Reviewerly/exHarmony huggingface dataset:
Robot Vision Recognition System Based on Improved YOLOv3 Algorithm

With the rapid development of the logistics business, a visual recognition system is being developed to speed up the transportation, packaging, and categorization of goods. This system can enhance identification speed and accuracy. First, the experimental platform was set up, and the experiment was performed on a six-axis robot with a camera lens, light source, and acquisition card. The receptive field module was then used to construct an updated YOLOv3 model based on the original YOLOv3 model to enhance the in-depth features and increase the feature representation ability. Experiments were conducted on VOC data sets. The mAP values of twelve different types of objects, the visual recognition effects of five different groups of pictures, and the visual recognition values of the original and revised models were compared. The results showed that the improved YOLOv3 model outperformed the original model in terms of mPA, visual recognition image, and recognition speed.
Research on the Evaluation Method of Chinese Character Writing Quality Based on Machine Vision

In recent years, with the increasing emphasis on traditional culture, writing robots have gradually become a hot spot in the field of robotics research. In this paper, a method for evaluating the aesthetics of written Chinese character was proposed. By comparing the writing results with the strokes of the standard regular script, calculating the two aesthetic indicators of balance and inclination, and finally the grade and relative comments were given to the writing results. The experimental results show that the influencing factor for the balance indicator is the type of stroke. While the inclination indicator is more dependent on the type of font structure. This method of evaluating the aesthetics of Chinese characters helps to further improve the level of written Chinese character and also provides a theoretical basis for the study of Chinese character strokes.
Target Tracking Method Based on Information Matrix for Multi-sensor Data Fusion

Smart driving technology has great potential to improve traffic safety, traffic efficiency, and driving experience. So, it is a hot topic for current and future research in the field of intelligent transportation. However, the future advanced driver assistance system will contain multiple sensors for multiple applications, and the sensors are usually asynchronous and their data may be disordered, which makes the fusion of sensor data very important. In this paper, a multi-sensor vehicle tracking fusion method based on information matrix fusion is proposed. The problem of correlation between sensor data due to common process noise and common trajectory history is solved by the method, thus the need to replace global trajectory estimates with fused local estimates at each fusion cycle is eliminated, and the problem of easy scattering of filters due to unknown external environment is improved.
Anonymity And Trust Roles In The Digital Barter Age

Anonymity is generally a controversial feature that presents multiple trade-offs. It preserves privacy but cannot be audited; it ensures voice to the voiceless but is prone to online abuse; it allows whistleblowing, but the information cannot be trusted. It is equally questionable when referring to digital/crypto assets. Depending on the perspectives, it might positively and/or negatively affect digital transformation strategies. This paper's first novelty resides in the original approach to analysing the anonymity and trust roles in electronic commerce transactions from the different stakeholders’ perspectives. A basic but solid framework for consistent digital transformation is presented. It is designed to help decision-makers, policymakers, entrepreneurs and engineers. The authors’ innovative assumption is that tokens and crypto-assets (including the so-called crypto-currencies) should be considered early forms of digital barter, easily substituted in the future. Another assumption is that bartering is the old/new frontier for illegal activities. Current forms of money are not free from pitfalls. It can be demonstrated by the fact that money laundering activities are evergreen and constantly change channels and techniques. Hence, the focus should be on adequately designed digital infrastructures to ensure the same level of trust currently granted to FIAT currencies and physical assets. Physical Assets Bartering is the most basic form of exchange. Money added (at least) two important functions: unit of account and store of value. Money popularity is linked to governments’ public trust and facilitated exchanges. However, it does not mean it is the absolute best form of bartering.
Anti-Fraud Analysis during the COVID-19 Pandemic: A Global Perspective

The ongoing coronavirus disease 2019 (COVID-19) pandemic has brought unexpected economic downturns and accelerated digital transformation, leading to stronger financial fraud motives and more complicated fraud schemes. Although scholars, practitioners, and regulators have begun to focus on the new characteristics of financial fraud, a systematic and effective anti-fraud strategy during the pandemic still needs to be explored. This paper comprehensively analyzes the lessons of anti-fraud that we should learn from the COVID-19 pandemic. By exploring the complex motives and schemes of fraud, we summarize the characteristics of financial fraud activities and further analyze the regulatory challenges posed by financial fraud during the outbreak. To better cope with the fraudulent activities during the pandemic, policy proposals on how to improve the supervision of financial fraud activities are put forward. In particular, the panoramic data and graph-based techniques are powerful tools for future fraud detection.
A Study on the Learning Effect of the Application of PBL(Project-Based Learning) Class Model in Aviation Service Major Subjects

