Reviewerly/exHarmony huggingface dataset:
Chaotic Systems-Based Secure Communication Scheme for Detection of Wind Turbines

With the popularity of the Industrial Internet of Things, there are huge security challenges in the storage and transmission of image information. How to ensure that transmitted images from malicious attacks and random tampering have become a hot topic of information security. In this paper, a new secure communication scheme is proposed in consideration of the security requirements of transmitted images during the detection of wind turbines process. The scheme is based on the image diffusion method of chaotic systems. By utilizing the sensitivity of the chaotic systems to the initial conditions and parameters, and using the parameters as the key to realizing the encryption and decryption of the image. Furthermore, a large amount of image information will be acquired in real-time during the inspection process of the fan blades by UAVs. Due to the limited computing and storage resources of UAVs, C-E-D three-layer architecture is adopted, which can effectively offload the complex tasks to the edge and cloud to significantly reduce UAVs latency and energy consumption. Test and analysis results show that the proposed encryption scheme is effective and reliable for image security communication applications.
Research on Online and Offline Hybrid Teaching Mode of College English Writing for Non-English Majors Based on iWrite2.0

In recent years, automatic writing evaluation software have gradually been applied to language teaching in Chinese universities in order to lighten the burden of writing teaching. However, due to the limitations of automatic evaluation software and the special features of writing course, the effectiveness of teaching still needs to be improved. Aiming at this problem, ¡Write 2.0 automatic writing evaluation software is integrated into the teaching of writing. Compared with the previous automatic writing evaluation software, iWrite 2.0 is based on associative word mining, Bi-RNN and big data corpus, guided by the theories of second language writing. It greatly improves the consistency of manual evaluation of automatic evaluation results. With the support of iWrite 2.0, this paper intends to construct an online and offline hybrid teaching mode for non English majors, following three principles---Engagement, Evaluation and Competition(EEC). this EEC hybrid teaching mode is applied to college English courses for freshmen lasting for one semester. The research findings have shown that this teaching mode could effectively improve students’ writing performances, enhance their participation in learning and reduce the writing anxiety of students. With rich writing resources, diversified evaluation and timely feedback provided by iWrite 2.0, this teaching mode has greatly improved the effectiveness of writing teaching.
Challenges of comprehensive training in engineering schools

This paper presents the challenges that must be assumed in the training of engineers so that these professionals have the hard and soft skills necessary to face the challenges of industry 4.0. The present demands professionals with high skills in new technologies, constantly changing, making universities mustreinforce their curricula for teaching so that learning can be permanent and effective. The permanent reinforcement in the curriculum, the focus on industrial and technological education, and a global vision will be the key for universities to achieve competitive professionals. For this work, the actions of some publicuniversities in the training of engineers were analyzed. Finally, an analysis of the strategies universities must assume so that future engineers have sufficient and necessary skills for modern industry is presented.
MKS ransomware attack exposes risk to chips industry

Headline INT: MKS cyberattack exposes risk to chips industry
Argumentative Scheme for Abduction

Introduction. The paper scrutinizes abduction through the lens of the argumentation theory. Abduction is treated as an argument with a special argumentative scheme. Argumentation schemes are seen as stereotypical patterns of common types of arguments used in everyday discourse. The main issue of this publication is to specify the scheme of abductive argument and supply it with so-called critical questions. Such questions should identify, reconstruct and evaluate abduction in dialogs. Methodology and sources. At first, I analyze D. Walton and S. Yu &amp; F. Zenker’s patterns of abductive argument, scrutiny their advantages and disadvantages. Then, based on the results of relatively new logical and philosophical investigations, I systemize the peculiarities of abduction. The role of D. Gabbay and J. Wood’s model is especially emphasized. Results and discussion. Both approaches (D. Walton and S. Yu &amp; F. Zenker) are not free of problems. However, several recent logico-epistemological specifications of abduction can reduce them. I mean the position that abduction preserves ignorance and presumes J. Wood’s conclusionality relation. This reasoning is weak and cannot be distinguished from other arguments. These proposals and almost unknown (with interrogative conclusion) Ch. S. Peirce’s scheme of abduction produce a core of argumentative scheme. Conclusion. I provide a version of argumentative scheme of abduction with the set of critical questions. Its formal structure is defined as a move from the consequent to antecedent with the investigand mood conclusion while the material side is seen as reasoning from surprise to investigation. Modified D. Gabbay and J. Wood’s model clarifies the controversial aspects of this argumentative scheme. It also specifies critical questions functions since they lose their traditional role of evaluation.
An Application of Logistic Regression Model in the Student’s Academic Performance at HUST, Vietnam

