Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Comprehensive Health Screening Medical Service Quality with Kano Model and PCSI Index

Purpose -This study aims to identify the priorities of medical service quality improvement by customer satisfaction characteristics and potential customer satisfaction improvement (PCSI) index based on the dualistic quality classification of Kano Model (1984) for Comprehensive Health Screeening Center in General Hospitals and Centers only for Comprehensive Health Screening and suggest a direction for future improvement.Research design, data, and methodology -Through advanced research on health screening medical service quality, this study set four service quality factors, including tangible, human, process and supportive factors, and 39 measurement items.Based on these items, the study used 117 questions, which consist of dualistic quality factors, customer satisfaction coefficients, positive and negative questions for PCSI index and questions for current satisfaction.300 effective samples were collected for adults in their 20s who experienced health screening service in Seoul, Gyeonggi-do and Incheon within the past two years.Collected data were input in the quality evaluation duality table to categorize quality factors and calculate customer satisfaction coefficients by Timko(1993).The study also analyzed PCSI index in comparison with current satisfaction and identified priorities in quality improvement.Results -It was found that the most urgent factors to improve the quality in both groups were adequate waiting hours and emergency response for complications, which are process factors classified as unitary quality.It is urgently needed to improve the quality as the PCSI index was high in supportive factors (complaint response team) as attractive quality in Comprehensive Health Screening Center in General Hospitals and in process factors (prevention of infection) as unitary quality in Centers only for Comprehensive Health Screening.As the PCSI index was low in space use as a tangible factor, it was found that the current level can be maintained instead of improvement.Conclusions -To improve the health screening medical service quality, it is required to focus on process factors (adequate waiting hours, emergency response for complications, prevention of infection) and supportive factors (complaint response team) among service qualities perceived by users.It is proposed to ensure continuous efforts to manage and reinforce priorities as a direction for future improvement in health screening service.
SLA-Aware Multi-Criteria Data Placement in Cloud Storage Systems

This paper proposes a multi-criteria data placement mechanism for typical cloud storage systems. The primary goal of this study is to place the clients' files in the storage cluster by taking into account the principles of service level agreements and the levels of user support services. For this purpose, a multi-criteria scoring model is defined based on a trade-off between three criteria that can be crucial from the clients' viewpoint: data access latency, data privacy, and computational performance. The second goal of the proposed mechanism is to improve the fairness of the storage allocation mechanism for various kinds of files compared to the conventional mechanisms. In this case, the cloud service provider gives each data storage node a chance to become the host of an already received file using a probabilistic ranking model. The experimental results demonstrate that the proposed data placement mechanism increases the robustness of cloud storage architecture under different file, network, and storage configurations compared to the existing data placement mechanisms.
Modelled Versus Satellite Retrieved Estimation of the Direct Normal Irradiance and the Sunshine Duration over Bulgaria

The incoming solar radiation is an essential climate variable that determines the Earth’s energy cycle and climate. Its accurate assessment is also relevant for the correct sizing, design, and dynamic simulation of solar energy systems. The three main solar radiation components—global horizontal irradiance (GHI), direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) are usually not all available simultaneously as measured data. Thus, the missing components have to be obtained by means of the numerical modelling. The present article is dedicated on the evaluation of the simulation capabilities of the solar radiation model DISC using satellite data as an input and as a reference. The DNI and, subsequently, the sunshine duration are simulated using the surface irradiance from the CM SAF SARAH2 product collection as a main input parameter. The model set-up implements also information from the Bulgarian operative system ProData as an auxiliary data. Computations on hourly basis for the year 2015 over domain, which covers Bulgaria with fine-spaced grid, have been performed. The results have been compared with their satellite counterparts from the same collection. The main conclusion is that the modelled results remarkably overestimate the satellite-derived estimations.
Recognition of Building Health Status Based on Machine Learning Algorithm

In order to improve the results of building health status recognition, a method of building health status detection based on machine learning algorithms is proposed. First analyze the process of building health status recognition, find the factors that affect the building health status recognition effect, and then select the main influencing factors for building health status recognition modeling, and introduce machine learning algorithms to describe the building health status and influencing factors The internal connection between the building health status recognition model is established, and finally the effectiveness and superiority of the method are analyzed using specific building health status recognition examples. The average recognition rate of the building health status exceeds 92% while the current classic The method's recognition rate of building health status does not exceed 90%, and the recognition speed is faster, which has better practical application value.
Adaptive Digital Image Watermarking Using DWT-PCA

