Reviewerly/exHarmony huggingface dataset:
A powerful probabilistic model for noise analysis in medical images

Abstract The statistical properties in various medical images demonstrate uncorrelated noise fluctuations. The signal noise fluctuations are generally due to physical imaging processes and have nothing to do with the tissue textures. Adding the noise types (e.g., quantization, electronics, photon) usually degrade medical images. The noise variation is usually assumed to be additive with zero‐mean, constant variance Gaussian distribution. However, close consideration of different medical images indicates the need for better model representation to minimise the noise that can be vital in decision‐making. This research proposed a probabilistic method to represent all real‐type noise in general medical images. The method aims to cover most classical statistical models such as Gaussian, lognormal, Rayleigh, Weibull, and Nakagami without a prior examination to test for fitness. The proposed model was applied to actual clinical images to test the performance of the noise originating from the physical processes. The noise is assumed to be additive white Gaussian type with a zero mean and constant variance. The theoretical literature indicates that a nonlinear function can better represent noise. This research helps to form a relationship between the image intensity and the noise variance that yields the fitting parameters in the introduced nonlinear function. The validity of the proposed method was proved mathematically and tested using the well know Kolmogorov–Smirnov (K‐S) and Akaike Information Criteria (AIC) tests. The method was successfully applied to various clinical images such as magnetic resonance, x‐ray, and panoramic images. The model's performance is compared with the classical models using root mean squared error (RMSE), relative error (RE), and R 2 as the evaluation matrices. The presented model has outperformed all classic models.
SMART HOME DATA VISUALIZATION FOR PROACTIVE HEALTH MONITORING OF COMMUNITY DWELLING OLDER ADULTS

Abstract The role of Ambient Assistive Living and smart home technologies, which utilize unobtrusive sensors to detect changes in health, is becoming increasingly important in the delivery of healthcare services to older adults. However, these technologies must be designed to meaningfully incorporate into clinicians’ decision making. Research has shown when clinicians are engaged in the design process of smart home systems, the accuracy and efficacy of the systems are improved. We present the process undertaken by a team of nurse researchers and computer science engineers to design clinically meaningful behavior markers derived from smart home sensor data that can be used by nurses to proactively identify changes in patient status. During the first phase of design, nurse researchers qualitatively analyzed time series from smart home sensors installed in the homes of community dwelling older adults and identified patterns in these data related to significant health changes. From this analysis, we assembled a candidate list of 15 sensor-based behavior metrics, such as percent time spent in each room or frequency of bathroom use. During the second phase of design, we will build on lessons we learned from participatory design to create behavior markers and visualizations that are inspired by clinical experience. These include visualizing behavior change over time, highlighting behavioral anomalies at multiple time scales, and calculating markers that are not directly observable such as time spent out of home. Lessons learned from clinicians using the data visualizations to proactively screen for health changes in near real time will also be discussed.
Opinion classification at subtopic level from COVID vaccination-related tweets

Coronavirus disease 2019 (Covid-19) is a contiguous disease which affected a large volume of population with a high mortality rate across the globe. For dealing with the recent spread of COVID-19, one of the prime measures was to vaccinate people in full extent. People across the globe have diverse opinion regarding the vaccination process, its side effect and effectiveness. Such opinions get located into different micro-blogging sites including twitter. Opinion mining through analyzing public sentiments of such micro-blogs is a common method for detection of public responses. This paper focuses on classifying the public opinions expressed related to COVID-19 vaccination at sub topic level. The procedure tries to find out different keywords regarding positive, negative and neutral sentences. From those keywords, different related query set was constructed using Rocchio query expansion algorithm for positive, negative and neutral sentiments. Later Extended query set is used to form subtopic using LDA algorithm to identify the nature of the tweets. The proposed LDA model came across with 0.56 coherence score with twenty subtopics, which is fair enough to classify the tweets in different classes. This trained model is finally used to classify the tweets in real time with Apache Kafka framework regarding different subtopic based on positive, negative or neutral sentiment.
Exploration and Practice of Gamification in Kindergarten Curriculum

In order to promote the development of education with the times, teachers should carry out the reform of early childhood education based on the new curriculum reform, follow the laws of physical and mental development of children, and design teaching activities.Play is an activity that children like very much, and it plays an important role in the healthy growth of children's physical and mental health.Under the background of quality education, the emphasis on the comprehensive development of children has been increased, and teachers should innovate the teaching mode of children.Carry out kindergarten courses in the form of games, stimulate children's interest in learning, highlight children's active learning status, build a child-centered learning environment, and create a learning atmosphere that is entertaining and entertaining, so that they can acquire knowledge happily, strengthen their bodies, and achieve quality Educational goals to promote the overall physical and mental development of children.
A Knowledge Enforcement Network-Based Approach for Classifying a Photographer’s Images

