Reviewerly/exHarmony huggingface dataset:
Cognitive Bias-Inspired Deep Robust Neural Networks Against Transfer-Based Attacks Considering Confidence Score

Transfer-based attacks, a type of adversarial attack, have become a growing threat in recent years with the proliferation of cloud services. Deep neural networks that exploit human cognitive bias (Loosely Symmetric-Deep Neural Network, LS-DNN) are known as a defensive technique against transfer-based attacks. LS-DNN can prevent malfunctions caused by adversarial examples with a high probability by incorporating human learning characteristics into the neural network’s nodes. However, maintaining accuracy against normal data and reducing Training Time is challenging. This paper proposes a new model called “LS[Formula: see text]-DNN” inspired by the Dropout method to solve this problem. Evaluation experiments on two datasets show that the proposed model can achieve both in high dimensions. In addition, we analyze the proposed model focusing on the variance and confidence score of the training parameters of the proposed model. As a result, we point out that the confidence score is an important indicator of robust models against transfer-based attacks.
Uncertainty as a global phenomenon and its manifestations in education

The phenomenon of uncertainty is considered from a philosophical standpoint as a manifestation of existence, its attributive properties (determinism), as well as properties of knowledge (especially in education). The manifestation of the phenomenon of uncertainty is considered in relation to science education, where it manifests itself most vividly and explicitly. In this connection, the structure and evolution of the scientific picture of the world is discussed. Three main stages of its evolution are distinguished: classical, non-classical and modern. In the classical era, Laplacian determinism dominates, which loses its significance under the pressure of new facts and V. Heisenberg’s uncertainty principle, which has become the basis of quantum mechanics as a fundamental physical theory. At the third stage of evolution, the concept of determinism is further generalized and the phenomenon of uncertainty becomes a fully attributive characteristic of existence.&#x0D; The role of definition as a logical procedure that reduces possible negativism due to the uncertainty of knowledge is discussed. The relationship between the concepts of uncertainty and complexity is considered; it is argued that complexity in some situations can be the basis of uncertainty.&#x0D; The article presents specific examples of uncertainties in education (the situation of students’ acquaintance with new material; the selection of new literature in the library; residual factors of postmodernism, etc.). It is shown that uncertainties should not be considered as a reason for concern in education, that with minor refinement of successfully working educational models, these uncertainties can be overcome.
IMPACT OF SOCIAL MEDIA ON CRYPTOCURRENCY INVESTING DECISIONS – A BEHAVIORAL FINANCE PERSPECTIVE

In Vietnam, many people are investing in cryptocurrency due to the hype on social media and Bitcoin's rapid growth.However, new investors often lack the necessary financial knowledge and skills to make wise decisions during times of extreme market volatility.This is where Behavioral Finance comes in.This field of study examines how traders and investors expose themselves to cognitive biases, leading to inefficiencies and anomalies in financial markets.To better understand the behavior and decision-making processes of cryptocurrency investors on social media, this research paper will use data mining and text-based analysis along with behavioral finance theory.By analyzing tweets, on Twitter related to cryptocurrency using Orange 3, we hope to uncover how investors react to information they receive from their peers and the market in general.This will provide guidance and essential information to help investors make wiser decisions and avoid emotional investing behavior.The implications of our findings will be especially helpful for short-term investors who need to gain knowledge and skills to navigate the market.Our research will also show that the principles of cryptocurrency investing do not necessarily follow traditional finance principles.
Computer Vision-Based Hand Baggage Inspection System

Object detection is a crucial task in computer vision. It brings two essential tasks, classifying and locating the object in the image or video. This task is performed by specialized human operators, which requires the knowledge and experience for reliable detection. However, the high volume of work often drives the operators to exhaustion, causing a low prevalence of targets. This work aims to build a model using a Deep Neural Network (Darknet) and a single pass object detection method (YOLOv4) for the detection of handguns, shurikens, and blade shavings in hand luggage, using the data set GDXray. The YOLO algorithm model identified different items that could impact security, which could be a tool that makes passenger boarding faster and safer.
Management of Time Resources in Madrasahs to Increase Work Productivity and Effectiveness: Literature Review