With the growing demand for sharing digital content over the internet, the need for multimedia security has been significant. Digital media contents like images, audio, video can be easily manipulated by computer to perceptible with original content. Advancement in Digital Watermarking techniques has been a solution to protect digital media content from copyright infringement. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. The main motivation for creating an adaptive mechanism is the fact that not all images are the same and hence a static embedding factor will not result in a robust embedding of watermark in the cover image in all cases. Experimental results show the efficiency and robustness of the proposed technique.
Novel Method to Analyze and Forecast Social Impact on Macro- and Micro-Economies Using Social Media Data

Stock markets are often seen to be the backbone of an economy, the variations in which could be indicative of the prevailing condition of the economy. Although the high vulnerability to external factors, it is often a natural challenge to quantify the impact. The research aims to identify and isolate the true performance of the listed companies by adjusting the impact from the external events—which is quantified using a novel approach considering social media as a platform. The resultant approach is highly generalizable and could be applicable for any industry which is highly significant compared to previous work. Hence, this could be seen used in the perspective of stakeholders such as potential investors, clients, and governments as well.
ECASR: Efficient Channel Attention Based Super-Resolution

Despite recent advancements in single image super-resolution (SISR) methodologies, reconstruction of photo-realistic high resolution (HR) image from its single low resolution (LR) counterpart remains a challenging task in the fraternity of computer vision. In this work, we approach the problem of SR using a modified GAN with specialized Efficient Channel Attention (ECA) mechanism. CA mechanism prioritizes convolution channels according to there importance. The ECA mechanism, an extension of CA, improves model performance and decreases the complexity of learning. To capture the image texture accurately low-level features are used for reconstruction along with high-level features. A dual discriminator is used with GAN to achieve high perceptual quality. The experimental result shows that the proposed method produces better results for most of the dataset, in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and mean-opinion-score (MOS) over the state-of-the-art methods on benchmark data-sets when trained with same parameters.
Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications

In 2020 the coronavirus outbreak changed the lives of people worldwide. After an initial time period in which it was unclear how to battle the virus, social distancing has been recognised globally as an effective method to mitigate the disease spread. This called for technological tools such as Mobile Contact Tracing Applications (MCTA), which are used to digitally trace contacts among people, and in case a positive case is found, people with the application installed which had been in contact will be notified. De-centralised MCTA may suffer from a novel kind of privacy attack, based on the memory of the human beings, which upon notification of the application can identify who is the positive individual responsible for the notification. Our results show that it is indeed possible to identify positive people among the group of contacts of a human being, and this is even easier when the sociability of the positive individual is low. In practice, our simulation results show that identification can be made with an accuracy of more than 90% depending on the scenario. We also provide three mitigation strategies which can be implemented in de-centralised MCTA and analyse which of the three are more effective in limiting this novel kind of attack.
The implementation of multi-platform technology

Abstract A single-platform technology is currently shifting to a multi-platform technology as the industrial revolution era is also developing. This study aims to identify the implementation of multi-platform technology in the industry-driven educational context. It has been found that multi-platform technology has a more effective and efficient effects either in the educational or the industrial context. It is also implied that a mobile-based multi-platform technology is preferred due to its accessibility and usability.
Image Enhancement Based on Adaptive Power-Law Image Intensity Transformation

This paper is devoted to solving the issue of improving the quality of video content (digital photos and videos) in real-time apps. This work aims to improve the efficiency of enhancing complex images through adaptive power-law intensity transformations in automatic mode. In this paper, a new approach to image enhancement in an automatic mode based on adaptive power-law image intensity transformation (APLIT) was proposed. The APLIT approach allows directly calculating the value of the exponent of the gamma correction for the set thresholds and reducing the computational costs compared with the traditional technique of power-law transformation. Based on APLIT, we have proposed two new techniques of adaptive power-law transformation. The proposed APLIT based techniques show results close to the traditional gamma correction but have lower computational costs. The proposed techniques are efficient, easy to implement, computationally low-cost, and are designed to normalize and enhance video in real-time apps.
Extended Reality in Educating the Next Generation of Health Professionals