Classification of photos captured by different photographers is an important and challenging problem in knowledge-based and image processing. Monitoring and authenticating images uploaded on social media are essential, and verifying the source is one key piece of evidence. We present a novel framework for classifying photos of different photographers based on the combination of local features and deep learning models. The proposed work uses focused and defocused information in the input images to extract contextual information. The model estimates the weighted gradient and calculates entropy to strengthen context features. The focused and defocused information is fused to estimate cross-covariance and define a linear relationship between them. This relationship results in a feature matrix fed to Knowledge Enforcement Network (KEN) for obtaining representative features. Due to the strong discriminative ability of deep learning models, we employ the lightweight and accurate MobileNetV2. The output of KEN and MobileNetV2 is sent to a classifier for photographer classification. Experimental results of the proposed model on our dataset of 46 photographer classes (46234 images) and publicly available datasets of 41 photographer classes (218303 images) show that the method outperforms the existing techniques by 5%–10% on average. The dataset created for the experimental purpose will be made available upon publication.
Super-resolution reconstruction for far-field beam profile based on deep learning

In order to obtain more information of far-field beam profile from limited measured data, a novel super-resolution reconstruction method for far-field beam profile based on deep learning is proposed. In this paper, the high/low resolution spot image sample is obtained by simulation, the EDSR neural network training model is used to study the mapping relationship between high/ low resolution spot images, and the super-resolution reconstruction method based on deep learning is realized. The experimental results show that the super-resolution reconstruction based on deep learning is superior to the conventional algorithms in PSNR and SSIM.
THE CONSTRUCTION AND APPLICATION OF A TEACHERS' PROFESSIONAL DEVELOPMENT PLATFORM UNDER THE CONCEPT OF SELF-DIRECTED LEARNING

Teachers' professional development is an important factor to improve the quality of teaching in colleges and universities.At present, teachers' professional development in colleges and universities mainly focuses on teacher training and consultation, lacking personalized attention to teachers' professional development.With the development of technology, the construction of a professional development platform for teachers based on the concept of self-directed learning provides a new perspective on teachers' professional development.This paper first analyzes the design ideas of A University's teacher professional development platform, and systematically describes how to integrate teachers' learning resources, build teaching portfolios and build online communication communities under the guidance of the self-oriented concept from the perspective of different sessions of data aggregation, self-development and teaching promotion.Then, after analyzing the application of the platform, the results show that the construction of a professional development platform based on the concept of self-directed learning plays an important role in supporting teachers' personalized development paths, promoting teachers' reflection on teaching, and building learning communities.
Typical Case of Digital Teaching of C Language Based on the Whole Process of Academic Evaluation-Understanding C Language Program

"C Programming Design" is an important professional basic course for students to opens the door to the intelligent world in the future. The teaching case takes the teaching process of "Understanding C Programming" as the carrier, designs a modern digital technology teaching case based on the OBE idea with the students as the center and the whole process of academic evaluation as the link. The design of the teaching case integrates online and offline teaching, builds a new five-in-one teaching system integrating education idea, teaching goal, teaching resource, teaching method and teaching evaluation, so as to achieve the goal of improving teaching efficiency and talent development quality. Finally, the implementation effect of the teaching case and the problems existed are analyzed, and the continuous improvement measures are proposed.
Experimental Investigation of Double-Sided Strain of Composites in Tensile Testing Using MultiCamera DIC

Composite materials are widely used in engineering because of their high strength, high modulus, and corrosion resistance. Accurately obtaining the mechanical properties of composites is helpful for their further development. However, in a uniaxial tensile test, the strain distribution between the front and back of the composite sheet sample may differ due to its heterogeneity, and its stress-strain curve depends on which side the strain is measured. To report the stress-strain curve more accurately, a multicamera digital image correlation (DIC) system was employed to measure the double-sided strain in this work. Two sets of 3D digital image correlation (3D-DIC) systems were placed in front of and behind the specimen to record the tensile process of the specimen, and a double-sided calibration technique was implemented to connect the two sets of 3D-DIC systems into a unified coordinate system. Young’s modulus, percent bending, and local strain history are analyzed based on the measurements of strain on both sides. The results show that the strain on both sides of the composite is not exactly same, especially in the strain concentration area. As a comparison, the strain distribution on both sides of the metal material is almost identical.
Methods for Determining Promising Areas of Research by Scientometric Analysis of Scientific Publications

