Reviewerly/exHarmony huggingface dataset:
Mitigating Adversarial Attacks Based on Denoising &amp; Reconstruction with Finance Authentication System Case Study

Deep learning techniques were widely adopted in various scenarios as a service. However, they are found naturally exposed to adversarial attacks. Such imperceptible-perturbation-based attacks can cause severe damage in nowaday authentication systems that adopt DNNs as the core, such as fingerprint liveness detection systems, face recognition systems, etc. This paper avoids improving the model&#x0027;s robustness and realizes the defense against adversarial attacks based on denoising and reconstruction. Our proposed method can be viewed as a two-step defense framework. The first step denoises the input adversarial example, then reconstructing the sample to close to the original clean image and help the target model output the original label. The proposed method is evaluated using six kinds of state-of-art adversarial attacks, including the adaptive attacks, which are known as the strongest attacks.We also specifically focus on demonstrating the effectiveness of our proposed work in Finance Authentication systems as a real-life case study. Experimental results reveal that our method is more robust than the previous super-resolution-only defense in respect of attaining a higher averaging accuracy over clean and distorted samples. To the best of our knowledge, it&#x0027;s the first work that reveals a comprehensive defense framework against adversarial attacks over Finance Authentication systems.
Implementation of Cyberbullying Detection using Machine Learning Techniques

Cyberbullying could be an upsetting on-line wrongdoing with disturbing consequences. It seems in several forms, and in most of the social networks, it's in text format. Automatic detection of such incidents needs intelligent systems. Most of the prevailing studies have approached this drawback with typical machine learning models and therefore the majority of the developed models in these studies are applicable to one social network at a time. In recent studies, deep learning primarily based models have found their means within the detection of cyberbullying incidents, claiming that they will overcome the restrictions of the standard models, and improve the detection performance. Cyberbullying is that the use of technology as a medium to bully somebody. Though it's been a difficulty for several years, the popularity of its impact on teenagers has recently inflated. Social networking sites offer a fertile medium for bullies, and youths and young adults UN agency use these sites are susceptible to attacks. Through machine learning, we are going to realize language patterns utilised by bullies and their victims, and develop rules to automatically realize cyberbullying content. We find that our approach is with success able to determine vital variations between cyberbullying and regular media sessions, and supply a performance increase in cyberbullying detection. This paves the means for a lot of nuanced work on the utilization of temporal modelling to find and mitigate the incidence of cyberbullying.
Teaching Strategies for Improving English Effectiveness in Higher Vocational Colleges Based on The Present Situation of English Teaching

As the development trend of the English era gradually became clear, more and more young people began to pay attention to the special importance of student English teaching in the English learning of Chinese college students. At the same time, in higher vocational colleges, the student English teaching system needs to make certain system adjustments and continuous improvements, not only because the current situation of the development of student English teaching in higher vocational colleges is not optimistic , but also because some students in colleges and universities are less enthusiastic about the work of college English education and learning, which leads to the low quality of college English teaching. As a key stage of talent training and corporate human resource savings in colleges and universities, we still need to continuously strengthen the learning of weak points of English. In other words, in vocational English colleges, our English teaching level needs to be improved to a certain extent. There are still many teaching methods in higher vocational English teaching that need to be gradually improved. This inevitably requires language teachers and English schools to give full play to their roles at the same time. Teachers must fully stimulate students’ interest in learning in the classroom, schools must organize various communicative English regularly. Teaching activities create a good communicative English learning atmosphere for the majority of students themselves, so that they can truly effectively improve their communicative English expression ability.
Two-scale Algorithm to Plan Coverage Paths for Multi-UAVs

The purpose of the coverage mission is to search this space by visiting specific points (Points of Interest (POI)) in order to scan as much area as possible. This type of mission appears for a diversity of applications, we quote, for example, aerial surveillance, radioactive waste, and pollution sources localization, data collection, etc. This paper addresses the problem of aerial coverage by deploying a fleet of Unmanned Aerial Vehicles (UAVs), Indeed, the planning algorithm proposed solves how to ensure a global sampling of an Area of Interest (AOI). First, Voronoi diagram is adapted to install a grid of measurement points (POIs), while ensuring a homogeneous sample of this area. Then, optimized paths are planned for the coverage mission passing through all the POIs. Considering the flight capabilities of UAVs, the coverage mission is modeled as a Vehicle Rooting Problem (VRP) and then resolved by taking advantage of the Simulated Annealing (SA) method. A generic solution, optimization, and reduced computation time are the key advantages resulting from simulation tests.
Real-time hand area segmentation for hand gesture recognition in desktop environment

