Reviewerly/exHarmony huggingface dataset:
Art Exhibitions During the Pandemic

Along with other sectors and fields, art environments were also faced shutdowns during the COVID-19 Pandemic, which was caused by a type of coronavirus that affected the whole world and resulted in a new period during which people had to work from home and adjust their lives accordingly. In this period, many of the exhibitions were continued online. The prolongation and uncertainty of the period made it necessary to present online exhibitions with more realistic solutions. And this created a new trend towards virtual reality applications that offer closest experience to reality. Virtual reality exhibitions became a notable alternative to online exhibitions in a lot of respects such as allowing typical curatorial arrangements as well as adding digital methods, and making the audience feel like they are actually visiting an exhibition. However, it obviously causes overcrowding and loss of aura as a result of a phenomenon based on excessive display in structures open to the participation of everyone. In order to preserve the aura of art and exhibition and to maintain its compatibility with new technologies, it’s necessary to introduce new curatorial understandings, new aesthetic perceptions, and new quality elements. In this study, the existence of art exhibitions in online environments, especially during the pandemic, will be evaluated within the context of qualitative descriptive analysis.
An Improved User Anonymous Secure Authentication Protocol for Healthcare System Using Wireless Medical Sensor Network

Wireless Medical Sensor Network (WMSN) consists of biosensors connected with each other implanted within the human body.It transmits data to remote medical centers.Medical professionals can access the sensors of the human body to inquire about his health condition remotely.Transmitting patient data over insecure wireless channels is a major challenge because health data are very sensitive and must not be disclosed to unauthorized users, so ensuring secure authentication and preserving anonymity is very important.To address this issue, many researchers have provided many protocols for WMSNs.An anonymous patient monitoring system using WMSN presented by Amin et al. and demanded that their system preserves mutual authentication, user anonymity and security against stolen smart device attacks.By studying thorough and in-depth analyses, we found that this system is attackable to privileged insider attacks and stolen smart device attacks.In addition, it does not protect user anonymity.Additionally, it fails to protect denial of service attack.Furthermore, it has an error in the password modification stage.To overcome the above limitations of the existing systems we have proposed an advanced and mask identity-based secure mutual authentication protocol using WMSN.An informal security analysis is performed, which shows that our protocol is secure against different types of attacks.Furthermore, in our proposed protocol we have used the BAN logic model to prove the correctness of the mutual authentication feature.In addition, it offers ease login, secure authentication and strong password change phases.
Input Fields Recognition in Documents Using Deep Learning Techniques

Identification of input fields that appear on a document is a crucial requirement while digitizing any document. This paper presents a Deep Learning based approach to detect input fields from a form or document which consists of text, images and input fields like textbox, checkbox. The forms have been crawled and labelled manually to generate a dataset for training Deep Learning models. The YOLO V3 model is trained on the labelled dataset having four classes (static text, static image, input text, checkbox) with 1500 instances. We used bounding box techniques to label the dataset. The paper presents detection of limited types of input fields generally appearing on printed forms. We also discussed how such detection models can scale and sustain higher loads. If given the labelled dataset for other types of input fields, the existing YOLO V3 can be trained for them as well. The model is trained for 3500 iterations and the accuracy achieved is 71 percent.
E-mail Literacy in Higher Education Academic Settings

The research purpose was to identify (1) the problems encountered by academic and administrative staff in emails received from students, (2) positive and negative qualities of the authentic emails of higher education students, (3) functional explanations of the academic email, (4) the problems encountered by students in emails received from academic and administrative staff, and (5) higher education students’ email writing awareness. An exploratory sequential mixed design was used. The study group consisted of 15 staff and 1064 higher education students. The qualitative data were collected from staff interviews and 80 authentic emails of students. And a survey was prepared based on qualitative data and then quantitative data were collected. The problems encountered by staff are style, carelessness, articulacy problem, spelling and punctuation problem, email incivility. The negative qualities of authentic emails are as follows: not using institutional username, formal language, paragraph structure in the email body, salutation, closing statement, contact information; username without name and surname, blank subject line, spelling and punctuation problems, sloppy wording, lack of self-introduction. Non-descriptive, late, and short answers, not getting answers, sloppy answers, emails with negative feelings disturbed students. Students’ awareness of writing academic emails displayed a more positive picture than the emails they wrote. Items in which students’ awareness is weak are as follows: trying to reflect their feelings to email, using punctuation marks to convey the feeling, writing email for long and complex matter, using paragraph structure, adding contact details, CC - BCC. Research results were discussed with relevant literature.
Dual Image Based Watermarking Scheme Using Quorum Function

