Reviewerly/exHarmony huggingface dataset:
Reciprocal Questioning Procedures in Reading Comprehension: Does It Increase Students’ Motivation?

Reading is essential and serves as a basic building block in English language learning process. As a result, a teacher must have an effective teaching technique in teaching reading comprehension. This study aims to find out the influence of reciprocal questioning towards students’ motivation in reading comprehension of the third-grade students at SMP Negeri 3 Praya. The research was conducted through experimental research, the pre-test and post-test were used as the design. The instrument was used a set of questionnaires (Likert scale) for motivation test. The data analysis that used was descriptive analysis and inferential analysis for measuring the motivation test in form of questionnaires. Then, according to the result of the test, it showed that t-test 4.865 was higher than the t-table 2.423 at the level of significance 0.50. It means that, there was a positive effect of Reciprocal Questioning procedure towards students’ motivation in reading comprehension.
Digitalization and Information Management Mechanism of Sports Events Based on Cooperative Sensing Model of Multisensor Nodes

This paper provides an in-depth study and analysis of the optimization of sports event management systems using wireless sensor networks. Aiming at the monitoring task of a directed wireless sensor network in a three-dimensional environment, the directed sensing nodes scattered inside the designated monitoring area in a random deployment manner usually have uneven distribution and other problems; we analyze the characteristics of the directed sensor nodes, probabilistic sensing model, and the cooperative sensing model of multiple sensor nodes for monitoring target points and propose a sensing optimization strategy in polar coordinates to guide the three-dimensional plane directed orientation adjustment and sensing optimization of sensor nodes, thus enhancing the sensing capability of network nodes. The experimental results confirm that the algorithm can improve the coverage of the area to be monitored and the quality of sensing service, and it reduces the overall energy consumption of the network by using the distributed node synchronization scheduling mechanism to extend the life cycle of the network to maintain good monitoring capability under the premise of the limited total usage of the directed nodes in wireless sensor networks. The application of wireless sensor network technology in sports competition management mainly includes the application of smart wearable devices in sports competition training, the application of goal-line technology in sports competition, and the application of eagle eye technology in sports events, all three technologies have certain advantages in the application of sports competition, and all of their help to promote the improvement of sports event management and the development of sports industry; the second aspect is wireless sensor. The second aspect is the application of wireless sensor network technology in sports event information management, which is mainly used to collect information related to sports events and fully utilize it to make sports event management more informative and digital, which is helpful to improve the level of sports event management; the third aspect is the application of wireless sensor network technology in sports event stadium management, which is mainly based on intelligent stadiums to create a more spectator-friendly and good experience for the audience, a more ornamental and good experience viewing place, to promote the development and growth of sports industry.
Overview of the Programming Language Python

In this chapter you will learn about the history, basic concepts, and versatile applications of Python.
End-to-End Driving based on Domain Adaptation from Simulation to Real Environment

In this research, we aim to develop a mobile robot that can navigate through on a university campus using end-to-end learning. We can easily obtain data frames that consist of sensory data (e.g., RGB camera) and control signals (e.g., steering) from a driving simulator. Although many prior works have proposed end-to-end driving methods trained on simulated data, they are not applicable to real-world scenarios. Therefore, we propose a domain adaptation method to apply a driving policy learned on simulation data to a similar real-world use case.
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Double-blind • Conference submission management system: Zmeeting submission system • Number of submissions received: 29 papers • Number of submissions sent for review: 29 papers • Number of submissions accepted: 19 papers • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100): 65.52% • Average number of reviews per paper: 2 • Total number of reviewers involved: 17 reviewers • Any additional info on review process: 1. Preliminary Review 2. The papers passed the first review will be reviewed again from the following aspects: Originality, Innovation, Technical Soundness, Applicability, Clarity of presentation and Relevance Contact person for queries: Please submit this form along with the rest of your files on the submission date written in your publishing agreement. The information you provide will be published as part of your proceedings.
Construction and Supply of Lifelong Learning Resources Based on the Internet

Modern information technology represented by the Internet has had a profound impact on human production and lifestyle. The knowledge people have learned at school has gradually failed to meet the needs of daily work and life. In this case, lifelong learning has become a necessary ability for people to adapt to the development of the information age. The characteristics of Internet technology information transmission and resource sharing help the whole people develop personalized lifelong learning. At the same time, the construction and supply of network information resources that support lifelong learning have become the key and core of influencing national learning and lifelong learning.
On Securing Smart Agriculture Systems: A Data Aggregation Security Perspective