the process of separating the hand area from a complex background, knownas hand segmentation, is a prerequisite for any vision-based hand gesture recognitionsystem. In some applications, only a rough estimate of the hand area is needed, but in otherapplications an exact segmentation of the hand, if possible, is needed. In this paper, threemethods for extracting the hand from the background in real-time were tested. Thesemethods use off-line learning of skin color; these methods are Histogram Intersection,Color Histogram, and Skin Color Modeling and Adaptation. The three methods weretested using a video sequence of 100 frames using four different lighting conditions, with400 frames in total. The different lighting conditions are fluorescent light only, a mixture offluorescent and daylight, daylight without the sun light, and daylight with the sun light.These video streams were taken from the same person under the above different lightingconditions. A comparative study between the three methods was performed. The resultsshowed that hand segmentation using Skin Color Modeling and Adaptation withFluorescent light produced the best results among the three methods. The objective of ourresearch is to accurately recognize the hand gestures, and then use it in many differentapplications. The work in this paper is the first stage in our research. The contribution inthis paper is the comparative study between three different techniques that use skin-colormodeling under different lighting conditions.
Low-light Video Enhancement with SVD-DWT Algorithm for Multimedia Surveillance Network

After the outbreak of the global pandemic COVID-19, video surveillance has become an important aspect of multimedia communication in monitoring-based systems, security systems, object tracing etc. However, due to drawbacks like video frame distortion, the quality of the video in surveillance may be affected in some cases. In this work, a video enhancement-based multimedia surveillance network has been developed by using a Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The proposal, in particular, uses SVD-DWT along with median filtering and contrast limited adaptive histogram equalization to enhance low illuminated distorted video frames by using video sensor nodes. The performance of the proposed scheme is evaluated by calculating parameters like mean square error, peak signal to noise ratio, normalized cross-correlation etc. of the video frames in different types of environments. The results delineate that the proposed scheme is better than other available methods in diverse types of environments.
University professors and blended learning from the perspective of the students

This article is developed with the purpose of making possible the strengthening of the diversification of educational modalities and institutional decision-making in the Autonomous University of Baja California. The results of a descriptive exploratory study are shared, which contributes in a relevant way to the construction of a diagnosis of current needs for specific teacher training for the operation and assurance of educational quality in blended learning. It reflects on the origin of the blended modality, its current configuration and characterization in the institution, as well as an initial quantitative exploration of the training needs of teachers who operate in this modality, from the perspective of the students. It is reiterated that teachers from all over the world, particularly those of higher education, must be prepared to guide future generations for the appropriation of their learning processes, with a positive attitude and developing technological skills.
Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE) is presented in this paper. Tl-MVAE estimates latent features calculated from tweet, video, and audio features extracted from tweets and videos. Then, important scenes are detected by estimating the probability of the scene being important from estimated latent features. It should be noted that there exist time-lags between tweets posted by users and videos. To consider the time-lags between tweet features and other features calculated from corresponding multiple previous events, the feature transformation based on feature correlation considering such time-lags is newly introduced to the encoder in MVAE in the proposed method. This is the biggest contribution of the Tl-MVAE. Experimental results obtained from actual baseball videos and their corresponding tweets show the effectiveness of the proposed method.
PROFIL TINGKAT KECANDUAN GAME ONLINE PESERTA DIDIKKELAS XMA PLUS AL MUJAMMIL GARUT

The purpose of this study was to determine the level of addiction to online games of class X MA Plus Al Mujammil Cisurupan Garut. The method used is descriptive quantitative research with data collection tools on the scale of online game addiction and student observation sheets in the classroom. The results showed that the level of students' online game addiction was in the non-addictive category 38.5%, mild addiction 15.3% and severe addiction 46.2%. The results of the study on the level of student addiction if addiction is not handled properly it can lead to uncontrolled students because they are not able to reduce playing time so that social relations and tasks are hampered. Keywords: : Online Game Addiction, Students
AUV navigation: challenges and solutions

The term navigation is commonly used to describe several different but related concepts. Some use the term for the process of determining the position (in some reference frame) and the orientation of the vehicle only, whereas others also include the problem of how to get from a point A to a point B in the navigation concept. In this chapter, we focus solely on the former meaning of the term. In other words, we define navigation to mean estimation of the six degrees of freedom of a rigid body (any vehicle or device), i.e., the position and orientation of the body. The position can be expressed in various reference frames (local or global), and the orientation is defined as the spatial orientation of the body frame relative to a local reference tangential to the Earth ellipsoid. This is more precisely defined in Section 5.2. It is often customary to include the estimation of linear and angular velocities in the navigation problem. The uncertainties of the estimates are often also part of the navigation output, and the navigation can be performed in real time, or in post -processing.
Geochemical Characterization and Economic Potential of Emure and Ijero Ekiti Pegmatites in Southwest Nigeria