This study was conducted to verify the effectiveness of the PBL learning method to improve the learning ability of aviation service students. For the study, classes were conducted for 15 weeks for 59 college students majoring in aviation services. Through the survey, the effects before and after learning were compared, and in-depth interviews were conducted with four students. The results are as follows. First, it was found that self-directed learning ability, learning commitment, problem-solving ability, and self-efficacy were improved. Among them, learning commitment was found to have improved the most. Second, as a result of conducting in-depth interviews with students, they thought PBL classes were very necessary, and their learning attitudes changed and they felt a sense of achievement. This study is meaningful in that it revealed the results by exploratory and qualitative research on the improvement of students
Virtual stain-to-stain transformations via cascaded deep neural networks (Conference Presentation)

We present a deep learning-based framework to virtually transfer images of H&E-stained tissue to other stain types using cascaded deep neural networks. This method, termed C-DNN, was trained in a cascaded manner: label-free autofluorescence images were fed to the first generator as input and transformed into H&E stained images. These virtually stained H&E images were then transformed into Periodic acid–Schiff (PAS) stain by the second generator. We trained and tested C-DNN on kidney needle-core biopsy tissue, and its output images showed better color accuracy and higher contrast on various histological features compared to other stain transfer models.
Method of using theodolite’s observation data of fixed star to evaluate INS’s attitude accuracy based on time-delay compensation

Abstract In order to evaluate the attitude accuracy of the TT&amp;C’s SINS under dynamic condition, a new method of using theodolite’s observation data of fixed star to evaluate SINS’s attitude accuracy based on time-delay compensation is proposed. The detailed design is given, and the influence of the time delay is analyzed based on real data experiments. The data analysis and experimental results shows that the new method can improve the computation accuracy, and it is valuable for engineering application of evaluating SINS’s attitude accuracy under dynamic conditions.
E-books vs. paper books: comparison of children’s reading comprehension and Behavior

People of the current generation, those born after the 1990s, are sometimes referred to as "Millennials" because of their familiarity to and comfort with technological advancements. They grow up in a world where everyone carries a minicomputer in their pocket (Zainuddin et al., 2020), and they utilize digital tools as an integral part of almost every aspect of their lives. However, studies on how digital technologies affect pupils' academic performance and motivation are still in their infancy. The purpose of this research is to explore how sixth graders' perceptions of their e-reading experiences are influenced by their use of personalized/gamified/PDF electronic reading practices at school and how that, in turn, affects their reading comprehension and motivation levels while studying English as a foreign language (EFL). Using a quasi-experimental design, this research surveys 80 Greek sixth graders from public schools on their experiences with online gaming. The study's subjects are divided into three experimental and one control groups. Each exploratory group received a prescribed reading schedule for one of the three electronic reading formats (PDF, gamified, or customized) during therapy. Contrarily, the control group was given a written guided reading schedule to adhere to. However, there was no discernible difference in the reading comprehension levels of the four groups, despite the fact that the experimental group read from a different medium than the control group did. The findings suggest that EFL students' usage of screen reading may boost their drive to read, which is promising.
A Proposal on How to Use Blockchain to Secure Communications in 5G Ecosystem

5G provides businesses with high-speed Internet access, faster bandwidth, and low latency. The use of IoT and 5G-enabled sensors provides new opportunities within networks. There are several use cases of 5G with IoT health care, agriculture, remote learning, logistics, manufacturing, government, and retail. However, some security and privacy problems must be addressed within the 5G ecosystem. There is a need to secure user and device associations and data integrity as 5G is becoming more and more popular. In this research-in-progress, we seek to solve security and privacy problems in 5G by applying Blockchain technology to secure 5G connections. The research will consist of an interdisciplinary team of researchers from Illinois State University (ISU) and non-academic-industry partners. During the study, we build a prototype that solves problems in agriculture and healthcare. A private 5G network is installed at ISU. Moisture sensors and IoT devices are installed at the university farm to monitor soil moisture. Tracking soil moisture helps to conserve water. Additionally, we also work with a local clinic and provide underserved patients with chronic illnesses with sensors and IoT devices that help them track and monitor their chronic conditions. The patients will have monitors that track blood pressure and blood sugar. The IoT devices and sensors send collected data to a database accessible by healthcare professionals at the clinic. This helps the patients receive care that helps them manage their conditions better. Blockchain will be applied during the proposed study to secure communications between the sensors at the university farm and the private 5G network. Blockchain is also be applied to secure communications between the patients and healthcare providers at the clinic. The collected results are analyzed to see if there is a significant difference in suspicious activities in agriculture and healthcare communications. The results have a broader impact, including helping underserved community members to receive good care in managing their chronic conditions. The results also help secure other use cases of 5G with IoT devices.
Crafting Transferable Adversarial Examples against Face Recognition via Gradient Eroding

