Reviewerly/exHarmony huggingface dataset:
Perceived technology integration of Filipino teachers teaching in the U.S.A.

Integrating technology, pedagogy, and content knowledge in teaching is a challenging but fulfilling undertaking. Teachers in the 21st century are expected to integrate technology in the classrooms effectively, while at the same time being knowledgeable in the pedagogy and content of the subjects being taught. The purpose of this study is to describe the perceptions of Filipino teachers teaching in the U.S. on the Technological Pedagogical Content Knowledge (TPCK) framework. Data was gathered through a survey using the Questionnaire to Measure Perceived Technology Integration Knowledge of Teachers by Hosseini &amp; Kamal (2012). The study found that among the TPCK’s subscales, the mean for pedagogical knowledge (PK) was the highest (4.34), while the mean for technological knowledge (TK) was the lowest (4.03).
Utilizing critical events to guide abstractive multi-document summarization

Abstract The authors have requested that this preprint be removed from Research Square.
Factors Influencing Learner's Motivation in Mobile Learning Environment: An Empirical Study on University EFL Learners

International students have greater difficulties navigating a variety of academic and social contexts than host nationals.Language-related issues were among the most often cited difficulties by students studying abroad.Many students who study abroad and rely on language to navigate have a tough time learning a new language.If asked to identify the most significant influence on language learning, most teachers and students would likely name motivation.Teachers of foreign languages frequently use the term "motivation" when describing successful or failed students.Nonetheless, there is no doubt that motivation is the most complex and difficult challenge confronted by language learners, particularly in this digital age.Numerous research on the efficiency of computer-assisted language learning have been conducted in recent years, and the literature on the psychology of language learning is vast.Regarding the psychological aspect of a mobile-assisted language-learning environment, relatively little research has been undertaken.In addition, few research have directly examined the attitudes of international students regarding the usage of mobile devices to motivate their foreign language learning.The purpose of this article is to investigate Chinese over-seas university students' use of MALL applications to motivate foreign language learning in Germany.The study evaluated whether and how perceived tools or activities can affect learners' motivation to acquire foreign languages in informal settings.In addition, the reasons why mobile devices motivate language use and specific types of motivation are investigated.
effectiveness of normal birth e-module on increasing nursing students’ learning satisfaction

Introduction: E-modules are information technology-based modifications of conventional modules. Students learning satisfaction can be used to gauge learning success. Learning satisfaction is the value of comparing the level of reality with the level of expectation in learning. This study aims to determine the effectiveness of the normal birth e-module in increasing nursing student learning satisfaction.Methods: Pre-experimental with a one-shot case study design with data from online survey results using the E-Module Satisfaction Questionnaire instrument. The population of this study was 244 active students of the Faculty of Nursing, Universitas Padjadjaran, who had completed the maternity course using the normal birth e-module. As many as 46 people were willing to be respondents and fill out the level of learning satisfaction questionnaire. The study samples were determined by G-Power and data analysis using the Pearson Correlation test and the R square test or the Coefficient of Determination.Results: Overall, the level of learning satisfaction of nursing students who use the normal birth e-module is very satisfactory/high, with an average of 4.51% in the range of 4.5 - 5.0. The percentage of the results of the assessment of all respondents was dissatisfied (0.2%), quite satisfied (5%), satisfied (38.7%), and very satisfied (56.1%). However, 2.2% or 1 out of 46 participants reported dissatisfaction with the respondent’s understanding. The coefficient of determination or R square test of 0.876 or 87.6% is in the 0.8 – 0.99 (very effective).Conclusions: The use of the Normal Birth E-Module is very effective as a learning method that can increase the level of learning satisfaction of nursing students.Keywords: e-module; learning satisfaction; normal birth; nursing students
Improved classification of breast cancer data using hybrid techniques

Breast cancer is the second leading cancer for women in developed countries including India. Many new cancer detection and treatment approaches were developed. The most effective way to reduce breast cancer deaths is detect it earlier. The frequent occurrence of breast cancer and its serious consequences have attracted world wide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an Ada Boost-SVM classification algorithm, Combined with k-means is proposed in this research for the early diagnosis of breast cancer. The effectiveness of the proposed methods are examined by calculating its accuracy, confusion matrix which give important clues to the physicians for early diagnosis of breast cancer
Optimized Prediction of Melanoma Using Machine Learning

