Reviewerly/exHarmony huggingface dataset:
A Hybrid Security Model for the Protection of Diagnostic Text Data in Medical Images over Internet of Things

As innovation for transmitting data has advanced exponentially, new avenues for protecting sensitive information have emerged up. Over the last several decades, many different strategies, such as steganography and cryptography, have been developed to safeguard sensitive information. As the use of IoT devices in healthcare has grown exponentially, concerns about patient privacy and confidentiality have surfaced as important roadblocks for healthcare service systems. Security in device-to-device communication is a challenging subject. There are a plethora of existing cryptographic techniques for use, such as Data Encryption Standard (DES), Rivest-Shamir-Adleman (RSA), and Advanced Encryption Standard. In this research, we provide hybrid security architecture for safeguarding medical image files that include interpretive text. The proposed scheme uses 2D-DWT to encrypt and conceal sensitive information. Both color and black-and-white photos are used as text covers. The proposed system’s efficacy was evaluated using a battery of tests that included PSNR, SSIM, MSE, and Correlation. The suggested model disguised sensitive patient information in a way that was comparable to traditional methods in terms of throughput, invisibility, and damage to the received steno-image. To implement the suggested system, we turn to MATLAB, with throughput and execution time serving as key metrics for evaluation.
Research Development of Computer Information Acquisition System based on Cyclic Data Analysis

Based on the multi-agent technology, based on the characteristics of the existing cyclic data analysis calculation model, combined with the functional requirements of the distributed information acquisition system, this paper constructs the formal architecture MMFA of the multi-computing model integration distributed information acquisition system. Researched the technology related to data communication and applied it in engineering. In this research, the research on data communication technology mainly focuses on the serial communication between the CP machine and the lower computer (SCM). Discussed the possibility of using multi-agent and other technologies for modeling, and established a formal abstract architecture based on the fusion of multiple computing modes.
An Iterative Majorization-Minimization Approach to Deep Denoiser-Plugged Image Inpainting

It is known notoriously that most deep learning-based computer vision models are black-boxes. In this paper, a preliminary attempt has been made, deriving an interpretable representation learning approach to a specific inverse imaging problem, i.e., image inpainting. The new approach is not only rooted in the classical optimization framework, but also closely connected to the numerical differential equations. What is more important, numerous experimental results demonstrate that the proposed approach has achieved comparable or even better performance than many cutting-edge restoration algorithms, particularly those based on either sparse representation or naïve deep representation.
MOBILE APPLICATION FOR DIET RECALL

This is a mobile application which helps users to track their diet and to manage their calories intake.Busy schedules make people forget about their health.This application provides an efficient way to take an overview of health status.There are many applications in the market for diet management but they forget some key points about Indian diets and Indian Ayurveda, which are included in this application.This application also has gamification features like providing points to motivate the user.This application is developed with the help of flutter development tool and hive database.
PROLOG, A TECHNOLOGICAL APPROACH TO TEACHING: THE CASE OF MATHEMATICS AND PROBLEM SOLVING

A Prolog program consists of a set of facts and rules rather than imperative statements, commonly used in most other programming languages.Therefore, the Prolog language is used to encode logic, from which the inference engine deduces logical conclusions.In this article, we argue that the use of the Prolog language can be useful to help students become familiar with mathematics or more broadly, any problem-solving based discipline.
Knowledge-Based Evolutionary Optimizing Makespan and Cost for Cloud Workflows

Workflow scheduling is essential to simultaneously optimize the makespan and economic cost for cloud services and has attracted intensive interest. Most of the existing multi-objective cloud workflow scheduling algorithms regard the focused problems as black-boxes and design evolutionary operators to perform random searches, which are inefficient in dealing with the elasticity and heterogeneity of cloud resources as well as complex workflow structures. This study explores the characteristics of cloud resources and workflow structures to design a knowledge-based evolutionary optimization operator, named KEOO, with two novel features. First, we develop a task consolidation mechanism to reduce the number of cloud resources used, reducing the economic cost of workflow execution without delaying its finish time. Then, we develop a critical task adjustment mechanism to selectively move the critical predecessors of some tasks to the same resources to eliminate the data transmission overhead between them, striving to improve the economic cost and finish time simultaneously. At last, we embed the proposed KEOO into four classical multi-objective algorithms, i.e., NSGA-II, HypE, MOEA/D, and RVEA, forming four variants: KEOO-NSGA-II, KEOO-HypE, KEOO-MOEA/D, and KEOO-RVEA, for comparative experiments. The comparison results demonstrate the effectiveness of the KEOO in improving these four algorithms in solving cloud workflow scheduling problems.
Mobile speleological parallel robot with a spatial tactile system for identification of the contact surface