In recent years, deep neural networks (DNNs) have made significant progress on face recognition. However, DNNs have been found to be vulnerable to adversarial examples, leading to fatal consequences in real-world applications. This paper focuses on improving the transferability of adversarial examples against face recognition models. We propose Gradient Eroding (GE) to make the gradient of the residual blocks more diverse, by eroding the back-propagation dynamically. We also propose a novel black-box adversarial attack named Corrasion Attack based on GE. Extensive experiments demonstrate that our approach can effectively improve the transferability of adversarial attacks against face recognition models. Our approach over-performs 29.35% in fooling rate than state-of-the-art black-box attacks. Leveraging adversarial training with adversarial examples generated by us, the robustness of models can be improved by up to 43.2%. Besides, Corrasion Attack successfully breaks two online face recognition systems, achieving a highest fooling rate of 89.8%.
Educación, paz y violencia: sobre los orígenes, desarrollos y logros del Proyecto «Transición a la Educación Digital» (TED), ¡Tu escuela en casa!

per se. During the COVID-19 pandemic, formal education systems around the world were severely affected, leaving millions of children, young people, and adults without face-to-face classes, therefore, more vulnerable to present and future violence (which could arise). In this context, the Transition to Digital Education Project arises in Paraguay, to continue formal education but at a distance. The Project impacted in various interesting ways in the lives of students and their educators, for this reason, it is necessary to know their developments, problems, and main consequences; for this, in this work we analyze academic articles, Paraguayan legal regulations (during the pandemic), and related documents, as well as, through an ethnographic perspective, the authors' own experience. The main pedagogical deficiencies before the pandemic will be presented, which influence the development of digital education rapidly imposed. The conclusions show that the collaboration between the State, educational institutions, and civil society (mainly volunteers) in favor of education has served as a mechanism to curb different expressions of violence, contributing less to the deterioration of living conditions due to COVID-19 and avoiding that thousands of students did not lose their academic year or drop out of school.
Privacy Preservation Modelling for Securing Image Data using Novel Ethereum-based Ecosystem

The broad usage of images in real-time applications demands a cloud infrastructure due to its advantages.Many use cases are built where the image data is shared, sharing becomes the core function, and the medical domain takes its broad advantage.The cloud is a centralized infrastructure for its all-operation usages; it depends mainly on the trusted third party to handle security concerns.Therefore, the privacy preservation of the image data or any data becomes an issue of concern.The distrusted system advantages are achieved using blockchain technology for image data security and privacy concerns.The traditional approaches of the security and privacy models raise many apprehensions as these are designed on the centralized systems of the data sharing mechanisms.It is also observed that large data files are not wisely handled, which demands building a framework model that takes image data and any other data of any size to ensure a dependable optimal security system.This paper presents a framework model to achieve optimal time complexity for securing the privacy aspects of the image data or any other data that uses space optimal file system using distributed security mechanism for both the storage and sharing of the data.The proposed framework model for optimal time complexity and security uses a duplication algorithm using stakeholder agreement to ensure efficient access control to the resources using the cryptographic approach to the Ethereum ecosystem.The performance metric used in the model evaluation includes the degree of availability and efficiency.On benchmarks, it performs well compared to the traditional cloud-built distributed systems.The quantified outcome of the proposed scheme exhibits a 42.5% of reduction in time for data repositioning, a 41.1% of reduction in time for data retrieval, a 34.8% of reduction in operational cost, a 73.9% of reduction in delay, and a 61% faster algorithm execution time in contrast to conventional blockchain method.
Automatic Extraction of Indonesian Stopwords