Life span of an average person at present is 60 to 75 years but lot of people are expiring due to accidents or because of the health issues they have. One of the main health issues that people are experiencing is skin cancer, it will give only five-year life to the people who are suffering from skin cancer. 5 years’ life we can’t live at our satisfaction people also need regular diagnosis to make their five years of life. Not every person dying because of the skin cancer, only the person who are unaware of their situation are one facing serious problems, so to overcome this problem we are making a machine learning model that makes use of pictures to predict the actual problem of the person.
The use of cardiometric and electrodermal activity indicators for the attestation of visual online content

The article presents some results of an interdisciplinary study aimed at clarifying the conditions for the use of cardiometric and electrodermal indicators of human activity as a means of attesting to visual online content. To record personal reactions to selected visual objects, the authors use a range of tools, including computer cardiometers, portable eye trackers and polygraphs of various modifications. To achieve this goal, the authors conducted a multi-level test of Russian youth using current political symbols and images from online communities and other social media platforms. The results of the study prove the high efficiency of using cardio-oculometric detection combined with automatic recording of electrodermal activity to obtain reliable information about affective and rational-logical features of perception of visual online content. The study also extends the research understanding of the specifics of using this combination of diagnostic tools in different conditions.
Leukocytes Classification Using Convolutional Neural Network

Maintaining a healthy life is very important in today’s world. Our body has ability to fight against contagious diseases with the aid of white blood corpuscles (WBC) which generates natural immune system. Preserving a good WBC count is crucial as it leads to various hematological problems, one among them is Leukemia, a condition which results in blood cancer, huge accumulation of WBC cells in bone marrow, an abnormal growth of WBC cells, hinders natural immune system fighting against infectious diseases. White blood cells can be categorised into Eosinophils, Lymphocytes, Monocytes, Neutrophils and Basophils. In this study, a pre-trained Convolutional neural network architecture called Le Net is used to automatically classify WBC cells. It efficiently classifies WBC cells from the given input sample blood cell images compared to other pre trained models such as Alex-Net and custom-built CNN called white capsule net. The hyper parameters Le Net are fine tuned to yield higher accuracy of 96%.
Internet of things and cloud controlled remote health management system in telemedicine for healthcare facilities

Telemedicine, one of the most well-liked and quickly evolving medical applications, enables patients and the general public to access better and quicker medical care. To enable the process of acquiring detailed information indicative of our physical and mental health for the optimum patient health monitoring, networked sensors are either wearables or implanted in our everyday environments. This project provides a detailed explanation of the methodology used as well as a number of design factors that should be taken into account when developing a patient health monitoring system. This method records the patient's vital signs, such as their heart rate and temperature, and sends an alert to the desired smartphone app.The proposed system is primarily intended for the countryside Indian population who suffers from hypertension and hypotension. It is also programmed to detect pre-cardiac arrest situations so that people in remote areas can take precautions and seek help from a medical or healthcare unit. Biomedical and photo-electronic sensors are employed to record the input parameters, with the analytical portion being handled by a computer. Live server demonstrations of the outcomes are made.
Research on the monitoring mechanism of pricing discrimination using big data versus existing platform customers under the quantum entanglement state

Abstract Big data is the fuel for the platform economy to take off, but it also brings the increasingly hidden and complex problem of big data-enabled price discrimination against existing customers. The quantum cognition and decision method can solve the sequential effect and interference effect caused by this problem, and make the decision maker form a reasonable strategy. The game model of big data "killing" is constructed from the perspective of platform e-commerce regulation, and the final strategy choice is "complete regulation and fair pricing", and the quantum entanglement theory is used to explain the strategy choice of both sides of the game under quantum entanglement state. The results show that the intrinsic mechanism of the regulation of big data "killing" behavior has quantum properties based on platform e-commerce and platform sellers; the regulation effect is better after considering quantum entanglement state, which can solve the problems of platform inaction and seller speculation to a certain extent and further optimize the online market trading environment.
The Semantic and Pragmatic Functions of /bʕdɪn/ “بعدين” in Informal Arabic

This study aims at investigating the functions of the informal Arabic discourse marker “بعدين” /bʕdɪn/. By figuring out these functions and highlighting the common used ones, this paper tries to help communicators to be aware of those functions in their communication and avoid any possibility of misunderstanding. “Twitter” was the main tool of collecting the data of this study. This social medium provided natural communication contexts that significantly assisted the analysis of this study. The dataset consisted of one hundred and five tweets containing /bʕdɪn/. Those tweets reflected texts written by native Arabic speakers from different nationalities. The analysis was guided by the relevance theory and the methodology of related literature. The findings of the study showed that /bʕdɪn/ has semantic meanings similar to the English adverbs “then” and “later.” It was used semantically as a temporal adverb putting actions in sequence or delaying a move to unspecified time in the future. /bʕdɪn/ was also used as a coherence marker supporting the value of the communicative situations. In this respect and based on the collected data, seven pragmatic functions were elicited in this study for /bʕdɪn/. Those functions were: a marker of orientation shift, a marker of result, a conditional marker, a marker of disagreement, a coordination marker meaning “but,” a marker of agreement and a marker of reason.
Robust Image Watermarking With Quaternion Fractional-Order Polar Harmonic-Fourier Moments Based On Wavelet Transformation: Resistance Against Rotation Attacks