In this paper, we use logistic regression model to study the factors affecting students’ results in the subject named ‘Analysis III (MI1130)’. The data is collected from the results of the questionnaires and the actual students’ academic performance given by the School of Applied Mathematics and Informatics (SAMI) of HUST. Data analysis provides a precise estimate of 5 factors that make an impact on students’ learning results in the subject, including the living environment, the CPA result, self-study time, difficulty in solving some Analysis III problems, and awareness of the application of the subject in reality. Then, some suggestions can be made to improve the quality of training and learning outcomes of students.
Utilization of Augmented Reality Technology in the Campus Environment

Technological advancements are accelerating in the modern day; one such advancement is augmented reality (AR) technology. Naturally, these advancements have an effect on various sectors of life, one of which is education. The education industry will be the most impacted if campuses implement and deploy augmented reality technology. One of these is through the use of augmented reality technology as an application-based learning medium for students in order to motivate them to learn and make it easier for them to improve their academic performance on campus. The issue at hand is the absence of utilization of augmentation reality technology as a learning medium on campus, which, of course, does not capitalize on technical advancements to improve student interest in learning. This study employs a research and development model. The method is divided into four phases: (1) planning; (2) data collecting; (3) analysis, discussion, and development; and (4) documentation. The findings of this study reveal that students enthusiastically support the usage of augmented reality technology as a learning medium in order to boost accomplishment and introduce new experiences through its use. However, it is required to have a system capable of serving as a technological medium, so that the construction of a system capable of serving as a medium for augmented reality may be easily accessed by students, who hope that this system would enable them to better their academic performance.
Improvement of Rooftop Solar Panels Efficiency using Maximum Power Point Tracking Based on an Adaptive Neural Network Fuzzy Inference System

Rooftop solar panels are a strategy for achieving Indonesia's renewable energy goals, but their non-linear characteristics make them difficult to control, especially in the face of extreme weather changes.An effective controller is needed to optimize the power output of solar panels.This study proposes a Maximum Power Point Tracking (MPPT) controller based on an Adaptive Neural network Fuzzy Inference System (ANFIS) to address this control problem.The capacity of the rooftop solar panels is 3,430-Watt peak (Wp) and they are connected to a 220-Volt (V) grid system.The system is designed, simulated, and analyzed using the Simulink model.The proposed ANFIS MPPT control for rooftop solar panels is compared to Perturb and Observe (P&O) MPPT and no MPPT systems.The simulation results show that in rapid changes in irradiation and extreme temperature, the efficiency of MPPT based on ANFIS is better than P&O MPPT and no MPPT by 0.4523 and 0.1115%, respectively.
Chosen Methods of Improving Small Object Recognition with Weak Recognizable Features

Many object detection models struggle with several problematic aspects of small object detection including the low number of samples, lack of diversity and low features representation. Taking into account that GANs belong to generative models class, their initial objective is to learn to mimic any data distribution. Using the proper GAN model would enable augmenting low precision data increasing their amount and diversity. This solution could potentially result in improved object detection results. Additionally, incorporating GAN-based architecture inside deep learning model can increase accuracy of small objects recognition. In this work the GAN-based method with augmentation is presented to improve small object detection on VOC Pascal dataset. The method is compared with different popular augmentation strategies like object rotations, shifts etc. The experiments are based on FasterRCNN model.
HTTPA: HTTPS Attestable Protocol

Hypertext Transfer Protocol Secure (HTTPS) protocol has become an integral part of modern Internet technology. Currently, it is the primary protocol for commercialized web applications. It can provide a fast, secure connection with a certain level of privacy and integrity, and it has become a basic assumption on most web services on the Internet. However, HTTPS alone cannot provide security assurances on request data in computing, so the computing environment remains uncertain of risks and vulnerabilities. A hardware-based trusted execution environment (TEE) such as Intel® Software Guard Extension (Intel® SGX) or Intel® Trust Domain Extensions (Intel® TDX) provides in-memory encryption to help protect runtime computation to reduce the risk of illegal leaking or modifying private information. (Note that we use SGX as an example for illustration in the following texts.) The central concept of SGX enables computation to happen inside an enclave, a protected environment that encrypts the codes and data pertaining to a security-sensitive computation. In addition, SGX provides security assurances via remote attestation to the web client to verify, including TCB identity, vendor identity, and verification identity. Here, we propose an HTTP protocol extension, called HTTPS Attestable (HTTPA), by including a remote attestation process onto the HTTPS protocol to address the privacy and security concerns on the web and the access of trust over the Internet. With HTTPA, we can provide security assurances for verification to establish trustworthiness with web services and ensure the integrity of request handling for web users. We expect that remote attestation will become a new trend adopted to reduce the security risks of web services. We propose the HTTPA protocol to unify the web attestation and accessing Internet services in a standard and efficient way.
Assessing the Effect of Open-Pit Mining Activities and Urbanization on Fine Particulate Matter Concentration by Using Remote Sensing Imagery: A Case Study in Binh Duong Province, Vietnam