The issues of identifying promising areas of scientific research, including scientometric methods, are considered. It has been revealed that scientometric analysis of scientific publications is used to evaluate the results of the activities of educational and scientific institutions. An analysis of the publication activity of Russian researchers, including in the priority area of the Strategy for Scientific and Technological Development of the Russian Federation, is presented. The necessity of developing a national system for evaluating scientific activity is shown.
La Scène au temps de l’Intelligence Artificelle

"The study makes a theoretical-predictive analysis of the transformations that the use of Artificial Intelligence and new media will generate in a medium time horizon in the theatre arts. Analysing some examples of contemporary creations in performing arts from Romania and other countries, it highlights different types of creativity through specific scenographic choices, dramaturgical compositions and spectator’s involvement, which break the tradition and draw a new horizon of scenic and performative experiences thanks to the intelligent use of new technologies and virtual realities. The directions targeted are aesthetic, professional and following the evolution of the act of reception. Keywords: Artificial Intelligence, theatre and new technologies, live and virtual performances, online broadcast, virtual set design, immersive theatrical experience."
LEARNING OF SOLVING SUSTAINABILITY CHALLENGES IN DIGITAL ENVIRONMENTAL ENGINEERING STUDIES

The study summarizes the green project management digital teaching and learning methodology and achievements so far, which are integrated and generated in the B.Sc. and M.Sc.levels in environmental engineering study programs of the LAB University of Applied Sciences (LAB), Finland.It is important to share the experiences internationally.The paper introduces the theoretical green management methods, which serves as a framework for the practical implementation in the sustainability challenges' courses.The practical implementation takes places in project form, where the companies and public bodies have the role of the sustainability challenges' commissioners.The commissioning organizations define the sustainability challenges for the students.The data collection for the study started in the spring 2019 and now, in the third year, it is time to report about the mid-term results of the study.The study has been carried out as an in-depth, multi-method case study collecting data from the students and commissioners and in oral and written form.The study introduces four project courses as practical implementation cases.The results give material and guidance for the further development of green project management studies.So far very few scientific papers report about the practical implementation of project form teaching and learning in green project management sphere in technology studies.The originality of the introduced concept is based on the originality of the regional strategy, LAB's role in the regional development system and own innovative ways to carry out problem and project-based learning.The relevance of the topic area is increasing as the ability to act as a sustainability maker is becoming as a norm, soon not to be "a special agent's work" solely.The main limitation of the study is the uniqueness of each project case and the amount of the gathered project cases so far.
EXPERIENCING FLOW IN VIRTUAL REALITY: AN INVESTIGATION OF COMPLEX INTERACTION STRUCTURES OF LEARNING-RELATED VARIABLES

Theoretical background: Learning in virtual realities (VR) has become increasingly important.In this context, VR appears to be particularly conducive to affective learning objectives, such as perspective-taking in VR actors.Previous research has often focused on investigating cause-effect relationships that focus on the direct effects of different VR visualization technologies on learning outcomes.Little research has been conducted on more complex constellations of learning-related variables.Therefore, the present study aims at elaborating a research design that can be used to investigate both the direct effects of VR visualization technologies as well as the mediating effects of learning process variables latent in VR.For this purpose, the research design will be experimentally tested by comparing head-mounted display (HMD)-based VR with laptop-based VR with respect to the intended learning objectives, and controlling for influences by underlying learning processes (here: the experience of flow within VR).Methods: 132 students of grades eight and nine were recruited.The subjects were assigned to experimental conditions (HMD vs. laptop).The VR content dealt with the exploration of the hiding place of Anne Frank at the time of World War II in Amsterdam.Questionnaires were used to collect several data including sociodemographic characteristics, knowledge gain, perspective-taking, subjective evaluation, and the learning process variable flow.Results: A significant main effect for the variable VR visualization technology was uncovered averaged across all learning indicators.However, a superiority of HMD-based VR was found for the two evaluative indicators and also for the affective learning indicator.For the cognitive learning indicator, the effect was reverse.More relevant than unidirectional relationships are the mediating effects.Mediating effects through the experience of flow were discovered several times.Thus, the experience of flow within VR can significantly explain the cause-effect relationships between VR visualization technology and learning outcomes, even if, for the most part, only effects for evaluative indicators could be determined. Conclusion:The present study was able to exemplify that the investigation of complex interaction structures of VR visualization technologies and learning process variables can make a large contribution to the understanding of learning in VR environments.Advantages of HMD-based VR over laptop-based VR with respect to some learning indicators were uncovered.The significant findings of the mediation analyses point to the fact that the direct effects of VR visualization technologies on parameters of learning can be significantly explained by learning process variables such as flow experience and are systematically overestimated if such learning processes are not taken into account.
AI ADOPTION IN HEALTHCARE: TRUST AND PRIVACY CONCERNS