This study presents a zero-watermarking algorithm that can resist rotation attacks. The algorithm uses quaternion fractional-order polar harmonic-Fourier moments (QFr-PHFMs) based on wavelet-transformation. First, the wavelet-transformation is applied to each component of the host image, which is in RGB three-channel color. The low-frequency sub-bands of each component are then extracted and represented using quaternion algebra. Multiple QFr-PHFMs are calculated, and the invariants of the QFr-PHFMs are utilized to establish the watermark system. The watermark extraction process is also simplified. The detection of the image requires a two-level wavelet transformation, followed by the calculation of multiple invariant moments of the low-frequency sub-image. The experimental results are shown and compared with similar methods. Simulations show that this method can produce high-quality visual effects and withstand noise, filtering, JPEG compression, and cropping attacks.
Acoustic as an Abelian U(1) local gauge theory

We propose that the acoustic pressure could be treated as a scalar field and acoustic could be treated as an Abelian U(1) local gauge theory.
Learning Media Development: Covid-19 Supplement Book-Based Science Literacy

The purpose of this study was to determine the feasibility of the Covid-19 Supplement book on virus material and the effectiveness of the Covid-19 Introduction Supplement book in improving student learning outcomes. The development research method (Development &amp; Research) produces products, so this research is oriented towards product development which has the process of describing it as well as possible and then its feasibility will be validated. In this study uses the ADDIE research model which includes the analysis, design, development, implementation, and evaluation stages. The subjects in this study were three expert validators (materials experts, linguists, and design experts), then teachers and class X students at SMA Negeri 1 Hutabayuraja. The results of this development research show that the Covid-19 Supplementary book on virus material was deemed appropriate by material experts, linguists, and graphic design experts, and the application of the Covid-19 Supplementary book on virus material can effectively improve student learning outcomes with student classical mastery of 75, 6%.
GPU-accelerated Dynamic Time Warping for Selective Nanopore Sequencing

Abstract The design and supply of RT-PCR primers for accurate virus testing is a complex process. The MinION is a revolutionary portable nanopore DNA sequencer that may be used to sequence the whole genome of a target virus in a biological sample. Human samples have more than 99% of non-target host DNA and Read Until is a protocol that enables the MinION to selectively eject reads in real-time. However, the MinION does not have any in-built compute power to select non-target reads. SquiggleFilter is a prior work that identified the accuracy and throughput challenges in performing Read Until using the state-of-the-art solution and proposed a hardware-accelerated subsequence Dynamic Time Warping (sDTW) based programmable filter on an ASIC. However, SquiggleFilter does not work for genomes larger than 100Kb. We optimize SquiggleFilter’s sDTW algorithm onto the more commonly available GPUs. DTWax better uses tensor core pipes, 2X-SIMD FP16 computations and efficient data handling strategies using offline pre-processing, coalesced global memory loads, warp shuffles and shared memory buffering among other optimizations. DTWax enables Read Until and yields ∼1.92X sequencing speedup and ∼3.64X compute speedup: costup over a sequencing workflow that does not use Read Until.
Kalman Recursions Aggregated Online

In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions. We restrict ourselves to the case where expert predictions come from Kalman recursions, fitting state-space models. By using exponential weights, we construct different algorithms of Kalman recursions Aggregated Online (KAO) that compete with the best expert or the best convex combination of experts in a more or less adaptive way. We improve the existing results on expert aggregation literature when the experts are Kalman recursions by taking advantage of the second-order properties of the Kalman recursions. We apply our approach to Kalman recursions and extend it to the general adversarial expert setting by state-space modeling the errors of the experts. We apply these new algorithms to a real dataset of electricity consumption and show how it can improve forecast performances comparing to other exponentially weighted average procedures.
Firefly: A Browser-based Interactive 3D Data Visualization Tool for Millions of Data Points

