Reviewerly/exHarmony huggingface dataset:
A Study on the Relationship between Learning Styles and Experiential Learning Steps of the Online Co-curricular Program Learning Experiences from the Perspectives of Kolb Learning Theory

Objectives This study observed the relationship between the Kolb’s experiential learning theory according to the learning styles and experiential learning steps to understand the changes in learning styles and experiential learning steps during an co-curricular program progressed online during COVID-19 pandemic.&#x0D; Methods The data for this study has been collected during 3 semesters (Semester 1 of 2020 to Semester 1 of 2021) in the University in Gyeonggi-do province, South Korea. The collected data for the study are 111 essays used for a document analysis during this five-semester period. To analyze the learning styles on this online co-curricular program, two methods were employed. First, a categorization was performed according to the Kolb’s learning type using constant comparison, then the categorized data is expressed in a scatter plot based on the steps of Kolb’s experiential learning theory. Then, the characteristics of each learning type is presented according to the contents of the categorized data. Second, the categorized data that was analyzed to understand the experiential learning steps was then reconstructed to elucidate the relationship among the factors making up the leaning steps.&#x0D; Results There were high percentages of “divergers” and “accommodators” among the students in this online co-curricular program, but less of the “convergers” and “assimilators” in the student group. In the experiential learning steps, it is shown that a concrete experience stage allowed the students to experience positive learning through a negative learning experience, and a reflective observation stage allowed the students to modify their behaviors and refresh their negative emotions related to the studies. Additionally, students were able to utilize supporting resources to effectively use the program as well as applying them during an abstract conceptualization, and ultimately learn to generalize their study strategy during the active experimentation stage.&#x0D; Conclusions Current study elucidated that emotional support and other supporting resources on the learning material facilitated positive learning experiences in an online co-curricular program, and some theoretical implications as well as real-life applications are discussed in this article.
Don’t Judge by Looks: Search User Interface to Make Searchers Reflect on Their Relevance Criteria and Promote Content-Quality-Oriented Web Searches

Searchers often judge the webpage quality by the visual appearance, even for tasks where the content quality is more important such as health information. We propose a search user interface (UI) that makes searchers reflect on their webpage selection criteria and promote content-quality-oriented web searches regardless of visual appearances. The proposed UI visualizes how often searchers have browsed high-quality webpages during a search session. It does not explicitly tell the searcher that a high-quality webpage means high content quality; instead, the searcher is expected to reflect on their relevance criteria and improve their search behavior towards careful information seeking by considering the relationship between their page selection and the visualized score on the UI. We conducted an online user study to verify the effectiveness of the proposed UI, and the results indicated that participants without a university education tended to focus on the content quality of webpages and browsed higher-quality pages with the proposed UI than with a conventional UI.
The Relation Between General Intellectual Ability and Study-Related Media Use Among University Students in Economics

As the student landscape changes in an era in which it is crucial to evaluate information and facts critically, it becomes important to understand students´ media consumption and its influences in the context of their study programs.Followig the g-factor theory when explaining cognitive skills such as critically dealing with information, this study looks at the relationship between general intellectual ability assessed by an intellengence test (BEFKI 11+) and the study-related media consumption of 709 first-year economics students from 25 universities in Germany.Results from regression analyses indicate that frequency of use and trust in certain online and print media correlate with general intellectual ability, even after controlling for several sociodemographic variables including sex, age and migration background.
ASAP

Embedded devices are increasingly ubiquitous and their importance is hard to overestimate. While they often support safety-critical functions (e.g., in medical devices and sensor-alarm combinations), they are usually implemented under strict cost/energy budgets, using low-end microcontroller units (MCUs) that lack sophisticated security mechanisms. Motivated by this issue, recent work developed architectures capable of generating Proofs of Execution (PoX) for the correct/expected software in potentially compromised low-end MCUs. In practice, this capability can be leveraged to provide "integrity from birth" to sensor data, by binding the sensed results/outputs to an unforgeable cryptographic proof of execution of the expected sensing process. Despite this significant progress, current PoX schemes for low-end MCUs ignore the real-time needs of many applications. In particular, security of current PoX schemes precludes any interrupts during the execution being proved. We argue that lack of asynchronous capabilities (i.e., interrupts within PoX) can obscure PoX usefulness, as several applications require processing real-time and asynchronous events. To bridge this gap, we propose, implement, and evaluate an Architecture for Secure Asynchronous Processing in PoX (ASAP). ASAP is secure under full software compromise, enables asynchronous PoX, and incurs less hardware overhead than prior work.
The influence of discourse continuity on referential form choice.