In order to preserve the confidentiality and copyrights of a medical image, digital watermarking techniques are comprehensively used. This paper presents a new digital image watermarking algorithm using dual image, quorum function and NMI technique to prevent the digital document from unauthorized alteration which improves the perceptibility of the watermarked image and is computationally effective. The proposed technique applied in interpolating kind of algorithm which is reversible because the original image can be recovered at the receiver end. Moreover, size of the original image is up-scaled using a neighbour mean interpolation. The efficiency of the proposed technique has been tested in irreversible kind of algorithms too. The experimental results prove that the image quality of proposed scheme is better than the traditional schemes.
Comparison of Xbox One and Steam Joystick-based Operating System User Interface using KLM- GOMS

Despite having many advances in video game control such as voice or gesture control, most video game console manufacturers still use a joystick as the main control for their console. Currently, the problem with video game manufacturers is that instead of using the joystick as the point of user interface design, they instead used a design from another device such as a computer or tv. The research will compare Xbox One and Steam OS user interface by using KLM-GOMS Model. Based on the calculation it is concluded that the overall Xbox One has a more efficient design compared to Steam.
Recognizing Physical Activities for Spinal Cord Injury Rehabilitation Using Wearable Sensors

The research area of activity recognition is fast growing with diverse applications. However, advances in this field have not yet been used to monitor the rehabilitation of individuals with spinal cord injury. Noteworthily, relying on patient surveys to assess adherence can undermine the outcomes of rehabilitation. Therefore, this paper presents and implements a systematic activity recognition method to recognize physical activities applied by subjects during rehabilitation for spinal cord injury. In the method, raw sensor data are divided into fragments using a dynamic segmentation technique, providing higher recognition performance compared to the sliding window, which is a commonly used approach. To develop the method and build a predictive model, a machine learning approach was adopted. The proposed method was evaluated on a dataset obtained from a single wrist-worn accelerometer. The results demonstrated the effectiveness of the proposed method in recognizing all of the activities that were examined, and it achieved an overall accuracy of 96.86%.
Comparison of Isokinetic Muscle Function and Anaerobic Exercise Capacity in the Knee According to Kukki Taekwondo Training Type

Introduction: This study’s purpose was to measure and compare isokinetic muscle function and anaerobic exercise capacity of knee joints according to the three Kukki Taekwondo competition types. Methods: A total of 21 elite Korean male poomsae (forms), gyorugi (sparring), and demonstration athletes majoring in Taekwondo were selected as the subjects of the study. Subjects’ physical fitness levels were measured using standardized strength, endurance, agility, balance, and flexibility tests. In particular, the isokinetic muscle function measurement system was used to measure the muscle function of subjects’ knee joints, and the Wingate test was used to measure their anaerobic exercise ability. Results: Gyorugi athletes showed better results than their poomsae and demonstration counterparts in nearly all metrics, including body composition, physical fitness factors, isokinetic muscle function (60°/sec), muscle power (180°/sec), and anaerobic exercise capacity. Gyorugi athletes were also taller and showed higher muscle mass, strength, muscular endurance, and agility. However, poomsae athletes showed a higher level of flexibility. The results found isokinetic muscle function and anaerobic exercise capacity of the knee joint to be most important for gyorugi athletes and then for demonstration and poomsae athletes, in that order. Conclusions: This study provides useful information needed to identify the physical fitness factors of the different Kukki Taekwondo athletes, and, in turn, it is may serve as important fundamental data for developing future systematic Taekwondo training programs.
Developing mathematical models for global solar radiation intensity estimation at Shakardara, Kabul