The paper considers the problem of conducting speleological studies of natural and artificial caves, including channels of the underground rivers, as well as various mine workings in dangerous or inaccessible conditions for a speleologist. In this case, external environment of the objects under study could turn out to be opaque for optical, radio, ultrasonic and other physical methods of monitoring the inner surface, for example, due to partial or complete flooding of the object with water with inhomogeneous suspensions. A solution to the problem is proposed by robotizing the research using a mobile speleological parallel robot (MSPR) with a spatial tactile system for identification of the contact surface. MSPR is created in the form of an active octahedral structure, which edges are rods with linear drives. The rod ends are pivotally connected to the corresponding vertices of the active octahedral structure ensuring its geometric invariability when the linear drives are turned off. As a result, MSPR is able to carry out contact (tactile) mapping of the inner surface under study regardless of the external environment transparency and its geodetic binding to the base coordinate system with visualization in the form of histograms. At the same time, MSPR is able to self-move along the internal surfaces regardless of their spatial orientation. MSPR and its functionalities are described, one of which is the ability to build the 3D histograms of the surrounding space bind to the base (inertial) coordinate system through the mechanical contact. The tactile system of identifying the contact surface makes it possible not only to move the MSPR, but also to tactilely monitor in the external environment that is opaque for optical, radio, ultrasonic and other physical control methods. MSPR allows robotizing speleological research in dangerous, hard-to-reach and inaccessible places for a speleologist, as well as to carry out rescue operations and ensure delivery of the required cargo.
The Impact Of Compensation And Work Environment On Employee Performance Through Job Satisfaction

This research seeks to discover the impact of compensation and office environment on employee performance as well as their overall sense of fulfillment. This is accomplished by including job satisfaction as a secondary variable in a regression analysis. Analysis of data comes from secondary research files and questionnaires. The most important data comes from the Smart PLS Version 3 SEM model. That data type is called primary data. The study proves that employee performance is affected by compensation, while the work environment has no effect on performance. However, compensation affects employee job satisfaction, while the work environment has no effect on it. Additionally, this study reveals that job satisfaction has an effect on the relationship between compensation and performance on work environment— leading to improved employee performance
New Tectonic Plate Model Could Improve Earthquake Risk Assessment

A new model of tectonic plates in New Zealand may identify areas of increased earthquake likelihood.
EARLY BREAST CANCER DETECTION USING ARTIFICIAL INTELLIGENCE CLASSIFIERS

The breast cancer is one of the most common diseases affecting women worldwide, the World Health Organization reported that over ten thousands approx women died from the disease in 2020 alone, accounting for approximately 10% of all female cancer deaths.As a result, breast cancer diagnosis is one of the most pressing issues that must be addressed as soon as possible.Multiple image modalities, including mammography, echography, and magnetic resonance imaging (MRI), are used to diagnose breast tumours in this context.Chemotherapy is one of the most common treatments for this pathology.However, several side effects (hair loss, osteoporosis, vomiting, and so on) may occur as a result of this treatment, and cancer will not respond to it.The purpose of this paper is to propose a novel method for predicting breast tumour response to treatment, which consists of three major steps: 1. Tumor segmentation from MR images; 2.Feature extraction from segmented tumours to generate a complete and exploitable database; 3. Use of deep and machine learning architectures to compute tumor-response prediction models.The experimental findings are applied to a publicly available QIN Breast DCE-MRI dataset of breast cancer patients.
THE PATTERN OF TEST-TAKING EFFORT ACROSS ITEMS IN COGNITIVE ABILITY TEST: A LATENT CLASS ANALYSIS

When examinees perceive a test as low stakes, it is logical to assume that some of them will not put out their maximum effort.This condition makes the validity of the test results more complicated.Although many studies have investigated motivational fluctuation across tests during a testing session, only a small number of studies have investigated motivational fluctuation across items within a single test.This study aims to examine the pattern of test-taking effort across items in cognitive ability tests when items are presented in a random order manner.Response Time Effort (RTE) was used as a measure of test-taking effort.This measure calculates the proportion of rapid responses in the test based on the response times for each item.Data from 213 university students completing the inductive reasoning test was examined using latent class analysis.The results suggested that examinees in low-stakes testing have different patterns of effort across items.Examinees who consistently provided a high level of effort across items had higher test performance, test-taking engagement, and RTE.Item position and item difficulty are also correlated negatively with test-taking effort.Implications of these results for researchers and practitioners are discussed.
THE LEGAL UNCERTAINTY ASPECT OF THE REGULATORY APPROACH REGARDING ONLINE CONTENT MODERATION

