Reviewerly/exHarmony huggingface dataset:
A REVIEW ON: IoT BASED PORTABLE ECG MONITORING SYSTEM FOR SAMRT HEALTHCARE

Emergency unit ICU is where the patients who are basically sick and conceded for therapy. For such basic circumstances the Specialists need to make some all-memories update patients wellbeing related boundaries like their circulatory strain, heartbeat ,temperature Asthma ,Environment and saline is full or not. Doing physically is too drawn-out an undertaking and furthermore for various patients it turns out to be near unthinkable. For this sort of circumstances this IoT based framework can achieve a robotization that can keep the specialists refreshed all time over the network. IoT Based ICU Observing Framework is an Arduino based framework which gathers patients data with the assistance of few sensors. The sensors which are organized, either worn on the patient’s body or implanted in our living surroundings, change the get- together of information inductive of our physical and mental state. Internet of Things (IoT) based savvy wellbeing checking framework is a patient checking framework in which a patient can be observed 24 hours. In the present world, Health observing frameworks are one of the most remarkable uses of IoT. In ICU, patient observing is basic and most significant movement, as little postpone in choice connected with patients’ treatment might cause super durable handicap or even demise. The majority of ICU gadgets are outfitted with different sensors to quantify wellbeing boundaries, however to screen it all the time is as yet testing position.. Keywords: EMG sensor ,ECG sensor,LM35 Thermister sensor, Humidity sensor DHT-11,Load Cell,Arduino Uno, Android App, Load Cell,Buzze ,Button.
A Reference Framework for Variability Management of Software Product Lines

Variability management (VM) in software product line engineering (SPLE) is introduced as an abstraction that enables the reuse and customization of assets. VM is a complex task involving the identification, representation, and instantiation of variability for specific products, as well as the evolution of variability itself. This work presents a comparison and contrast between existing VM approaches using qualitative meta-synthesis to determine the underlying perspectives, metaphors, and concepts of existing methods. A common frame of reference for the VM was proposed as the result of this analysis. Putting metaphors in the context of the dimensions in which variability occurs and identifying its key concepts provides a better understanding of its management and enables several analyses and evaluation opportunities. Finally, the proposed framework was evaluated using a qualitative study approach. The results of the evaluation phase suggest that the organizations in practice only focus on one dimension. The presented frame of reference will help the organization to cover this gap in practice.
MULTIMODAL DISCOURSE ANALYSIS OF SARCASM IN ENGLISH MEDIA AND NEWS ARTICLES

With the rapid development of information technologies, the implementation of visual content has become a complementary component of social discourse, particularly in the media and news sectors. In this respect, it is increasingly important to pay huge attention to media literacy and relevant information processing. Sarcasm, one of the most widely used language choices in social discourse, can easily be a part of any media or news article. Sarcastic remarks are used for numerous reasons, namely, to indirectly express contempt, pretend an attitude, mock a situation or a person, or perhaps, they are more creative solutions to anger-provoking situations. The aim of the present research is to detect sarcastic messages in media and news articles through the multimodal markers of the discourse. The dataset analyzed to achieve the above-mentioned goal is derived from American and British media and news platforms Politico, The Guardian, and The Sun.
Applied Harmonic Analysis and Data Science

Data science has become a field of major importance for science and technology nowadays and poses a large variety of challenging mathematical questions. The area of applied harmonic analysis has a significant impact on such problems by providing methodologies both for theoretical questions and for a wide range of applications in signal and image processing and machine learning. Building on the success of three previous workshops on applied harmonic analysis in 2012, 2015 and 2018, this workshop focused on several exciting novel directions such as mathematical theory of deep learning, but also reported progress on long-standing open problems in the field.
Geometry and Optimization in Quantum Information

Quantum information theory seeks to understand the fundamental limits set by quantum mechanics for information processing tasks. The mathematical aspects of quantum information rely on tools from various fields including mathematical optimization, high-dimensional convex geometry, operator algebras and representation theory. The goal of this meeting is to focus on the mathematical aspects connecting geometry, optimization and quantum information theory and develop new tools to solve some of the open problems at the intersection of these fields.
When Big Data Meets NFT

Non-fungible tokens (NFTs) are unique digital assets that are based on smart contracts of blockchain technology and traded via cryptocurrencies. They became known to the public in 2021 and kept growing rapidly. Until the second quarter of 2022, the total volume of NFTs has reached $12.22 billion. However, since the NFT market is still in its early stage, there are limited studies on this topic. In this paper, the first purpose is to analyze the overall market structure and volatility, characteristics of top NFTs on famous marketplaces, and future trends of NFTs. The next focus is to summarize current research trends about the concept of NFT, investigate the challenges faced by researchers, and provide current data collection or feature extraction techniques that are frequently utilized to solve those challenges.
Improvement of conjugate gradient methods for removing impulse noise images