The rapid growth of the Indonesian language content on the Internet has drawn researchers' attention.By using natural language processing, they can extract high-value information from such content and documents.However, processing large and numerous documents is very timeconsuming and computationally expensive.Reducing these computational costs requires attribute reduction by removing some common words or stopwords.This research aims to extract stopwords automatically from a large corpus, about seven million words, in the Indonesian language downloaded from the web.The problem is that Indonesian is a low-resource language, making it challenging to develop an automatic stopword extractor.The method used is Term Frequency -Inverse Document Frequency (TF-IDF) and presents a methodology for ranking stopwords using TFs and IDFs, which is applicable to even a small corpus (as low as one document).It is an automatic method that can be applied to many different languages with no prior linguistic knowledge required.There are two novelties or contributions in this method: it can show all words found in all documents, and it has an automatic cut-off technique for selecting the top rank of stopwords candidates in the Indonesian language, overcoming one of the most challenging aspects of stopwords extraction.
A Study of Encryption for Multimedia Digital Audio Security

Driven by the development of multimedia, the encryption of multimedia digital audio has received more attention; however, cryptography-based encryption methods have many shortcomings in encryption of multimedia information, and new encryption methods are urgently needed.This paper briefly introduced cryptography and chaos theory, designed a chaos-based encryption algorithm that combined Logistic mapping and Sine mapping for confusion and used a Hopfield chaos neural network for diffusion, explained the encryption and decryption process of the algorithm, and tested the algorithm.It was found that the keys obtained by the proposed algorithm passed the SP800-22 test, and the correlation between the three encrypted audio and the original audio was 0.0261, -0.0536, and 0.0237, respectively, all of which were small, and the peak signal-to-noise ratio (PSNR) values were -0.348 dB, -7.645 dB, and -3.636 dB, respectively, which were significantly different from the original audio.The NSCR and UACI were also closer to the original values.The results prove that the proposed algorithm has good security and can encrypt the actual multimedia digital audio.
Factors Affecting Accounting Fraud in Malaysian SMEs

Accounting fraud is a serious phenomenon affecting businesses globally due to the deliberate manipulation and alteration of financial records.This may lead to massive financial loss to the companies, destroying the company's image and the employees' lives.Furthermore, the effect will devastate SMEs as they may fail to sustain their businesses due to the outcomes of fraudulent behavior.This study takes a closer look at Malaysian SMEs with the aim of examining whether relationships may exist between pressure, opportunity, rationalization, ethical values and accounting fraud in Malaysian SMEs.This study is conducted with the goal to contribute to the existing knowledge on accounting fraud in SMEs, which can facilitate the creation of policies and standards for curbing or minimizing accounting fraud.A deductive approach was employed using a quantitative method.Data were collected from 155 managers from SMEs in the state of Selangor, Malaysia using online questionnaires.Data analysis was performed using the Statistical Package for Social Sciences (SPSS) software.The application of the SPSS software was necessary in testing the reliability of the data, linearity, correlation, and multiple regression tests, and examining the relationships between the independent and dependent variables.The research results revealed that only pressure and opportunity had a significant positive relationship with accounting fraud in Malaysian SMEs.The scientific novelty of the study was the test results on the rationalization and ethical values that refute the results of past studies.This study provides implications for the research findings.
Hate Speech and Identity Crystallization in Indonesia’s Cyberpolitics: Peace-Making Criminology Approach

This study aims to present aspects of mass psychology and to examine the representation of crimes against hate speech.This study uses a combination of library research design and field research based on virtual ethnography.Virtual ethnographic studies are used to explore the elements that accompany the choices and attitudes of a person or group through interaction data in the world of social media.This is supported by a peacemaking criminology approach to determine the ecosystem for the use of hate speech.This paper proves that a person's motivation to use speech that can offend or cause hatred is strongly influenced by responses to other behaviors.Hate speech undergoes a cultural configuration in a society controlled by the struggle for hegemony and sensitivity to offense.Peacemaking criminology provides an alternative to criminal independence for perpetrators of hate speech after realizing their mistakes and willing to take responsibility.Hate speech is an expression of identity resistance because of a different social environment so that the imposition of criminal sanctions is not based on domination on the basis of revenge and deterrence.
Epidemiological and educational analysis of SARS-CoV-2

This article analyzes some variables that became a determining factor when talking about education in times of pandemic, a virtual education. In addition to the stress that is typically generated in the student, we can analyze that health plays an important role since it affects their performance and mood, being other reasons for school dropout. In this article we also focus on the science of vaccines as a creative solution so that it can counteract the adverse effects of the SARS-CoV-2 virus, thus providing a solution to one of the problems mentioned above. Finally, education is shown as an important pillar that is in continuous change for both the educator and the student who, despite the changes in lifestyle caused by SARS-CoV-2, are finding ways to adapt and evolve.