This research is a literature review that aims to understand the practice of managing time resources in Madrasas and the factors that influence it. By collecting data from various sources, this research resulted in a better understanding of the practice of managing time resources in Madrasas, as well as the factors that influence these practices, such as competence, motivation, work environment, and organizational support. This research provides a better understanding for practitioners, especially teachers in Madrasahs, to understand the factors that influence their practice of managing their time resources and develop effective strategies to increase work productivity and effectiveness. Although this research does not produce novelty directly, it provides a strong basis for further research to develop models or strategies that are more effective in managing time resources in Madrasahs. This research is expected to make a positive contribution to the development of time resource management practices in Madrasas.
Klasifikasi Kanker Payudara Menggunakan Algoritma SVM dengan Kernel RBF, Linier, dan Sigmoid

Breast cancer ranks first in both the gender category and the death rate. Late treatment is often found in cases of breast cancer which causes an increase in the risk factors for this cancer. For this reason, early detection of breast cancer is needed, so that treatment can be done in a timely manner, so that the death rate due to breast cancer can be reduced. For this reason, this article offers early detection of breast cancer using classification. The dataset in this study used the Wisconsin breast cancer dataset taken from Kaggle. Initially the dataset has a missing value, besides that the categorical data is not yet in numerical form, so it is necessary to do preprocessing with the missing value imputing technique and encoding to convert categorical data into numeric data. The dataset is divided into two proportions, namely 80% as training data and 20% as testing data. In the classification process, datasets that have been preprocessed are classified using SVM with three different kernels, namely the linear kernel, the RBF kernel, and the Sigmoid kernel. Based on the research results that have been obtained, the linear kernel shows the best classification results when applied to the SVM classification, namely with an accuracy value of up to 99%, followed by RBF kernel performance with an accuracy rate of 92%, and finally the sigmoid kernel with an accuracy value of 41%
The Use of Infographics as a Systemic Functional Approach to Improve Students' Understanding of Shakespeare's Drama

Despite its importance, it should be noted that teaching literature, especially in an EFL setting, can be challenging. Numerous studies have been conducted to examine various literature on teaching methods for EFL students. The strategies used in the research, however, heavily rely on teachers' attempts to spoon-feed students' understanding. Thus, in this research, the writers aim to study students’ perceptions of using infographics in studying intrinsic elements in Shakespeare’s drama. This study adopts the mixed-method approach. The participants of this study are 71 students from the second semester consisting of 41 females, and 30 males majoring in English Language and Literature (BBI) at UBM University. The result of the questionnaire was analyzed by using SPSS. In addition to processing the questionnaire results, the writers also conduct a semi-structured interview to gather additional perspectives and in-depth insights about the usage of infographics to improve students' comprehension of Shakespeare's play. This study concludes that participants were eager to learn fundamental aspects of Shakespeare's drama through infographics. Students' short attention spans and Shakespeare's difficult language can both be overcome with the use of infographics.
User Authentication Using Typing Gesture with Smartwatch

A method for enhanced user authentication that relies on sensory data taken from a smartwatch while the user types the username and password has been explored.Eventually, these inherent gestures would work as an added layer of security to the current password-based authentication scheme in a hostile scenario assuming the username-password has been compromised.In our experiments, we recorded the 3D coordinate values given off by the accelerometer and gyroscope over a set of username-password typing combinations.For the sensor data collection, we developed an Android Wear OS smartwatch application, then proceeded to implement our method of sensor data processing and performed experiments to demonstrate the potential of this method.We experimented with 50 samples taken from five users, performed 1,800 genuine and impostor authentication attempts, and achieved an equal error rate (EER) as low as 0.07.With such low EER, the proposed method can be an effective solution to username-password breaches.
Effects of Instruction in Pre-Writing Strategy on Students’ Achievement in Idea Organization and Paragraphing

The rate at which students' composition writing is degenerating requires an urgent approach to improving students’ ability in idea generation and paragraphing at the secondary school level. It is against this backdrop that this paper attempt to determine the effects of instruction in pre-writing strategy on senior secondary students’ achievement in idea generation, and paragraphing. The study was a quasi-experimental one that utilizes a non-equivalent pre-test and post-test design. Students’ composition writing achievement test was used to collect data from 120 SS one students from four intact classes. The data collected were computed using the mean, and standard deviation to answer the research questions and student's t-test to test the hypotheses formulated. Findings from the analyses showed that exposure to the pre-writing strategy achieved higher coherent paragraphing in composition writing than their counterparts who were taught composition writing using the conventional method.
Improving Poetry Writing Skills with the Think Talk Write Model