Global demand increase for rare metals deposits has re-enacted the search for economically viable deposits. Precambrian pegmatites occurring as near dykes and massive types were studied for Emure-Ekiti and Ijero-Ekiti respectively, with the aim of characterizing them geochemically and determining their possible economics values. A total of twelve samples comprising seven from Emure-Ekiti, three whole rock muscovite extract for Ijero-Ekiti were analysed for major and trace elements using X-ray fluorescence method. Thin sections prepared for the Emure-Ekiti pegmatites show that quartz, microcline are major mineral constituents. The geochemical results reveals that the Emure-Ekiti and the whole rock samples of Ijero-Ekiti are considerably siliceous, with an average value of 68.72% and 68.6% respectively, while the muscovites extracts average 48.86%. Mean values of major oxides for the Emure, Ijero[whole rock, muscovite] are as follows; Al2O3 - 20.42%, [13.74%, 35.15%]; Fe2O3 - 1.83%, [1.24%, 2.19%]; MgO - 0.013%, [0.029%, 1.04%]; CaO - 0.02%, [0.34%, 2.4%]; Na2O – 1.01%, [4.40%, 1.045%]; K2O – 3.17%, [2.77%, 11.07%]. Trace and rare-earth elements result shows that the Emure-Ekiti pegmatites is “barren” – depleted in rare metals Ta, Nb, Cs, Sn, Rb, etc, and compare favourably with Apomu, Ago-Iwoye and Ijebu-Ife barren pegmatites. On the other hand, two of the whole rock samples are mineralized – fairly enriched in rare metals Ta, Nb, Rb while the remaining sample and the muscovite extract are barren; as depicted by the Rb vs Rb plot and when compared to the other rare metal pegmatites across the world.
Penggunaan Media Virtual Reality Sebagai Pengganti Karya Wisata Untuk Siswa Kelas Rendah Di Masa Belajar Dari Rumah

The Covid-19 pandemic gave recommendations to stay at home and establish policies for learning from home, this resulted in students being unable to go to school to carry out learning activities. Likewise field trips which are part of learning activities are usually carried out by students to go directly into the environment and find their own learning information and require students to leave the house By utilizing technology, we are familiar with virtual reality learning media, based on several virtual reality research that has the potential as an innovative learning medium. for low grade students and can improve student learning outcomes. But can virtual reality replace field trip learning? for low grade students in the home study period. This research was conducted with the hope that it can be useful and provide new insights and knowledge related to the use of virtual reality media as a field trip for low-grade students during their learning from home. With virtual reality, students can still do field trips while still paying attention to the steps in the field trip, namely: the preparation period, the implementation period and the return period. Virtual reality that prevents being effective in increasing student interest and learning outcomes can also replace field trips during the Covid-19 pandemic. So that students do not need to leave the house to do field trips.
THE EFFECT OF COMIC STRIPS AS AN ENGLISH TEACHING MEDIA TOWARD STUDENTS’ READING ABILITY IN DESCRIPTIVE TEXT

This research was done due to the lack of students' reading ability in descriptive text in MTsS Yati Kamang. This research was classified as a quasi-experimental research. To experiment class the researcher choose VII.II class and VII.I is experiment class. The researcher uses test to measure students’ reading ability. Afterwards, the researcher proved that the Comic strip as a media was able to increase students' reading ability in descriptive text. Experiment class and control class there are 38 students. There were 19 students as the respondents in experimental group and 19 students as the respondents in control group. The experimental class was taught by using Comic strips as media in teaching and the control group was taught only by using Picture. The result of paired sample t-test showed that the tobtained (6,591) was higher than ttable(2,326) with the level significant 0,01. The null hypothesis was rejected and the alternative hypothesis was accepted. It means there is a significant effect of using Comic strips toward students’ reading ability in descriptive text at the seventh grade of MTsS Yati Kamang.
THE EMERGENCY TRANSITION OF HIGHER EDUCATION INSTITUTIONS TO NEW EDUCATIONAL TECHNOLOGIES IN THE CONTEXT OF THE COVID-19 PANDEMIC: A CASE FROM RUSSIA

THE EMERGENCY TRANSITION OF HIGHER EDUCATION INSTITUTIONS TO NEW EDUCATIONAL TECHNOLOGIES IN THE CONTEXT OF THE COVID-19 PANDEMIC: A CASE FROM RUSSIA
THE EFFECTIVENESS OF AUTHENTIC MATERIALS FOR READING COMPREHENSION ON STUDENTS’ MOTIVATION