Abstract We present Firefly, a new browser-based interactive tool for visualizing 3D particle data sets. On a typical personal computer, Firefly can simultaneously render and enable real-time interactions with ≳10 million particles, and can interactively explore data sets with billions of particles using the included custom-built octree render engine. Once created, viewing a Firefly visualization requires no installation and is immediately usable in most modern internet browsers simply by visiting a URL. As a result, a Firefly visualization works out-of-the-box on most devices including smartphones and tablets. Firefly is primarily developed for researchers to explore their own data, but can also be useful to communicate results to researchers and/or collaborators and as an effective public outreach tool. Every element of the user interface can be customized and disabled, enabling easy adaptation of the same visualization for different audiences with little additional effort. Creating a new Firefly visualization is simple with the provided Python data preprocessor that translates input data to a Firefly-compatible format and provides helpful methods for hosting instances of Firefly both locally and on the internet. In addition to visualizing the positions of particles, users can visualize vector fields (e.g., velocities) and also filter and color points by scalar fields. We share three examples of Firefly applied to astronomical data sets: (1) the FIRE cosmological zoom-in simulations, (2) the SDSS galaxy catalog, and (3) Gaia Data Release 3. A gallery of additional interactive demos is available at alexbgurvi.ch/Firefly .
Peculiarities of the use of distance learning information technologies in higher education institutions of Ukraine

The article considers the process of informatization as one of the main directions in the development of higher education. Distance learning technologies that can be used in the traditional educational process to improve the quality of education are analyzed. The main attention is paid to the local (internal university) computer network. A description of the information resources of such a learning system is given. Problem: based on the analysis in the science of research, own research results, the research problem was formulated, determined by the following contradictions: the growing need between high-quality training of specialists and the lack of analysis of problems and prospects for the development of distance learning in Ukraine.
Optical Set up for Scanning System in Manufacturing Inspection

Abstract Essentially, 3D scanning systems are fundamental in today’s industry, since there is a necessity for the automation of the whole manufacturing process with the aim to achieve better results in terms of accuracy, quality, and manufacturing time. Indeed, experts move towards the integration of high-resolution sensors, and optical devices alongside software, which may offer several advantages for large manufacturing units. In this paper, a stereo vision system is demonstrated in detail while performing a scan. The developed model could be used to carry out several measurement experiments, and the measurement results are analyzed, which proved the correctness and feasibility of the method. Delivered results and outcomes are discussed.
Computational Paths -- An approach in the $LND_{EQ}-TRS_{2}$ system

We use a labelled deduction system ( LND$_{ED-}$TRS ) based on the concept of computational paths (sequences of rewrites) as equalities between two terms of the same type, which allowed us to carry out in homotopic theory an approach using the concept of computational paths. From this, we show that the computational paths can be used to perform the proofs of the $LND_{EQ}-TRS_{2}$ rewriting system.
Leveraging hardware-control imperfections for error mitigation via generalized quantum subspace

In the era of quantum computing without full fault-tolerance, it is essential to suppress noise effects via the quantum error mitigation techniques to enhance the computational power of the quantum devices. One of the most effective noise-agnostic error mitigation schemes is the generalized quantum subspace expansion (GSE) method, which unifies various mitigation algorithms under the framework of the quantum subspace expansion. Specifically, the fault-subspace method, a subclass of GSE method, constructs an error-mitigated quantum state with copies of quantum states with different noise levels. However, from the experimental aspect, it is nontrivial to determine how to reliably amplify the noise so that the error in the simulation result is efficiently suppressed. In this work, we explore the potential of the fault-subspace method by leveraging the hardware-oriented noise: intentional amplification of the decoherence, noise boost by insertion of identity, making use of crosstalk, and probabilistic implementation of noise channel. We demonstrate the validity of our proposals via both numerical simulations with the noise parameters reflecting those in quantum devices available via IBM Quantum, and also experiments performed therein.
A Structural Approach to Tree Decompositions of Knots and Spatial Graphs

Knots are commonly represented and manipulated via diagrams, which are decorated planar graphs. When such a knot diagram has low treewidth, parameterized graph algorithms can be leveraged to ensure the fast computation of many invariants and properties of the knot. It was recently proved that there exist knots which do not admit any diagram of low treewidth, and the proof relied on intricate low-dimensional topology techniques. In this work, we initiate a thorough investigation of tree decompositions of knot diagrams (or more generally, diagrams of spatial graphs) using ideas from structural graph theory. We define an obstruction on spatial embeddings that forbids low tree width diagrams, and we prove that it is optimal with respect to a related width invariant. We then show the existence of this obstruction for knots of high representativity, which include for example torus knots, providing a new and self-contained proof that those do not admit diagrams of low treewidth. This last step is inspired by a result of Pardon on knot distortion.
PRAGMATIC ADAPTATION OF HEADLINES IN MODERN MASS MEDIA

Карпенко Н. А