Optimization problems occur in most disciplines like engineering, physics, mathematics, economics, administration, commerce, social sciences, and even politics. The conjugate coefficient is the cornerstone of conjugate gradient algorithms with the desired conjugate property. In this study, we discovered fresh second order information for the Hessian from the target function, which might lead to a new search direction. Based on a unique search direction, we proposed the update formula and nonlinear conjugate gradient technique. Under Wolfe line search and moderate objective function assumptions, the strategy has acceptable descent property and is always globally convergent. According to numerical results, the technique is successful and competitive in recovering the original picture from an image corrupted by impulsive noise.
Intersubject integration of humanities and management cycle disciplines

The problem of interdisciplinary educational integration reflects the realities of the modern world. Rapidly changing technologies set new requirements for the training of specialists capable of innovation. The study of the vertical and horizontal interdisciplinary integration resources for the disciplines of the humanities and management cycle is the purpose of this work. The structure of the article includes a theoretical and methodological analysis and an empirical part, represented by two stages of research (for socio-humanitarian and management disciplines). The article presents the results of modern views on the problem of interdisciplinary integration in higher education. The results of the theoretical analysis showed a mismatch between the depth of the theoretical study of the problem and the success of their practical implementation in the conditions of modern higher professional education. The complexity of the implementation is due to the changed psychological and socio-psychological characteristics of Generation Z students. Based on the theoretical analysis, a typology of interdisciplinary competencies was proposed. The aim of the empirical part of the study was to study the resources of vertical and horizontal integration at a technical university. Based on the empirical research, the possibilities of vertical and horizontal integration of the disciplines of the socio-humanitarian and management cycle were identified. An integrative matrix is presented. A promising direction for this topic is the development of an integrative module “Management workshop”.
Variational selective segmentation model for intensity inhomogeneous image

Variational selective image segmentation models aim to extract a particular object in an image depending on a set of user-defined prior points. The current model suffers from high computational costs due to the traditional total variation function that results in a slow segmenting process. In addition, it is not designed to segment images with intensity inhomogeneities. In this research, we formulate a new variational selective image segmentation model based on the Gaussian function. A Gaussian function is proposed to replace the traditional total variation function to regularize the variational level set function. To segment images with intensity inhomogeneities, the local image fitting idea was in corporate into the formulation. The efficiency of the proposed model was then assessed by recording the computation time while the accuracy was measured using Jaccard and Dice similarity values. Numerical experiments using synthetic, natural, and medical images demonstrate that the proposed model is about 6 times faster than the existing model, while the Jaccard and Dice values are about 11% and 7% higher, respectively, compared to the existing model. In the future, this research can be extended further into a 3-dimensional modeling and vector-valued image framework.
Ricerca scientifica e dati genetici

The paper aims to study the relationship between scientific research and protection of fundamental right of the human being. In particular, it focuses on the concern linked to the use of the genetic data in the field of the predictive medicine and discusses how current legal frameworks and technical safeguards can mitigate the risks connected to new technologies, artificial intelligence, and digital communication.
Accurate Diagnostic Tissue Segmentation and Concurrent Disease Subtyping with Small Datasets

Abstract · Purpose: to provide a flexible, end-to-end platform for visually distinguishing diseased from undiseased tissue in a medical image, in particular pathology slides, and classifying diseased regions by subtype. Highly accurate results are obtained using small training datasets and reduced-scale source images that can be easily shared. · Approach: an ensemble of lightweight convolutional neural networks (CNNs) is trained on different subsets of images derived from a relatively small number of annotated whole-slide histopathology images (WSIs). The WSIs are first reduced in scale to preserve anatomic features critical to analysis but also facilitate convenient handling and storage. The segmentation and subtyping tasks are performed sequentially on the reduced-scale images using the same basic workflow – generating and sifting tiles from the image, then classifying each tile with an ensemble of appropriately trained CNNs. For segmentation, the CNN predictions are combined using a function to favor a selected similarity metric, and a mask or map for a a candidate image is produced from tiles whose combined predictions exceed a decision boundary. For subtyping, the resulting mask is applied to the candidate image, and new tiles are derived from the unoccluded regions. These are classified by the subtyping CNNs to produce an overall subtype prediction. · Results and conclusion: this approach was applied successfully to two very different datasets of large WSIs, one (PAIP2020) involving multiple subtypes of colorectal cancer and the other (CAMELYON16) single-type breast cancer metastases. Scored using standard similarity metrics, the segmentations outperformed more complex models typifying the state of the art.
Online Learning as an Alternative Learning Modality in Ecuador’s Education Institutions Amidst Crises and Outbreaks: A SWOT Analysis