This study aims to provide an empirical investigation of different factors that could affect AI adoption in healthcare.Since prior research has overlooked AI adoption in healthcare, especially in the Arab context, this research in progress can fill this untapped gap and develop a theoretical framework to evaluate the customer-concerned factors (i.e., trust and privacy concerns) across different healthcare services.Due to its good generalizability, a theory of planned behavior can fit well to the healthcare context and hence can provide an adequate theoretical lens to understand the phenomenon of AI adoption.This study can expand our knowledge of AI and highlight the importance of its role to reinforce the provided healthcare services as well as meet the pressing patients' needs for better quality.
DESIGN AND IMPLEMENTATION OF A CHATBOT AS A TOOL TO ASSIST A HELPDESK TEAM

The existence of internal helpdesk teams is a common occurrence in companies nowadays, especially when considering the IT sector.These teams are an expensive resource and are only able to serve a limited number of users at a given moment, which evidences the importance of helpdesk teams operating as efficiently as possible.A common occurrence in the daily operations of these teams consists in the existence of a set of repeated tasks that could be automated through the usage of a chatbot capable of acting on behalf of helpdesk team members.By allowing a chatbot to perform some of these repeated actions, helpdesk teams are able to focus on other tasks, thus allowing to increase their productivity.Additionally, the usage of chatbots to assist a helpdesk team creates a highly available tool, capable of giving answers in a short time frame.In this paper, the design and implementation of such a tool is presented, including concepts and approaches related to chatbot development.As a result, a fully functional chatbot named Triton was produced, capable of helping employees of a consulting company with helpdesk-related problems and questions.
A SYSTEMATIC REVIEW OF RESEARCH ON EFL ONLINE LEARNING: EFFECTIVENESS, CHALLENGES, LEARNING TOOLS, AND SUGGESTIONS

Due to COVID-19 pandemic, educational departments such schools, colleges, and universities have been experiencing significant disruptions.All teaching and learning activities of face-to-face learning have switched to online learning.This study provides a systemic review of the literature with the aim to investigate the effectiveness, challenges, learning tools, and suggestions in EFL online learning based on an analysis of 40 published article journal from 2019 to 2022.This study has not yet been reviewed in the literature as part of a systematic review of studies.This study is based on Kitchenman's (2009) systemic literature review (SLR) approach, which allows it to gather data from a variety of sources.The dataset has been collected from SCOPUS databases.These data were analysed using Creswell's (2012) content analysis.The research findings revealed that all e-learning tools (e.g., Voki, LMS 'TES Blended Space', Pigai, Zoom meeting etc) used by universities teachers were very effective and beneficials for learners.However, some challenges were also identified during learning activities such as students lack of learning climate, poor internet connections, financial problems, technical problems, students' demotivation etc.Thus, the research findings suggested three aspects should be considered, namely a) having extensive and high-quality online resource access, b) using online resources on a regular basis, and (c) not only students but also teachers are being trained to use ICTs-in.In addition, teachers need to keep working on reinforcing the connection between digital technology and education meanwhile students need to made aware that it is acceptable to use digital media even it the most formal of educational settings.To summarize, this study suggests that once the Covid-19 pandemic is ended, the use of technology integration on online learning in EFL classrooms can be extended for equitable quality education for students.
STUDENT ACCEPTANCE OF SOCIAL ROBOTS IN HIGHER EDUCATION - EVIDENCE FROM A VIGNETTE STUDY

Social robots have the potential to play a vital role in education.Student technology acceptance may be considered as a driver for their successful integration in educational processes.Based on the ICAP (Interactive, Constructive, Active, Passive) framework and research on social robot roles, we have developed eight vignettes describing scenarios for the use of social robots in higher education, e.g., teaching assistant.Students in an introductory university course completed the vignettes; they answered questions based on the unified theory of acceptance and use of technology (UTAUT), as well as on their ethical approval.Drawing from a sample of N = 361 students, we carried out confirmatory factor analysis.In line with the conceptual basis, eight scenarios can be described.Overall, students do not accept social robots as presenters; however, the assessment of other roles, such as teaching assistant, is positive.By means of a latent profile analysis, we were able to identify four profiles that may be meaningful from a conceptual point of view.One profile is particularly noteworthy.It comprises students who accept social robots not as a means of instruction, e.g., as a tutor, but as a tool, e.g., to promote computational thinking.