Fine particulate matter (PM2.5) leads to respiratory and cardiovascular diseases. Nowadays, PM2.5 monitoring on a large scale is a significant concern. The study aims to estimate the PM2.5 concentrations and assess the effect of open-pit mining and urbanization on PM2.5 concentrations by using satellite observations and ground-based stations over Binh Duong province, Vietnam. Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly data of PM2.5 concentrations, temperature, and relative humidity at ground-based stations in 2020 (May–December) are matched to establish the relationship between AOD and PM2.5. Linear regression and multiple linear regression methods show a good correlation with R = 0.806 and R = 0.817, respectively. The study applies both regression models to estimate the spatial distribution of monthly PM2.5 concentrations over Binh Duong province and validate with ground-based stations data (R = 0.878; RMSE = 6.62 µg/m3). The results show that the annual PM2.5 concentrations are high in the southern districts where it is densely populated and tend to decrease in the northern districts of which the highest are found in Di An (18.76 µg/m3) and Thuan An (18.76 µg/m3), the lowest is in Phu Giao (14.20 µg/m3). The study demonstrates that the development of urban built-up density and population density increases the contribution of fine particles. At the same time, open-pit mine areas are the main contribution of coarse particles.
Text Regression Analysis for Predictive Intervals Using Gradient Boosting

In this paper, we aim to explore text data analysis with the help of different methods to vectorize the data and carry out regression methods. At present, we have a lot of text categorization techniques, but very few algorithms are dedicated to text regression. But many regression models predict only a single estimated value. However, it’s more efficient to predict a numerical range than a single estimate, since we can be more sure that the true value will be within the range rather than considering the estimated value as the true value. We aim to combine both these techniques in this paper. The goal of this paper is to collect text data, clean the data, and create a text regression model to find the best-suited algorithm that could be used in any situation, through the use of quantile regression.
Digital Transformation of Supply Chain with Supportive Culture in Blockchain Environment

This study aims to evaluate the effect and usefulness of digital transformation of supply chain management (SCM) on blockchain with a supportive culture. This paper explores the effect of blockchain on SCM under consideration of automated controls with smart contracts, fundamental attributes, cooperation, supportive culture, transparency and identification, and trust building. Here, this study finds that the supportive culture has great potential to boost the transformation of SCM rapidly and successfully. Blockchain technology has the potential to transmit the supply chain. Finally, this current study indicates that the transformation of SCM in blockchain with supportive culture has a positive impact on the success of organizations. Therefore, this study inspires policymakers and stakeholders to ensure a supportive environment to build a robust sustainable supply chain that will be traceable, more effective, and efficient.
Leveraging Moroccan Arabic Sentiment Analysis Using AraBERT and QARIB

Given the significance of Sentiment Analysis, NLP emerged with studies focusing on this task. The studies being conducted to investigate Arabic Natural Language Processing are not as significant as those in other non-Latin alphabet languages are. This lack of research is due to the rich and complex nature of the Arabic language, as well as the lack of free lexical resources. When it comes to dialectal Arabic, the studies become even more limited, revealing additional challenges. As a result, our paper focuses on Moroccan Arabic Sentiment Analysis by incorporating pre-trained Arabic BERT models -AraBERT and QARIB- throughout the process with various combinations such as SVM, CNN, and fine-tuning the pre-trained model itself. Our proposed system achieved the highest accuracy of 93% while using QARIB model. The research results obtained using our approach are compared with those of other leading methods, showing that our approach is highly effective and can provide valuable guidance for future improvements.
Real Time Multi-digit Number Recognition System Using YOLOv3 and YOLOv5

To cope with modern requirements, recognition of combined multi-digit numbers are necessary. State-of-the-art methods have used object detection models to recognize the individual digits and this way makes the automatic multi-digit detection/recognition techniques not effective, not certain and not stable. For this reason, it is necessary to apply a learning-based method/system to detect the multi- digits. In this study, YOLOv3-tiny and YOLOv5 based real-time multi-digit number detection/classification system has been designed. Images have been taken from the camera, the multi-digit number's region has been localized and finally, the multi-digit recognition process has been performed. In cases where more than one digit number is sent to the system, it is possible to find out how many digits this multi-digit number has by processing the probabilities of what each written number may be, separated by deep learning. MNIST and Street View House Number (SVHN) datasets have been used in the experimental study. The results have been analyzed comparatively and the training and test results have been given.
Implementation of a Tag Playing Robot for Entertainment