The primary objective of this paper is to analyse the weaknesses, threats, strengths, and opportunities (SWOT matrix analysis) of online teaching and learning in educational institutions (primary, secondary, and higher education) in Ecuador during the COVID-19 pandemic. A qualitative study of the descriptive documentary type was conducted, where the information obtained from virtual education in times of crisis and outbreaks was collected and analysed. It was found that educational centers at different levels of schooling still continued to use traditional teaching methods and processes subject to old procedures as a basis for their incursion into virtuality, although other establishments began to handle combined modalities such as blended and hybrid online (e-learning) modes of learning. In conclusion, the deadly coronavirus disease (SARS-COV-2) abruptly halted much human activity worldwide. This forced hesitant educational systems that still used traditional face-to-face teaching methods in classrooms to move and change to online learning in many regions, regardless of the socioeconomic level and demographics of the students. Aside from determining how to deal with the problems and challenges of virtual education during epidemics and natural disasters, it is clear that the number of technology-driven educational start-ups has increased significantly.
Students' Perception about the Use of an Educational Web Application During the COVID-19 Pandemic

SARS-CoV-2 virus has caused universities to update their courses in the distance modality. The general aim of this mixed research was to build and analyse the use of a web application for the educational process about the t-test considering data science. In particular, the professor of the Teaching of Mathematics II course needed to update the school activities because of the new educational demands caused by the COVID-19 pandemic. To facilitate the educational process of math, this teacher decided to build a web application that presents the formulas and calculation of the mean, standard deviation and statistical error to understand the use of the t-test. This technological tool allows the personalisation of learning through the simulation of data. The participants were 42 students from a Mexican university. The results of machine learning indicated that the contents of the web application positively influenced the assimilation of knowledge, satisfaction during the learning process, development of mathematical skills and learning in the distance modality. The decision tree technique allows the construction of four (4) predictive models about the use of the web application for the educational process about the t-test. Finally, educators have the opportunity to improve the teaching-learning conditions during the SARS-CoV-2 virus through the design and construction of web applications.
A Systematic Study of the Literature on Career Guidance Expert Systems for Students: Implications for ODL

The continual evolution of employment opportunities in the present industrial era has raised the need for career-long expert advice. Similar to other fields, thankfully technology has come to our rescue in the area of career guidance also. This paper presents a systematic review of Expert Systems (ES) developed for career guidance, course selection and evaluation of students in the past ten years. The popular research databases Google Scholar and Science Direct were used for obtaining the relevant research papers through broad keywords. The keywords were refined to identify the articles related to rule-based, case-based and fuzzy logic-based ES used for career guidance. A total of twenty-five peer-reviewed relevant articles with full-text available online was selected for the final study. In order to avoid duplicity, technical reports and unreferenced literature were excluded. The review identifies the relatively high weight given by the researchers to rule-based systems owing to their simplicity and broad applicability. However, the relative merits and demerits of rule-based, case-based and fuzzy logic-based ES are highly dependent on the field of application. Nevertheless, ES find wide applications in the area of career guidance and have the potential to enhance the career guidance accessibility of the most remote students.
Promotion of Reading through Distance Learning Service

This paper explores how the operations of an academy library got transfigured during closures due to COVID-19. The author offers tips on how to maintain access to resources, how to engage students in learning, and support academy staff. This paper reflect how the GEMS Modern Academy school library promoted reading during distance learning.
Students' Information Gathering Behaviors and Teachers' Approaches

The purpose of this study is to reveal students’ information gathering behaviors and processes and teacher's approaches to improve their learning outcomes in middle school inquiry-based learning. We compare the dif erences in behaviors in information gathering process between learners with high and low understanding of the themes as their learning outcomes and teachers' approaches for both types of learners.Participants were seventeen ninth grade students and two teachers as their supervisors. During the Period for Integrated Studies, the students searched for information, reflected on their behaviors, and summarized the information they gathered on slides. The analysis targeted seven students who selected themes related to SDG 4 (four students) and SDG 8 (three students) and two teachers. As a result of the analysis, students with a high level of understanding of the themes showed behaviors of browsing and referring to materials corresponding to pillars of investigation (sub-subjects). For the behavior, teachers provided students with supplementary guidance on keyword searches and introductions to websites.
Meta Analysis on The Correlation Between Listen-Read-Discuss (L-R-D) and Reading Comprehension