Due to the continuous growth in world population, there are significant issues upcoming related to food, health, and education. The most common problem is related to the production and supply of food from agricultural sector. With the recent advancements in technologies, the smart agricultural systems have evolved as an immediate solution for increasing the growth of food production and to meet the market demand. The smart agricultural systems are engineered by combination of sensors that coordinate and monitor the agricultural field’s basic needs like humidity of soil, temperature of field, growth of the crop, etc. These sensors receive the commands from the user application and generate the sensed data to the gateway node and the results are analyzed by the field monitoring teams. Though there is an automated monitoring process, there is a high risk of data breach happening in these remote unstable environments. In this chapter, we aim to conduct a study on how to secure the smart agricultural systems by initially making a systematic risk assessment on the system. Based on the vulnerabilities and risk studied, we propose a light weight Arduino-based smart agriculture model. This model focuses on secure data aggregation using rc4 algorithm. Finally, security analysis is conducted on the proposed model and a project-specific checklist is summarized that highlights the recommendations for securing the smart agriculture systems.
Acquisition in short steps of adaptive action models of mobile robots for target environments by transfer learning

Recently, autonomous mobile robots based on action models trained by deep reinforcement learning have been studied. Generally, it is desirable to train action models in the environment simulating the actual environment. However, it takes many steps to complete training and acquire an action model adaptive to the target environment. A purpose of this study is to acquire an action model adaptive to each environment in short steps by transfer learning from generic action models. The generic action models are acquired by training in environments containing various artificial shapes. An adaptive action models to each target environment can be obtained in shorter steps based on fine tuning of the generic action models.
AN EQUIPMENT-ORIENTED VULNERABILITY THREAT ASSESSMENT METHOD FOR ELECTRIC POWER SUPERVISION AND CONTROL SYSTEMS

With increasing application of information technology in supervision and control of power systems, the risk of cyber-attacks in power grids continues to rise. Organized and premeditated cyber attacks have become prominent security threat to power systems. Vulnerability index is used to describe security degree of a computer controling system, so can be used in power systems, which can be regarded as distributed computer controling systems. This paper proposes a method to assess vulnerability threat of key equipments in electric power supervision and control systems. Considering system configuration and hackers&apos; attack skills, an evaluation model of cyber attack behaviors based on analytic hierarchy process is built firstly. Then a probability model is established to evaluate the probability of each attack path consisting of multi behaviors. Finally, an equipment-oriented vulnerability threat assessment method is proposed, using cyber attack behavior evaluation results and probability of related attack path as iput. The method considers both system configuration conditions and hackers&apos; attack skill evaluation, and realizes quantitative evaluation of vulnerability threat of key equipments. An IEC 61850 based substation system is analyzed as test case, to verify the effectiveness of this method.
Detection of Coronavirus Phishing Emails using Echo State Neural Network

Salahaddin University-Erbil (SUE) is the oldest and largest public comprehensive university in Kurdistan region was founded in 1968 in Sulaimani. In 1981, the university was moved to Erbil, It is the educational home to more than 26,000 undergraduates and more than 900 graduate students.
Facial Image Segmentation by Integration of Level Set and Neural Network Optimization with Hybrid Filter Pre-processing Model

Face segmentation is the process of segmenting the visible parts of the face excluding the neck, ears, hair, and beards.In this field, several methods have been developed, but none of them have been effective in providing optimal face segmentation.Hence, we proposed a novel face segmentation method known as level-set-based neural network (NN) algorithm.This method exploits a hybrid filter for the pre-processing of images, which eliminates the unwanted noises and blurring effect from the images.The hybrid filter is the combination of Median, Mean, and Gaussian filters and effectively removes the unwanted noises.Hence the images are segmented by utilizing level-set-based NN algorithm which is commonly based on the population set and effectively reduces the gap between the predicted and expected outcomes.The proposed method is compared with state-of-art methods such as Fully convolution network (FCN), Gabor filter(GF), multi-class semantic face segmentation(MSFS), and genetic algorithms (GA).From the experimental analysis, it is evident that the proposed work achieved better results comparing to other approaches.
ON RECENT PUBLICATIONS OF THE STATE INSTITUTE FOR ART STUDIES

The article presents an overview of publications of the State Institute for Art Studies (Moscow, Russia), whose activities involve the organisation of extensive scientific research in the theory and history of art, philosophy of art, economics of art and cultural studies. The author pays special attention to the major projects of the last two years, the content and scope of which demonstrate the exceptional role of the Institute in preserving Russia's cultural heritage in all types of art, conducting fundamental research and supervising interinstitutional projects. The first section of the article outlines the main publications of the Institute focusing on the study and promotion of Russian culture in the world artistic space. The second section, titled "In Memory of Deceased Teachers", is devoted to the legacy of renowned art historians who worked at the Institute. The third section deals with publications that contain new or previously unknown facts about the life and creative pursuits of representatives of art, discovered by the authors while analysing their epistolary works, memories, diaries and manuscripts. The section "On the Interweaving of Cultures and the Languages of Art" reviews published monographs, joint scientific works and conference proceedings that demonstrate an integrated approach to the study of art.
Challenging the Long Tail Recommendation on Heterogeneous Information Network