Solar energy properties such as Global Solar Radiation (GSR) intensity could be determined in either methods, experimentally or theoretically. Unfortunately, in most countries including Afghanistan, the first method which is more acceptable, but due to the high cost, maintenance and calibration requirements is not available. Therefore, an alternative widely used way is the second one which is model developments based on the meteorological (atmospheric) data; specially the sunny hours. The aim of this study at Shakardara area is to estimate atmospheric transparency percentage on 2017, determining the angstrom model coefficients and to introduce a suitable model for global solar radiation prediction. The hourly observed solar radiation intensity H (WHm-2 ) and sunshine hours S (
Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism

Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mechanism-based BSO (ABSO) algorithm based on the chaotic local search in this study. The adjustment of the search space using the local search method based on an adaptive self-scaling mechanism balances the global search and local development performance of the ABSO algorithm, effectively preventing the algorithm from falling into local optima and improving its convergence accuracy. To verify the stability and effectiveness of the proposed ABSO algorithm, the performance was tested using 29 benchmark test functions, and the mean and standard deviation were compared with those of five other optimization algorithms. The results showed that ABSO outperforms the other algorithms in terms of stability and convergence accuracy. In addition, the performance of ABSO was further verified through a nonparametric statistical test.
Advancement of Destination Service Quality Management Technology in Tourism industry

In this study, the researchers have proposed, developed and tested a novel model whichhighlighted the effect of the Destination Service Quality (DSQ) dimensions (like local transport,accommodation, cleanliness, hospitality, different activities, airport, language communication services) on thetourist satisfaction, in UAE‟s tourism industry. For this purpose, the researchers distributed 700 questionnairesamongst the international tourists visiting Dubai. 565 of the 700 (i.e., 80.71%) of thevalid tourist questionnaireswere returned. Thereafter, the researcher carried out the multiple- regression fortesting all research hypotheses.The findings have supported all the study‟s hypotheses. The destination marketers, planners and managers mustinvestigate all dimensions for developing better marketing and operational strategies.
Entrepreneurial management technology for nursing professionals

to validate entrepreneurial management technology for the nursing practice.methodological study, carried out based on the development of an entrepreneurial management technology, based on literature review and, later, content validation by 11 experts, recognized by their peers as entrepreneurs. The validation process took place through Delphi conferences, between June and September 2018.on the second round of Delphi conferences, at least 90% agreement was obtained on all items. In addition, all items were validated as pertinent and considered prospective and inducing of new thinking and acting among nursing professionals who wish to undertake and explore opportunities, goods, and services in the area.it was noted that the experts, in general, showed good adherence to the initiative and ease in validating the theoretical-conceptual dimension and the dimension of "Personal/professional qualities necessary for the performance of entrepreneurial management"; however, they had difficulty in validating the methodological steps.
Enabling Preserving Bisimulation Equivalence

Most fairness assumptions used for verifying liveness properties are criticised for being too strong or unrealistic. On the other hand, justness, arguably the minimal fairness assumption required for the verification of liveness properties, is not preserved by classical semantic equivalences, such as strong bisimilarity. To overcome this deficiency, we introduce a finer alternative to strong bisimilarity, called enabling preserving bisimilarity. We prove that this equivalence is justness-preserving and a congruence for all standard operators, including parallel composition.
Blockchain Technologies and Social Media: A Snapshot

Facebook has proposed to integrate the cryptocurrency 'Libra' into its services, a plan which has been heavily discussed in science, business and politics. On Steemit.com one receives a tradable-token namend Steem in return for writing popular articles or post comments while at Wildspark.me one is rewarded for discovering the next big YouTube video. This shows that Blockchain technologies have an undeniable influence on the development of today's Social Media Networks. On the one hand, we have the technology hype around Blockchain and what kind of new applications are made possible, and on the other hand, the speculation hype around digital tokens (cryptocurrencies) and new forms of investment (ICO, STO), which have in turn resulted in a large number of start-ups in the Social Media and Blockchain sector. Start-ups and products, which quite often disappear from the market in a relatively short time. This paper provides a Snapshot, how Blockchain technologies could influence established Social Networks and gives an overview of known start-ups and their platforms which facilitate Blockchain technologies (like Steemit, Social X, Obsidian, Indorse, Sola, onG, PROPS Project, Block.One, Coil, All.me, Diaspora, Ardor.rocks, Mind, Wildspark and HyperSpace). Finally, a discussion on how Blockchain technologies can be used beyond speculation is provided.
Association Rule Mining for Intrusion Detection System: A Survey

Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.
Augmented Reality Appling with Consistency of Behavior using Oriented Bounding Box Algorithm

Augmented reality can be defined as the process of merging digital information in real videos, it plays big role in many application such as education, medicine, and media industries. The merging of virtual moving object into real videos consists of two main stages: the detection of moving objects into real videos and the collision detection between real and virtual objects. In this study, an algorithm for merging virtual moving object into real video has been proposed, it is based on the three frame differencing technique followed by two pre-processing steps to detect real moving object. While the collision detection stage is based on the intersection between moving object path and the bounding box of real object. The experiment results show the good accuracy of the proposed algorithm in extracting moving object and merging virtual object into video frames.
ComplexityParser: An Automatic Tool for Certifying Poly-Time Complexity of Java Programs

ComplexityParser is a static complexity analyzer for Java programs providing the first implementation of a tier-based typing discipline. The input is a file containing Java classes. If the main method can be typed and, provided the program terminates, then the program is guaranteed to do so in polynomial time and hence also to have heap and stack sizes polynomially bounded. The application uses antlr to generate a parse tree on which it performs an efficient type inference: linear in the input size, provided that the method arity is bounded by some constant.
Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images

Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histo-pathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using proper countermeasures to tackle WSI high resolution and very imbalanced dataset. The experimental results show that one can successfully classify adenomas dysplasia grade with 70% accuracy, which is in line with the pathologists’ concordance.
Top Tree Compression of Tries.

We present a compressed representation of tries based on top tree compression [ICALP 2013] that works on a standard, comparison-based, pointer machine model of computation and supports efficient prefix search queries. Namely, we show how to preprocess a set of strings of total length n over an alphabet of size $$\sigma$$ into a compressed data structure of worst-case optimal size $$O(n/\log _\sigma n)$$ that given a pattern string P of length m determines if P is a prefix of one of the strings in time $$O(\min (m\log \sigma ,m + \log n))$$ . We show that this query time is in fact optimal regardless of the size of the data structure. Existing solutions either use $$\Omega (n)$$ space or rely on word RAM techniques, such as tabulation, hashing, address arithmetic, or word-level parallelism, and hence do not work on a pointer machine. Our result is the first solution on a pointer machine that achieves worst-case o(n) space. Along the way, we develop several interesting data structures that work on a pointer machine and are of independent interest. These include an optimal data structures for random access to a grammar-compressed string and an optimal data structure for a variant of the level ancestor problem.
Firm-Level Cybersecurity Risk and Idiosyncratic Volatility

The authors propose a measure of firm-level cybersecurity risk developed by employing pattern-based sequence-classification method from computational linguistics to determine the proportion of time devoted to issues related to cybersecurity risk during earnings conference calls. Using their measure, they investigate the effect of cybersecurity risk on firm-level return volatility; they examine both idiosyncratic volatility and implied volatility and find that firm-level cybersecurity risk is positively correlated to idiosyncratic volatility on the days on which earnings calls are held. This suggests that the discussion of issues related to cybersecurity risk during earnings calls is related to an increase in the component of the volatility that responds only to firm-specific news. That positive relationship is robust to alternative measurements of the language in earnings call discussions and to industry classifications. <b>TOPICS:</b>Security analysis and valuation, risk management, big data/machine learning <b>Key Findings</b> ▪ The authors propose a new measure of firm-level cybersecurity risk that applies textual analysis to earnings conference call transcripts; they use that measure to investigate the effect of cybersecurity risk on firm-level return volatility. ▪ They find that discussion of issues related to cybersecurity risk during earnings calls is associated with an increase in the component of volatility that responds only to firm-specific news. ▪ The authors show that the impact of cybersecurity risk on firm-level volatility is robust to alternative measurements of the language in earnings call discussions and to different industry classifications.