One of the English language skills is reading. Reading is an activity in the cognitive process to get information in writing. Reading comprehension is a reading skill that is on a higher order. Reading comprehension is cognitive reading (reading to understand). There are many strategies for learning reading comprehension. One of them is the listen-read-discuss strategy. Listen-Read-Discuss (LRD) is a strategy that helps students build strong background knowledge to understand texts. The present study represents a meta-analysis of research results on the application of listening-reading-discussion strategies in reading comprehension. This meta-analytic study is a study that uses secondary data in the form of data from previous research findings. This research can be called follow-up research in the form of research reports and literature analyses. The tool of this research work is documentation, namely to obtain information about the application of the practice of listening-reading-discussing in reading comprehension. Data collection techniques were collected using a documentary technique. The technical data analysis used is descriptive, and to assess the sensitivity of the result of the so-called robust meta-analysis. From all 12 (twelve) studies, we can conclude that all studies show a good correlation with the listen/read/discuss (LRD) strategy in terms of reading comprehension. This means that LRD strategy helps learners to understand writing.
IMPROVING STUDENTS’ ABILITY IN WRITING RECOUNT TEXT THROUGH MULTIMODAL LEARNING MANAGEMENT

This research is concerned with Improving Students’ Ability in Writing Recount Text through Multimodal Learning Management [MLM]. This research is conducted by adopting qualitative and quantitative designs. Specifically, the researcher adopts experimental research under the quantitative design. The respondents of this research are 64 students at Junior High School of Sumbawa in the Academic Year 2020/2021. The research instrument used in collecting qualitative data is a questionnaire, and in collecting quantitative data is a test. The research results show that 1. Most of the students are more active and enthusiastic during the process of teaching and learning when the media is applied, 2. The students’ writing score test increases after being taught with multimodal learning management and also have good responses toward the learning teaching process, and 3. The students’ mean score of pre-test is 68.72 and post-test is 86.22 in experimental class. Meanwhile, the students’ mean score of pre-test is 63.75 and post-test is 76.03 in control class. There is significant increase of the student’s score in the post-test of experimental compared to the students’ score in the control class. Therefore, it is concluded that MLM can improve students’ writing recount text.
DEVELOPING DIGITAL WRITING TEST FOR STUDENTS OF ENGLISH AND LITERATURE DEPARTMENT

This study was aimed at developing Digital English writing test by using Quia or Quizziz for semester 2 students in Academic Year 2021/2022. Quizizz application is a digital application that has a colorful display with animation on its interface. This writing test is to find out the students’ initial ability in writing text, such as making a simple outline and write them into a simple paragraph with main idea and topic determined by the researchers. This developing test is useful for students of English and Literature Department and developed as a pre-test for mapping freshmen students’ competency in writing. Research and Development design was used in this study, with the data used from the result of need analysis as well as interviewing the lecturers and students. The data were chosen by random sampling with the total number is 60 students of the second semester in Academic Year 2021/2022 of English and Literature Department as well as 4 lecturers of writing course.
THINK PAIR SHARE (TPS) STRATEGIES AND DIRECTED READING THINKING ACTIVITY (DRTA) TO IMPROVE STUDENTS’ MOTIVATION ON READING COMPREHENSION ACHIEVEMENT

The research deals with teaching strategies and students’ motivation on reading comprehension achievement. The objective of this study is to reveal that the students’ achievement in reading comprehension taught by Think Pair Share is significantly higher than that taught by Directed Reading Thinking Activity using question answer relationship teaching study. The research method used is quantitative research design. The populations of this research are the students of Midwifery Grade II at STIKes Mitra Husada Medan, Academic year 2021/2022. Two classes are randomly selected and the total samplings are 72 students. The instruments used are multiple choice test and questionnaire. The data analyzed are by Two-Way ANOVA with 2x2 factorial designs with F observed = 14.1 and F table = 4.00, and the results indicated the Fobserved Ftable, so null hypothesis (Ho) was rejected. It is concluded that the students taught by using TPS strategy is better than those taught by using Directed Reading Thinking Activity on reading comprehension achievement. Based on this research, researchers recommend using TPS learning model because this learning model can improve students' ability to solve reading comprehension problems. This model can also be used as an alternative to apply innovative English learning, especially in reading, creating an exciting learning atmosphere, and also providing opportunities for students to express their ideas in English in their own way.