COVID-19 has severely impacted health professions education and assessment has mostly shifted online. Major challenges remain especially about teaching-learning and assessment of clinical skills. Interacting with an individual online does not provide the range of information provided by an inperson meeting. There have been dramatic advances in computers and the internet in the last 60 years. Extended reality (XR) is all real- and virtual-combined environments and human-computer interactions generated by computers and wearable devices. The future physical world is likely to face multiple challenges. It is likely that human interactions and learning will increasingly occur in XR spaces. Three-dimensional holographic avatars and interacting and manipulating objects in XR spaces will become easier. Most medical education can occur in these spaces. Universities will invest substantially in these spaces. The safety of the physical world, the cost and usability will determine the extent of use of these learning spaces.
Flashcards as a Microlearning Tool in English Language Teaching

The paper deals with the use of a mobile application based on the principle of flashcards to support the teaching of English to primary school pupils. Using such an application while doing homework is a prerequisite for expanding pupils' vocabulary, improving pronunciation and storing words in long-term memory. The reason for using the mobile application is to make English language teaching more attractive, since in the Czech Republic there is a declining trend in the level of English language acquisition. Based on further described criteria, a mobile application working on the principles of flashcards was selected. The application was available to a group of pupils to facilitate their home preparation for their English lessons. The pupils' acquired knowledge was then tested and also their work with the application was monitored. Simultaneously, a feedback through a questionnaire survey was obtained from the pupils using this application. Besides the effect of flashcards on the level of vocabulary in the English language of a selected target group, the paper also deals with the potential use of flashcards as one of the possible tools of microlearning.
An Improved Kernel Density Estimation with adaptive bandwidth selection for Edge detection

Edge detection has been one of the most active and challenging research aspects in the last three decades in the applications of pattern recognition and machine vision as well. Although, the recent research showed that there has been lots of work reported on the basics of Kernel density estimation (KDE), but the selection of proper bandwidth not yet reported in any literature work, because it was the most challenging issue on both under and over smoothed images in terms of non-parametric KDE. Realizing the aftermath, the current research delivers a well-defined KDE approach which allowed us an adaptive selection of bandwidth by the enactment of Shannon entropy for keeping the effect of low edge gradient in feature space. Furthermore, our adaptive bandwidth selection technique is validated through three measures like entropy, energy, and blur for more analytical analysis; along with the statistical control limit is applied to generated gradient images for appropriate edges. Finally, our proposed methodology is compared with state of art techniques in terms of the Figure of Merit (FOM) measure, and which confines a higher rate of accuracy in detecting edges. This is a more reliable and precise estimation.
Identifying Social Networks of Programmers using Text Mining for Code Similarity Detection

The availability of code in many online repositories and collaborating platforms has posed new challenges in source code attribution not only for plagiarism detection but also in other settings such as in the use of insecure copied code in commercial application, etc. The sophistication of different type of attacks in the code sequence used especially by the students requires more effective code similarity detection algorithms. In this paper, a novel source code detection method is proposed that can identify programmers' social network based on advanced pattern detection text mining techniques. The proposed methodology has significant advantages against existing methods since ARPaD algorithm can detect all common patterns between all possible code sequences in one run. Therefore, the computational time is massively reduced to 0(mn log <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> ). In order to assess the performance of the methodology, a new dataset was created by assigning to 46 students a code project with specific instructions. The assessment results have been visualized, producing the social network graphs of possible collaboration teams.
Design of Parallel Rank-Order Filtering System Based on Neural Circuits of Discrete-Time

A rank-order parallel filtering (ROF) system is presented. It is modeled by system of difference equations. There are designed functional block-schemes of the system. In contrast to other analogs, the system has arbitrary high resolution of inputs, linear computational complexity and it can process unknown finite value inputs located in any known confined region. The ROF has moderate computational and hardware implementation complexity. Moreover, it does not need readjusting and appropriate control circuits. Numerical simulations showing performance of the ROF are given.
BeSober: Assisting relapse prevention in Alcohol Addiction using a novel mobile app-based intervention