Much research in the functional linguistics literature suggests that the use of zero pronouns is driven by the degree of interclausal connection. Kim (1990, 1992) claims that in clause chain languages such as Korean and Japanese, zero pronouns are primarily used following an interclausal connective with a tight interclausal connection that maintains subject continuity (whether the subject referent is maintained or changed in the following clause) and action continuity (temporal sequence or action sequence is continued or interrupted in the following clause). Consistent with the claim, the results of Experiments 1 and 2 demonstrated that Korean speakers used more zero pronouns following a connective with higher subject continuity (Experiment 1) and action continuity (Experiments 1 and 2). The effect of connectives was observed when grammatical role, referential predictability, and coherence relation were controlled for (Experiment 2). These results are best explained by assuming the role of discourse continuity on referential form choice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Comparison of Machine Learning and Rule-based Approaches for an Optical Fall Detection System

A major challenge caused by the aging of the modern society is the home care of elderly people. Many elders prefer staying in their own home over moving into a nursing home for as long as possible. For this reason, the project DeinHaus 4.0 Lower Bavaria researches possible technical solutions which can help the elderly to live in their own homes for a longer period of time.The topic of falls is one main part of the research because the number of falls and thereby caused injuries increase with rising age. In this context, this work presents a prototype for an optical fall detection system based on pose estimation, running in real time on an embedded device connected to a USB-camera. In the process of development, different approaches including machine and deep learning, as well as a rule-based algorithm are applied to solve this task. All methods reach an accuracy higher than 94 % when used on publicly available data sets, but machine learning seems to have drawbacks when applied in the real-time application due to limitations in the available training data. The rule-based approach, on the other hand, seems to be more capable of generalizing different types of fall events.
Analysis of a Cascade Scaling Algorithm using Data Mining Methods

As a result of the huge amounts of data available in any subject of research, data mining technologies confront considerable obstacles. Due to the large volume of data, most existing data mining methods are inapplicable to many real-world problems. Data mining methods become ineffective when the problem size becomes too large. Scalability is an issue that must be addressed in data mining algorithms in order to construct high-performance, efficient, and scalable data mining algorithms. A new scalability technique is proposed and applied to several data mining problems in this research. The proposed scaling methodology is developed using a cascade approach. The approach begins with the collection of a large data set from several sources, which is then preprocessed. Once the dataset has been preprocessed, decompose it into smaller data sets of equal size subsets. Then apply a data mining approach to each subset, with the identical data mining method stated for each subset. The findings of the data mining approach on all subsets are pooled and aggregated for the final output. The performance of the suggested algorithm is assessed using a number of criteria, including accuracy, precision, recall, F-score, and execution time. Social advertising and bank marketing are the two datasets on which the proposed method is tested. The suggested approach's performance is compared with non-scale data mining methods, and it is found that the scaling method outperformed non-scale data mining methods on all measures.
Transfer Learning-based Facial Expression Recognition with modified ResNet50

Facial Expression Recognition System (FER) is a crucial task for applications like computer vision. It is very significant for the machine to observe and recognize human emotional changes. Recognizing facial expressions is a very challenging domain of research due to the complexity in extracting facial features from an image. In this paper, we developed a Transfer Learning model in which three additional convolution layers are combined after the freezed pre-trained ResNet50 convolution blocks. The convolutional layers added are of different sizes so that low and high-level features can be extracted. The features extracted from the convolution layers are flattened and multi-class classification is done by fully connected layers in the network. The FER2013 dataset is used for training and testing the proposed model. We obtained an accuracy of 85% using the model and the results are compared with existing face expression models.
Object Detection using Image Dehazing: A Journey Of Visual Improvement

Object Detection in hazy conditions is very challenging as haze significantly degrades the visibility of images limits visibility especially in outdoor settings. Here we introduce an interesting method to deal with haze that is present in images. Before applying any object detection method on the hazy input image, it is needed to be dehaze first and recognised later.For dehazing we have used the an Image Dehazing network known as All-in-One Dehazing Network (AOD-net) which is based on reformulation of atmospheric model and generates clean and clear image through a light-weight CNN and for recognition we have used the third version of famous YOLO i.e. YOLOv3. We test our method on various real time hazy images and compare the object similarity results on hazy image as well as on dehaze image. Along with this we have compared the number of object which are recognised in hazy image and in output clear image.
Denoising of Images using Fractional Fourier Transform

