the product to employers as a means of boosting worker productivity. “Personal wellbeing experiences,” announced at the Microsoft Ignite conference last month, will arrive on Teams early next year, Business Insider reported Sept. 24. The “experiences” include “emotional check‐ins” that prompt users to select an emoji expressing how they felt about the workday, a “virtual commute” intended to give people time to reflect before and after workdays, and guided meditation sessions through a partnership with Headspace. Microsoft suggests that it is important for employers to track and encourage these behaviors if they want to squeeze the most productivity out of employees without causing burnout. For instance, Microsoft cited a study showing that reflections typically performed during commutes can increase productivity by 15%, and another that found that four sessions on the Headspace app could reduce burnout by 14%. With the Teams update, the data from the emotional check‐in tools will be used as an input to gauge employees' capacity for productivity, integrated within Microsoft's Workplace Analytics productivity tracking platform.
An Assessment of Gpu and Cpu based Convolutional Neural Network for Classification of White Blood Cells

White blood cell (Leukocytes) is made up of bone marrow located in the blood and lymph tissue. They are portion of the human body’s immune system, thereby helping the body system to fight against infection and other related diseases. The number of leukocytes in the blood is usually part of a complete blood cell (CBC) test, which may be used to check for conditions such as infection, inflammation, allergies, and leukemia. Automation of variance count of leukocytes offers valuable information to medical pathologist to diagnose and treat of many blood based diseases. Early characterization and classification of blood sample is a major lacuna in the medical field, giving rise to lots of challenges for pathologist to adequately predict blood based disease. Several successful efforts have been made to address the aforementioned challenges with the use of machine learning generally and Convolution Neural Network in particular. However the processor configuration which can result in real time, and accurate classification of the high dimensional pattern is imminent, and a vast number of researchers are not explicit on the system configuration used to obtain the result in their report, which is the crux of this research. In this research,12,500 augment images of blood cells was obtained from the Kaggle Repository online. The leukocytes are contained in the blood smear image and categorized into five major types of their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte. The color, geometric and texture features are used by the pathologists to differentiate the leukocytes. The Simulation was done using python programing language and python libraries including Keras, pandas, sklearn, numpy, scipy and matplot for potting of graphs of results. The simulation was done on both CPU and GPU processor to compare the performance of the processors on CNNs based classification of the data. While CPU has faster clock speed GPU has more cores. Hence the evaluation metrics used which are precision, specificity, sensitivity, training accuracy and validation accuracy revealed that GPU processor outperforms CPU in terms of the stated metrics of comparison. Therefore a high configuration processor (GPU), which handles graphics better is recommended for processing image data that involves the use of machine learning techniques
DESIGN OF ROBOTIC PETS TO HELP THE ELDERLY WITH SOCIAL INTERACTIONS

Aging is a global issue.The human body and mind are interlinked during this process.Specifically, an unhealthy mental state has an adverse effect on the body, and loneliness and inadequate social ties affect people's moods.After retirement, elderly people lead a different life.Real pets can help elderly people develop good social relationships, and their characteristics can improve people's social ties.However, elderly may have difficulties to raised real pets as the cause of pets' allergies, uncontrollability behaviors, and elderly's living conditions.Hence, as an alternative solution, many pet robots have been developed as a companion for the elderly and to provide emotional care for them.Even if some of them are used to accompany elderly people or assist them in their social activities, these applications are limited to indoor places and interactions between people and pet robots.Hence, there is a lack of interactions that can strengthen personal activities and promote interpersonal relationships.This study explored how elderly people should interact with robotic pets, and investigated the functional requirements for robotic pets.Through a combination of technological means and pet characteristics, the intent is to help the retired elderly people maintain and strengthen social ties and even develop new social relationships.Using a semi-structured interview approach, this study investigated the daily life of their life after retirement to explore the appropriate time at which interaction design can intervene, and explore the requirements for robotic pets.Furthermore, this study provided follow-up research ideas and design reference.