This study aims to determine the improvement of poetry writing skills with the Think Talk Write model. The method used in this research is a class action research design (CAR). This research was conducted in two phases, which consisted of planning, implementing, observing, and reflecting. Data collection techniques used in this study were tests, observations, and interviews.The data analysis technique used in this study uses quantitative methods accompanied by descriptive analysis. The results showed that the poetry test results experienced an increase in pre-cycle learning, cycle I and cycle II. The average value of the poetry test class in the pre-cycle was 60.21, the first cycle averaged 66.86, and the second cycle averaged 76.04. Improving the learning process of students who take part in learning is even more optimal before and after activities compared to cycle I and cycle II activities.Keywords: Poetry writing skills, think talk write
Review of high school students emotional problem measurement scale utilizing Rasch model and validation according to difference in exercise time between groups

This study reviewed the high school students emotional problem measurement scale using Rasch model and validation according to difference in exercise time between groups The Korea children and youth panel survey 2018 data was used that 2265(boy=1217; girl=1048) high school students were measured. Winsteps 5.4.2 program and Facets 3.85.1 program were used and the significance level was set at 0.05. The results were as follows. First, the 4-point response category was appropriate for all 23 items of the three factors measuring emotional problems(8-item in physical symptom factor; 5-item in social withdrawal factor; 10-item in depression factor), and the unidimensionality was satisfied for each factor. Second, one item in the physical symptom factor was reviewed as an inappropriate item, and there was no inappropriate item in the social withdrawal factor and depression factor. Third, as for the differential item function according to gender, one item was extracted from the factor of social withdrawal and one item was extracted from the factor of depression. The physical symptom factor did not appear. Fourth, for all three factors reviewed, students who actively exercised for more than 2 hours a week had significantly lower the physical symptoms, social withdrawal, and depression than students who exercised less than 2 hours. The known group difference validity was verified according to the difference between the exercise time groups. Therefore, it was finally composed of 3 factors, 20 items(7-item in physical symptom factor; 4-item in social withdrawal factor; 9-item in depression factor), and 4-point response categories.
New York State Social Studies Regents Exam Item Analysis and Implications - Multiple Choice Questions &amp; Constructed Response Questions -

The researcher selected the Regents History Test, a high school graduation test that measures achievement based on the New York State curriculum, as the study subject. Regent, New York State’s high school graduation test, has accumulated a long experience and adopts both a history selective test and a written answer test. Therefore, it is considered that it will provide useful implications for the relationship between the types of historical selective and self-answering questions, the production of rubrics, and the history evaluation goals and item development in Korea.BR The purpose of this article is to learn about the characteristics of the New York State graduation test, the Regents History/Social Sciences exam, to look at the framework for historical thinking or historical content that the evaluation questions are trying to reach, to systematically analyze the types of past questions, and to study Korean history The purpose of this study is to obtain implications that can be applied to the development and scoring of multiple-choice and open-answer questions. The research method was combined with literature research on the New York State Regents exam, as well as past questions and rubric analysis of the Regent “Global History and Geography” and “American History and Government” exams from 2018 to 2023.
Edukasi Penggunaan Media Baru dalam Penanggunalan Bencana Erupsi Gunung Merapi

This article discusses educational activities on the use of new media as an effort to deal with disaster events in Demen, Pakembinangun, Pakem, Sleman. This community service activity is carried out by providing training on the use of new media, such as: YouTube, WhatsApp, Twitter, and Google Maps. The methods used are reflections, lectures, simulations, discussions, and evaluations regarding digital media. The result is that the Demen people know that most of the people quite understand the types of communication media, both conventional media and new media that present disaster information. Only about 18 out of 20 participants thought that television and Facebook did not have a positive effect on disaster management. Meanwhile WhatsApp, Twitter and YouTube actually provide a lot of information that has a positive impact on them, mainly used to find the latest information updates, coordination and cooperation. This service activity also invites the public to use Google Map as a new form of collaboration in mapping evacuation routes and 3 gathering points when natural disasters such as the eruption of Mount Merapi occur. This effort was made to reduce the risk of social panic and reduce the possibility of traffic accidents when evacuating.
Analisis Faktor-Faktor yang Mempengaruhi Kinerja Sistem Informasi Akuntansi pada LPD di Kecamatan Petang