This paper presents the results of the prestigious Horizon 2020 MIICT: ICT-enabled services for migration innovation project.The project was conducted by a multidisciplinary team of technical developers, social scientists, lawyers, practitioners, and migration experts in three pilot countries of Cyprus, Spain, and Italy.The project focused on deepening societal understanding of the factors that impact upon refugee and migrant populations' ability to access key public services and subsequently created an ICT solution to solve these problems.The project deployed a co-creation methodology, which proved essential when designing ICT solutions to aid migrant integration, enabling researchers to involve both the migrants and those assisting them in their migration journeys, namely NGOs and service providers in the study as co-researchers.The researchers, together with the participants co-designed a multilingual, intuitive, inclusive, interactive platform called IMMERSE that catered for the real needs of migrants, asylum seekers and refugees.The innovative co-creation methodology deployed empowered migrants to be proactive and independent in their migration journeys and the resulting interactive platform created provides vital access to information and services like health, language, housing, employment, education, and legal assistance in a one-stop-shop design.This paper further highlights the lessons learned from the research, concerning the need for digital training courses to be implemented to overcome the digital divide and the inclusion of extensive legal and ethical protocols in migrant languages, to be integrated into the platform to counteract migrants' fears of digital surveillance and create confidence and wider usage of the ICT platform.
PROMOTING CYBERSECURITY AWARENESS UTILIZING A “BUILD YOUR OWN ADVENTURE” SERIOUS GAME

Cybersecurity is of critical importance as we continue into the digital era.With technology improving rapidly and the accessibility of such technological devices becoming more prominent globally, the need for proper cybersecurity awareness has drastically increased.This has led to a need for methods to improve cybersecurity awareness.This paper addresses one such method, a "build your own adventure" serious game, where the aspects of cybersecurity awareness are combined with effective gaming principles.A serious game that teaches the users about cybersecurity awareness in a novel way is proposed.
ESTABLISHING CYBERSECURITY AWARENESS OF TECHNICAL SECURITY MEASURES THROUGH A SERIOUS GAME

Cybersecurity is broadly considered an important concept for the protection of individuals and families, and also governments and organizations from online threats.This importance notwithstanding, there is a lack of understanding regarding cybersecurity globally, not only within the working class but within the general public.It can therefore be deduced, that there must also exist a lack of understanding regarding the technical principles of cybersecurity, such as how an individual's computer keeps them safe within an online environment using techniques such as firewalls and cryptography.This paper addresses this lack of understanding by means of a serious game.
EXCHANGING PERSONAL DATA THROUGH BLOCKCHAIN TECHNOLOGY TO FULFIL INCREASING PRIVACY NEEDS

The risk of privacy intrusion is a developing concern due to the advancement in data mining techniques and the increased value of data.This study focuses on developing a system that implements blockchain technology to allow the exchange of personal data while ensuring the authenticity and privacy of the exchanged data are preserved.The privacy of data is ensured using encryption while the authenticity is intuitively built into blockchain technology with the consensus algorithm and cryptographic signatures.The blockchain interaction was enhanced by developing a companion application that simplifies exchanging data over the blockchain using QR codes.The expectation is to create awareness of the potential for using blockchain technology for secure-, private-, but verifiable data exchange, the possible implementations for future systems, and to reduce negative sentiments in regards to data sharing.
GAMIFIED VR SIMULATION IN NURSING EDUCATION: HOW TO GENERATE VALUE WITH VR IN NURSING EDUCATION

A shortage in nursing staff in Switzerland has reached a high level, and forecasts see the problem growing for the next years as there is a lack of candidates who are willing to sign up for nursing education programs.Prior research suggests that virtual reality (VR) can have positive effects on motivation, learning outcome, and trust.Therefore, we examine if VR in nursing education can help raise the attractiveness of education programs and help raise the number of people signing up for education programs which incorporate VR in their training and education.
FROM PRACTICE TO SCIENCE: SOCIAL REACTIONS CAUSED BY CONVERSATIONAL AGENTS  A LITERATURE REVIEW FOR RESEARCHERS AND PRACTITIONERS

The number of publications focussing on the design of Conversational Agents (CA) has been continuously growing.However, from a research perspective, a review of the existing publications that visualizes both design elements and effects on user behavior is still missing but needed to reach an overall understanding of the connections and dependencies of researched design elements.From a company's perspective, practitioners are forced to first decide on certain design elements and then to optimize these according to their goals, due of the actual state of research.To close this gap, we conducted a systematic literature review and developed a conceptual model that links different design cues with social signals and social reactions.On the one hand our model helps researches to get a better understanding of the influence of several design cues of CAs.On the other hand, our model helps practitioners to design conversational agents according to their companies' goals.
DEVELOPING AN ONTOLOGY OF MULTIPLE PROGRAMMING LANGUAGES FROM THE PERSPECTIVE OF COMPUTATIONAL THINKING EDUCATION