Although robots have been around for a while now, robots continue to charm and attract people. The attraction has led to the increasingly numerous applications of robotics in entertainment, such as in crowd attractions, schools, amusement parks, or individuals’ homes for enjoyment. In this paper, two self-sufficient robots are designed, implemented, and controlled to play a tag game. The two autonomous and identical robots will be set in a bounded space, the playing field. The objective is for one robot, playing the part of “It” to find, identify, and tag the second robot. Then, the tagging process of an object will activate a Bluetooth pairing protocol, through which the “It” robot will confirm the identification of the other robot as a team member. After confirmation, the two robots will switch roles and begin the game again. The authentication process is performed through the Bluetooth protocol, while the LiDAR (Light Detecting And Ranging) sensor decides the direction of the other robots. Finally, the robots were implemented and tested in different scenarios, and they could identify each other on the playing field and switch roles successfully.
FinTech Risk Management and Monitoring

The recent emergence of financial technology innovations in the financial services industry also faces many challenges due to some significant risks. This chapter aims to identify specific fintech risks and appropriate ways to manage the risks. A qualitative research method is used to explore the objectives of this study. The findings of this study include the major risks behind the rapid development of fintech, and the fintech risk management steps. Also, this study identifies four key regulatory techniques that have important applications in managing and monitoring fintech risks. Finally, the findings summarize the main challenges of fintech risk management.
Learning Video-Independent Eye Contact Segmentation from In-the-Wild Videos

Human eye contact is a form of non-verbal communication and can have a great influence on social behavior. Since the location and size of the eye contact targets vary across different videos, learning a generic video-independent eye contact detector is still a challenging task. In this work, we address the task of one-way eye contact detection for videos in the wild. Our goal is to build a unified model that can identify when a person is looking at his gaze targets in an arbitrary input video. Considering that this requires time-series relative eye movement information, we propose to formulate the task as a temporal segmentation. Due to the scarcity of labeled training data, we further propose a gaze target discovery method to generate pseudo-labels for unlabeled videos, which allows us to train a generic eye contact segmentation model in an unsupervised way using in-the-wild videos. To evaluate our proposed approach, we manually annotated a test dataset consisting of 52 videos of human conversations. Experimental results show that our eye contact segmentation model outperforms the previous video-dependent eye contact detector and can achieve $$71.88\%$$ framewise accuracy on our annotated test set. Our code and evaluation dataset are available at https://github.com/ut-vision/Video-Independent-ECS .
Spatial Temporal Network for Image and Skeleton Based Group Activity Recognition

Group activity recognition aims to infer group activity in multi-person scenes. Previous methods usually model inter-person relations and integrate individuals’ features into group representations. However, they neglect intra-person relations contained in the human skeleton. Individual representations can also be inferred by analyzing the evolution of human skeletons. In this paper, we utilize RGB images and human skeletons as the inputs which contain complementary information. Considering different semantic attributes of the two inputs, we design two diverse branches, respectively. For RGB images, we propose Scene Encoded Transformer, Spatial Transformer, and Temporal Transformer to explore inter-person spatial and temporal relations. For skeleton inputs, we capture the intra-person spatial and temporal dynamics by designing Spatial and Temporal GCN. Our main contributions are: i) we propose a spatial-temporal network with two branches for group activity recognition utilizing RGB images and human skeletons. Experiments show that our model achieves $$97.1\%$$ MCA and $$96.1\%$$ MPCA on the Collective Activity dataset and $$94.0\%$$ MCA and $$94.4\%$$ MPCA on the Volleyball dataset. ii) we extend the two datasets by introducing human skeleton annotations, namely human joint coordinates and confidence, which can also be used in the action recognition task. The code is available at https://github.com/zxll0106/Image_and_Skeleton_Based_Group_Activity_Recognition .
MGTR: End-to-End Mutual Gaze Detection with Transformer

People’s looking at each other or mutual gaze is ubiquitous in our daily interactions, and detecting mutual gaze is of great significance for understanding human social scenes. Current mutual gaze detection methods focus on two-stage methods, whose inference speed is limited by the two-stage pipeline and the performance in the second stage is affected by the first one. In this paper, we propose a novel one-stage mutual gaze detection framework called Mutual Gaze TRansformer or MGTR to perform mutual gaze detection in an end-to-end manner. By designing mutual gaze instance triples, MGTR can detect each human head bounding box and simultaneously infer mutual gaze relationship based on global image information, which streamlines the whole process with simplicity. Experimental results on two mutual gaze datasets show that our method is able to accelerate mutual gaze detection process without losing performance. Ablation study shows that different components of MGTR can capture different levels of semantic information in images. Code is available at https://github.com/Gmbition/MGTR .