Since the evening LPD plays a role for the community, the LPD should continue to improve its performance every day in order to improve the well-being of the community. In order for LPD to best serve its customers, LPD managers must make wise use of developments in information technology. The population of this survey is all his LPD staff throughout the Petang District and units related to Accounting Information Systems. The sample for this survey was determined by a targeted sampling method with a total of 66 respondents, and the data for the survey were obtained from the questionnaire distribution results and analyzed using multiple linear regression methods. As a result, the top management's support did not affect the performance of LPD's accounting information system in Petang Province, and the formalization of system development did not affect the performance of LPD's accounting information system in Petang Province. The quality of the information system had a positive impact on the performance of his LPD's accounting information system in Petang district, and the size of the organization had a positive effect on the performance of his LPD's accounting information system in Petang district. The existence of the Steering Committee does not affect the performance of the accounting information system of the Pétain District LPD.
Selection of FFPE blocks with confocal microscope to reduce routine workload in pathology department

Pathology departments face high costs of slide cutting, staining and coverslipping. We employed a confocal microscope (Histolog® Scanner) allowing to scan FFPE blocks to investigate whether this could reduce the number of slides prepared.
Towards On-Demand Deployment of Multiple Clients and Heterogeneous Models in Federated Learning

In this paper, we increase the availability and integration of devices and models together in the learning process to enhance the convergence of federated learning (FL) models. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be while serving multiple FL models. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process while supporting multiple models. We make use of the containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. The performed experiments using the Mobile Data Challenge (MDC), MNIST, KDD datasets, and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients with less discarded rounds and more available data of each running FL application.
Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated Learning

Autonomous driving has been gaining a lot of attention in the field of transportation technology in recent years. The use of autonomous vehicles has the potential to reduce the number of road accidents caused by human error, improve traffic flow, increase fuel efficiency and save time for travelers. In federated learning systems, selecting trustworthy autonomous vehicles (AVs) to participate in training is critical for ensuring system performance and reliability. In this work, we propose a trust-aware approach to AV selection that incorporates the performance of each AV using the Local Interpretable Model-Agnostic Explanations (LIME) method and One-Shot Federated Learning. We modify the XAI LIME Deep Q-learning-based AV selection model to include the trust metric, resulting in the Trust-Aware XAI LIME Deep Q-learning-based AV selection model. Our experiments show that the trust-aware approach outperforms the standard approach in terms of both accuracy and reliability, demonstrating the effectiveness of incorporating trust metrics in AV selection.
DFFNet: Deep Federated Radio Fingerprinting Based on Fractional Wavelet Scattering Network

The rapid development of the Internet of Things (IoT) has highlighted the critical importance of security and privacy in cognitive cities. In this context, radio frequency fingerprinting (RFF) identification has emerged as an excellent authentication scheme that provides intelligent and efficient identification in IoT systems. By leveraging RFF, we can improve the security and privacy of cognitive cities while also enhancing their operational efficiency. The RF nonlinear features are unique and unchanging, operating at the hardware level. This attribute renders them amenable to sufficient learning through convolution neural networks (CNNs), which have demonstrated remarkable identification accuracy. Nonetheless, CNNs suffer from a lack of strong interpretability and necessitate vast quantities of training data. Additionally, the enormous amount of data required for training imposes greater demands on computing resources, which are often inadequate in IoT. Moreover, traditional training schemes employ centralized datasets, which cannot ensure corresponding privacy. More recently, federated learning and fractional wavelet scattering network have been proposed to solve the problems above. To address this issue, we in this paper proposed a deep federated radio fingerprinting based on fractional wavelet scattering network (DFFNet) which can acquire the subtle features from non-stationary signals. The advantage of DFFNet is that the federated learning is applied to achieve privacy preserving during the learning process. Meanwhile, fractional wavelet is suitable for non-stationary signal’s features extraction with high interpretability. The representative experiment results demonstrate that hybrid federated framework DFFNet achieve about 99.1% identification accuracy under practical application.
Metaverse: Telepresence in 3D Virtual Digital Worlds through the use of Avatars

This article addresses telepresence through avatars in virtual environments such as Metaverso. Important concepts such as Metaverse and 3D Virtual Digital Worlds, avatars, presence and telepresence were discussed. The applications of avatar telepresence in a variety of areas, including education, healthcare, entertainment, professional training, and customer service, as well as privacy and security concerns associated with this technology, were also explored.