This paper proposes the design of an ontology of multiple programming languages and give three examples to show the methodology.Our ontology aims to summarize the core of computational thinking logic by elaborating the concepts of three object-oriented programming languages in the industry: Python, Java, and C#.Therefore, the construction of the ontology lies not only on these three programming languages but also on their common concepts.This kind of ontology design facilitates the ontology extension and merging when concepts of other programming languages are added in the future.This ontology could be used to not only provide a guideline for any research work focusing on computational thinking education but also describe the common concept of visual programming tools and existing programming languages.Based on this ontology, an adaptive tutoring system intended to provide learners with personalized programming exercises, is under development.
ENHANCED FEATURE EXTRACTION USING OPTIMAL WHITENING AND STACKED AUTOENCODERS

There are multiple preprocessing techniques used across the globe to enhance classification performance.Researchers in the past have incorporated various methods to improve feature extractions from image and text dataset.Extracting optimized features is still a challenge in the current scenarios, not only because size of the data is humongous, but it also involves noisy components.In this manuscript, we have proposed a method to compute the most optimized features from datasets by first transforming the features into the optimal ZCA features which are later fed into a 2-layered Stacked Autoencoder (SA).The novelty of the manuscript lies in the idea of deriving optimal features by performing optimal whitening (instead of plain ZCA) which is followed by feature extraction via stacked autoencoders.The in-depth features learned from SA are then fed into a feed-forward neural network as a final step towards classification.We have experimented rigorously on 10 non-computer vision, 2 hyperspectral remote sensing data set, and 1 computer vision datasets and found that classification accuracy increases to 2-4% on an average across many datasets.Additionally, we have validated the feature extraction method across plain vanilla neural network architecture and found the performance to increase by at most 5% on a few datasets.
EVALUATION OF DATA OPTIMAL STORAGE SYSTEM IN HYBRID CLOUD

A hybrid cloud is a mix of compute systems, storage systems and services consisting of on-premises systems, private clouds and public clouds for satisfying requirements such as security and performance.In the case of hybrid cloud configuration where data are in on-premises storage systems and compute systems is in public clouds, data should be transferred to the public cloud when a read/write command is issued by compute systems.Thus, long data transfer time is a problem since the on-premises systems and public clouds are connected by the internet.One possible solution is to create an additional cache area inside the compute system; however, it is not clear how effective it is.In this paper, we verify the effect of the data cache by creating the hybrid cloud environment with/without the cache area and measuring input/output latency.We created the measurement environment in the hybrid cloud with dm-cache technology and set up the assumed workload of TPC Benchmark TM E with the write-through mode.The average latency with the cache area reduces by up to 79% compared to without the cache area, and we confirm the effect of installing cache areas in this environment.
AUTOMATIC SPEECH RECOGNITION SYSTEM TO ENHANCE THE USE OF VOCALIZATION STRATEGY

Vocabulary learning that overlooks the discrimination of single sounds in English language learning could lead to communication difficulties, such as the inability to hear and speak English pronunciation.To address this issue, vocalization strategy, in which students listen to the sounds of words and memorize them aloud, is considered a useful learning strategy, and has been reported to influence performance in English language learning.Many studies have shown that metacognition promotes the use of this strategy.In this study, we developed an English vocabulary learning support system using dictation-type speech recognition technology and evaluated whether it activates learners' metacognition and promotes the use of vocalization strategies.
Optimizing Bilingual Learning to Improve Students' Critical Thinking Skills Through PheT Media

Optimizing bilingual learning through PheT media is one of the conscious efforts of teachers to improve students' critical thinking skills. This study aims to optimize bilingual learning using Phet's media on the critical thinking skills of class XI students at SMA Teuku Nyak Arif Fatih Bilingual School. This type of research is quasi-experimental that uses a nonequivalent control group design model. The population in this study was all class XI which amounted to 2 classes. The sample in this study was taken using a total sampling technique, namely the number of samples equal to the total population of 36 students. The data collection technique used is pretest and posttest learning outcomes tests. The data analysis technique used a two-party t-test with the normality test and homogeneity test first as a prerequisite for the analysis. The results obtained based on data analysis using a two-party t-test, namely - . So according to the hypothesis then is accepted and is rejected, it can be concluded that bilingual learning using PheT media is not optimal to improve the critical thinking skills of students at SMA Teuku Nyak Arif Fatih Bilingual School.