Chronic alcohol consumption has become one of the major concerns of the society in the sector of public health. The death toll due to excessive consumption of alcohol is increasing exponentially. Face-to-face interaction to create awareness and to stymie the consumption of alcohol is a quixotic solution. Alcohol relapse remains a challenging problem in disorders associated with alcohol addiction which is related to spatial-temporal factors like periods and specific places. In this paper, a new generation of relapse prevention mobile application called “BeSober” is proposed. It assists users to develop abstemious habits in drinking, providing support within the intervention period, tracking risky alcoholic spots, presenting community-based support from alternative users or alcohol addiction therapists, monitoring addict's behaviour and offer personalized recommendations to assist the addict in staying sober.
A Middle-out Approach for Building a Legal domain ontology in Arabic

Introducing ontology in information retrieval provides the obvious benefit of higher precision and addresses other common issues such as information quality and user adaptation. However, the main disadvantage is the costs (i.e., time and effort) of manually constructing an ontology and of its representativeness of the specified domain. This paper considers the ontology construction process and proposes a middle-out approach that allows the construction of a well-founded ontology speedily. The domain application that interests us is Moroccan commercial law. The ontology to be built aims to support users in describing a specific legal situation and retrieving the relevant legal articles and court decisions in similar cases. The proposed approach combines a top-down and bottom-up strategy. The first allows us to define an ontological model of the legal domain by reusing an existing core ontology, whereas the second populates and refines this model based on an ontology-learning process from Arabic texts.
A Noble Watermarking Scheme Based on Spatial Domain Approach with Pixel Exchange and Compressive Sensing

Security plays an important role in hiding the sensitive digital data in multimedia applications. People use different techniques to hide the confidential information behind an image. Authors, in this research, have used the least significant bit method to hide the secret information, and then applied cryptographic techniques to encrypt the image. In this paper two images were taken, one as the base or cover image and the other image containing important information which should be hidden behind cover image. Authors applied the cryptographic techniques. Initially, the cover image was disintegrated into four equal segments. Each segment was selected and divided into four sub-segments based on the frequency component. The sub-segments were selected and pixels between them were exchanged, then the modulus function was applied to encrypt each sub-segment, followed by integration of the four sub-segments to form one segment. The next phase was to choose the four segments obtained from the first step and exchange the pixels between them. The modulus function was repeated to decrypt each segment and combined to get the original data.
Design VLSI Architecture for 2_D DWT Using NEDA and KSA Technique

Image compression emanating utilizations of data compression on digital images. The Discrete Wavelet Transform (DWT) is getting familiar with an extreme move in image processing. To eliminate the drawback in the JPEG standard and rising areas of mobile and Internet communications, the new JPEG2000 standard has been created based on the principles of DWT. With the prevalence of the 2_D DWT, the technology has seen predictable improvement. All things considered, there is still a need for more robust and efficient compression technology using DWT. Hence, a 2_D DWT is implementing with a multiplier-less NEDA technique. NEDA is consisting of a buffer, ROM, and adder. There are many types of adders used in digit circuits but Kogge Stone Adder (KSA) is used in the proposed method. KSA is a very efficient adder and consists of the XOR gate and HA. Proposed Schemes are simulated Xilinx software with the VHSIC Hardware Description Language (VHDL) platform and calculates parameter i.e. speed and frequency.
A Project Management Hybrid Model of Software Development in an Academic Environment

The project management paradigm is changing. Agile methodologies are part of this change. They came in opposition to traditional standards and their dissemination and intertwining is resulting in the adaption of traditional models to hybrid models, especially when dealing with small projects. They allow to balance flexibility and predictability. There is no standard hybrid model, it must be adapted to the project being developed. This paper aims to analyze the performance of a hybrid model were the initiation, planning and finalization followed the traditional methodology and the development followed the agile methodology (scrum). This model was used by a team responsible for the management and development of a software project in an academic setting. The project consisted of the development of a digital platform to support project management in Small and Medium Enterprises (SME). The team was composed by university students and the hybrid model was defined by the teachers of the course. Data were collected through observation and the application of a questionnaire to the team. The results show that the model used was effective for the management and development of this project, done in an academic environment, since it was less formal and more adaptable to people with little experience in project management than a purely traditional model.