The research is about to find out the students’ motivation through the authentic materials in experimental class and using textbook in control class, and to find out the significant result of students’ motivation between control and experimental class at the tenth grade of SMA Negeri 1 Sumber. The research method is quantitatif research with quasi experimental research design. The population of the research is all of MIPA students at the tenth grade and the sample are students of X MIPA 7 and X MIPA 8 that consist of 72 students. The techniques of collecting data are questionnaire and observation. Therefore, the research findings showed that students’ response in experimental class has a very good result after the treatment with the mean 86.42 from 69.18 and the Paired Sample T-test score is 0.00 means that there is a significant result after the treatment. However, students’ response in control class is not really showed the good result because it decreased from 69.76 to 68.87 with the Paired Sample T-test 0.429 means that there is no significant result after the treatment. In addition, the hypothesis test using Analysis of Covariances (ANCOVA) was pointed out that the significant was 0.00 means lower than 0.05. It is indicated that Ha is accepted, namely there is a significant result of students’ motivation from the use of authentic materials for reading comprehension.
Mean-Variance and Mean-Variance-Correlation Neural Network Regression Models

We introduce two neural network models designed for application in statistical learning. The mean-variance neural network regression model allows us to simultaneously model the mean and the variance of a response variable. In case of a two-dimensional response vector, the mean-variance-correlation neural network regression model enables us to jointly model the means, the variances and the correlation.
Hybrid Machine Intelligence for Imbalanced Data

Data Imbalance is the major problem faced while implementing machine learning concepts to real life problems. Imbalanced dataset poses a severe issue for a huge many supervised learning problems which leads to increasing interest from both industrial and academic sectors. Since the class having less instances of data points is outnumbered by the class having maximum instances of data points. The training dataset includes all data points to fit into a classical classifier. The classifier classifies the whole set of data to majority class by and ignores the minority class by considering it as noise. Therefore it is significant to choose a training dataset which perfectly fits in the processing phase of the order of an imbalanced dataset.Imbalance data is present everywhere in the real world. Existing studies on imbalanced data inquiries them for binary classification. It is presumed that conventional ML techniques try to reduce experimental risk factors. Therefore, the arrangement precision of the minority class is regularly abandoned. Different data level methods like undersampling, oversampling and algorithm level methods like one class learning and ensemble cost sensitive have been presented for better performance of classifiers while handling imbalanced datasets and distributions. Grounded on these factors, this study aimed a hybrid viewpoint to handle the problems caused by imbalance data in a better manner.
Social trust in business and its socioeconomic effects in Korea

Chapter 8 examines social trust in business as well as its determinants, effects, and enhancement in Korea. Trust in business, which affects the economy and society through various channels, is seriously low in Korea. The Korean business sector has developed two distinctive groups of enterprises: a large number of SMEs and a limited number of conglomerates (chaebol). SMEs have stagnated over time, while chaebols have expanded and diversified through the financial incentives and institutionalised privileges provided by the government as part of its industrial policy. Chaebols have become the dominant economic force and were a critical culprit of the1997 crisis, after which they were forced to undertake reforms. The main causes of low trust in business include: economic power concentration in chaebols, poor corporate governance, unfair chaebol-SMEs relationships, and business corruption. To prevent business corruption, illicit intra-group transactions of chaebols should be prohibited. The most critical elements for raising trust in business are a strong political will and public support.
Can Educational Interventions Reduce Susceptibility to Financial Fraud?

Financial fraud is pervasive and can be devastating for its victims. While numerous campaigns designed to warn and educate consumers about financial fraud exist, there is very little evidence on whether these initiatives are effective at reducing susceptibility to scams. We conduct a randomized experiment among a representative sample of U.S. adults and find that short, online educational interventions can meaningfully reduce fraud susceptibility, and that effects persist for at least three months following a reminder. Investigating mechanisms, we find no evidence that the educational intervention reduced willingness to invest generally, but rather increased knowledge which participants were able to selectively apply. We find that beneficial effects are concentrated among individuals who are more likely to invest, particularly the financially sophisticated. Our results indicate that brief financial education interventions can meaningfully reduce susceptibility to financial fraud.
Nighttime image dehazing based on improved erosion dark channel and multi-scale clipping limit histogram equalization

In this paper, a new night dehazing strategy is proposed by modifying the transmission and atmospheric light conditions. To suppress the local light sources, a Gaussian based atmospheric light model is developed along with erosion filters to improve the traditional atmospheric light factors. A similar erosion operator is also utilized for transmission to refine, and the model is combined with a multi-scale clipping limit of ICLAHE to obtain an optimal nighttime dehazing effect. The experimental results confirm that the proposed strategy performs better in comparison to state-of-the-art techniques and is computationally less intensive.