In this paper, we are using fractional Fourier transform (FrFT) for the application of image processing. Our objective is to use the FrFT in such a way that it can provide the solution to image denoising issues on a larger scale. The assertion behind using FrFT is that it has been broadly used and validated in the context of signal processing as a time-frequency analytical tool. Many used cases are available in the context of signal processing, such as detection, filtering, and feature extraction of the signal. Considering these studies, we use the FrFT to enhance and denoise the images using a parallel set of filters. Through simulation, we have observed that our method has outstanding and comparable outcomes in terms of accuracy concerning the existing denoising algorithms.
Real Time Identity Identification using Deep Learning

When it comes to recognizing someone, the most significant feature is their face. Face recognition aids in verifying any person's identification by using his particular traits because it acts as an individual identity for everyone. The whole technique for authenticating any face data is separated into two stages. Face Recognition system is used in the first step. is done rapidly unless in circumstances when the item is put relatively far away, and then the second phase begins in which the face is identified as a person. The entire process is then repeated, assisting in the development of a face recognition model, which is regarded to be one of the most meticulously planned biometric technologies. The photographs of people's faces are collected by people, and also the images are processed immediately by the identification equipment. As a result, the paper offers pertinent facial recognition research from a variety of perspectives. The study outlines the developmental stages and the technology associated with facial recognition. We offer face detection and recognition analysis research for real-world settings, as well as universal Face recognition databases and assessment criteria We take a look at face recognition in advance. Face recognition has emerged as a viable future growth path with a variety of applications.
A Deep Learning Approach for Gloss Sign Language Translation using Transformer

One of the most recent applications of machine learning is to translate sign language into natural language. Many studies have attempted to classify sign language based on whether it is gesture or facial expression. These efforts, however, ignore genuine sentences' linguistic structure and context. The quality of traditional translation methods is poor, and their underlying models are not salable. They also take a long time to complete. The contribution of this paper is that it suggests utilizing a transformer to perform bidirectional translation using a deep learning approach. The proposed models experiment on the ASLG-PC12 corpus. The experimental results reveal that the proposed models outperform other approaches to the same corpus in both directions of translation, with ROUGE and BLEU scores of 98.78% and 96.89%, respectively, when translating from text to gloss. Additionally, the results indicate that the model with two layers achieves the best result with ROUGE and BLEU scores of 96.90% and 84.82% when translating from gloss to text.
Abstractive Text Summarization for Covid-19 News with Data Augmentation

Since the spread of Corona Virus disease or Covid-19 at the end of 2019, there has been an extensive amount of news about Covid-19 and it takes a long time for humans to read the news, process it and retrieve important information from it. Therefore, automatic text summarization is necessary in this matter as it can help us process information faster and use it to make better decisions. Currently, there are two main approaches to automatic text summarization: extractive and abstractive. Extractive text summarization is conducted by identifying important parts of the text and extract a subset of sentences from the original text. Abstractive text summarization is closer to human&#x2019;s method as it is the reproduction or rephrasing based on interpretation and understanding of the text using natural language processing techniques. In this paper, we present text summarization of Covid-19 news using abstractive method to be close to human&#x2019;s method of summary. We also apply data augmentation in the pre-processing part to be an example case of working with data that are not perfect or diverse enough.
Investigating the Charity Funding System using Blockchain Technology

Corruption is one of the biggest evils in society. Most of the black money of the world is transacted through Nongovernment organisations and charities. Though, the intention of a few genuine people who want to help the society could be misused by others. In this context, this paper proposes a solution for the online charity system using blockchain technology. The donations made by any donor can be traced and maintained on the blockchain system. The system takes the real money and converts it to virtual tokens and all the transactions done on the systems are done in form of tokens. These tokens are donated by donors and when these tokens are distributed to receivers, there are various algorithms implemented to distribute the tokens. The comparison of proposed algorithms is evaluated and analyzed based on execution time and execution cost.
Dynamics of two-photon Tavis-Cummings model with Kerr media

The entanglement between two qubits not-resonantly interacting with a single-mode field of an ideal resonator with Kerr media via degenerate two-photon transitions was studied. The exact solution for time-dependent density matrix and calculated on its basis the qubit-qubit entanglement parameter - negativity was derived. The results showed that Kerr media and detuning dramatically affected the entanglement behavior. More interestingly, that for initial entangled qubits states Kerr media and detuning avoids the entanglement sudden death effect.
Robustness in Multiple-Hypothesis Tracking

Though significant progress has been achieved in the mathematical theory of multi-target tracking and its application in numerous surveillance domains, robust solutions are not always achieved in practice. This paper offers design suggestions for improved performance with a primary focus on multiple-hypothesis tracking based methods.
Feasibility model for solar-powered cryptocurrency mining setups

This paper presents a model for assessing the financial viability of cryptocurrency mining setups powered by off-grid solar photovoltaic (PV) systems.The model considers the features of mining hardware, the network attributes, the price of virtual currency and the solar potential of the installation site, to predict the payback period of the investment in months.As a case study, the feasibility of mining using various state-of-the-art Application-Specific Integrated Circuits (ASICs) and Graphics Processing Units (GPUs), powered by PV installed in New Zealand has been investigated.The results show that for ASICs setups, the initial cost is very high compared to GPU setups.However, considering the best-performing cryptocurrencies, the payback period for ASICs is much shorter than for GPU setups.This work will help to improve the sustainability of cryptocurrency mining businesses by reducing their dependence on exhaustible energy resources and their impact on the environment.
A Novel Data Recovery Algorithm for FAT32 File System

The data recovery technology of the FAT32 file system is widely used in computer forensics. Based on the traditional scanning algorithm, this paper draws on and improves the Sunday algorithm and the AC automata algorithm, combined with the characteristics of data decoding by the hexadecimal dump, and proposes a specific scanning strategy to classify and scan purposefully and recover and obtain evidence. Documents and information are displayed to realize a stable and fast scanning traversal program. For incomplete Office files, it can also analyze a series of streams of its compound documents, and finally obtain valuable relevant information to obtain evidence.
An Adaptive Truth Discovery Model Consisting of Multiple Truth Part and Single Truth Part

In this era of big data, conflicts are often encountered when information is provided by multiple sources. Truth discovery task is a good solution. However, traditional truth discovery methods focus on providing a point estimator for each object&#x0027;s truth, or pay much attention on multiple truth. We want to break the limitations of traditional models and find a more universal model to conquer variability of the real world. In this study, in order to cope with the changing environment, we propose an adaptive approach based on Bootstrapping and Kernel Density Estimation from Multiple Source. We compare it with other baseline on synthetic datasets, and the experimental results prove that BKDEM not only has efforts in performance, but also is robust.
SOCIAL SCIENCES IN THE SOCIO-TECHNICAL SYSTEM : THE SCIENCE OF DECISION-MAKING AND SOCIAL CONFLICTS MANAGEMENT IN THE DIGITAL ENVIRONMENT. (ANALYTICAL REVIEW)

The review provides a characteristic of modern society as a global socio-technical system that represents a global environment for digital communication. It is stated that in the digital social environment there is a forced structural change in the social sciences associated with the actualization of their new objectivity. It is shown that in the global socio-technical system social dynamics as such, decision-making practice, and management of social conflicts are becoming topical research subjects.
Large-capacity information hiding scheme based on minimum pixel modification

Information hiding is an important technique for security-related applications, and it can be used wildly in many scenarios. Secret data embedding methods are proposed in spatial, frequency, and other domains by researchers in recent years. After investigating the existing information hiding schemes, we find that there is still room to improve the data embedding performance. Inspired by some works of literature, we propose a new scheme based on the modulus function with Minimum Pixel Modification (MPM22) for hiding secret data into carriers. The main idea of the scheme is to embed confidential bits into cover-image pixels while minimizing modification to original pixels. The correctness and high performance of the scheme are proved in mathematics and comprehensive experiments. Experimental result shows MPM22 has the largest embedding capacity (3 < bpp (bit per pixel) less than 7) compared with other schemes while maintaining the best image quality (peak signal-to-noise ratio (PSNR) >36.1 [email protected]k=3, PSNR>30.5 dB @k=4)。 The experimental result also confirms that our proposed scheme has higher performance than other schemes.
Content and requirements for the development of environmental competence in future teachers

Personal development in the education system is ensured by the formation of universal educational actions as the basis of the educational and upbringing process. The concept of universal learning activities takes into account the experience of the competence-based approach, i.e. using the acquired knowledge and skills in practice.