Recommender system, regarded as the lifeblood of many web systems, plays a critical role of discovering interested items from near-infinite inventory and exhibiting them to potential users. However, most of the existing recommender systems usually tend to recommend popular items and cannot discover niche items to surprise users, which is well known as the long tail problem. Data sparsity is the primary cause that users&#x2019; historical data are not enough to learn their detail interests. Another reason is that the learning models have to neglect some individuality information for global optimum. In this paper, we propose a novel suite of heterogeneous information network (HIN) based methods for long tail recommendation. We first model both users&#x2019; behavior data and context data with a unified HIN to handle the data sparsity issue. Then, we propose a basic solution that predict user&#x2019;s behavior based on its similar historical behaviors via Degree-aware General SimRank on HIN. To improve the accuracy, we investigate the contributions of different typed data, a novel enhancement framework is proposed based on deep neural network. Distinct from the traditional learning models, our methods predict user&#x2019;s behavior case by case which maximizes the personality information and can effectively discover the interested niche items. Experiments show that the proposed algorithm outperforms state-of-the-art techniques in long tail recommendation.
113 Making Virtual a Reality During a Pandemic: Improving Learning Opportunities in Medical Education through Virtual Reality Simulation

William Osler was the first to be credited with taking medical students out of the lecture theatre and to the bedside Could VRS provide a meaningful learning opportunity during the first wave? Could we elicit the strengths and weaknesses of virtual simulation in medical learning? We used the Oxford Medical Simulation (oxfordmedicalsimulation.com) VRS platform where the learner manages an acutely unwell patient with specified learning objectives (opting for the 2-D to make it accessible to students at home). Scenarios were grouped, accompanied by didactic learning resources and released on a weekly schedule. Data were collected with consent on the number of scenarios accessed, performance score and student feedback. The VRS course ran for 5 weeks (access extended to 11 weeks). In total, 224 students expressed an interest in accessing the VRS platform. Of the 224 students, 64 accessed the scenarios (50% first-year students). The students accessed 821 scenarios. The average score on all first attempts of scenarios was 75%; second attempts 78% and third attempts 90% ( The high initial response rate suggested student interest and engagement. The low (21%) conversion rate to accessing the VRS platform may be explained by initial technical issues and the voluntary nature of the project. The quantitative data show the importance of repetition in improving learning. Participation over time may improve with incorporation into the medical school curriculum. Lower usage among the final-year medical students may be explained by volunteering and early commencement of clinical duties. This innovation reveals some strengths of VRS: basic equipment; learner-directed; improved performance and student interest. Overall, the VRS platform allowed the delivery of a rapid response to fill a gap in clinical education. The next phase of this project will be to provide live tutor-supported debrief.
System for Fake Currency Detection Using Image Processing

Abstract: Counterfeit money has always existed an issue that has caused many problems in the market. Technological growth development has made it possible to create extra counterfeit items which are distributed in the mitigation market the global economy. Bangui existing banking equipment and so on trading sites to check the authenticity of funds. But the average person does not do that have access to such systems and that is why they are needed in order for the software to receive counterfeit money, which can be used by ordinary people. This the proposed system uses image processing to find out if the money is real or fake. System built uses the Python system completely language. It contains similar steps grayscale modification, edge detection, separation, etc. made using appropriate methods. Keyword: Counterfeit currency, Image Processing, Python programming language, grayscale conversion, edge detection, segmentation.
Introduction to Machine Learning

When we are born, we are literally incapable of doing anything. We can’t even hold our head straight for a few months, but eventually, we start learning. During those days, we all fumble, make tons of mistakes, fall down, and bang our head many times but slowly learn our way to sit, walk, speak, and finally run. As a built-in mechanism, we don’t require a lot of examples to learn about things around us. For example, just by observing two to three dogs around, we can easily learn to recognize a dog from a cat. We can easily differentiate between a cycle and a car by seeing a few cars and bikes around. Even though it seems very easy and intuitive to us as human beings, for machines it can be a herculean task.
Multi-Dimensional Analysis of the Factors Affecting the Timbre of Bamboo Flute

Bamboo flute is one of the ancient national wind instruments in China, with a history of more than 8,000 years. It not only has a unique style and strong expressive power, but also has profound knowledge and scope for playing techniques. With the development of the flute, through the cooperation among the breath, the tongue, mouth, and fingers, the timbre will be played more crisply and brightly. There are a variety of performance skills in breath. And these skills have their own characteristics, which play an important role in the expression of music. This paper mainly discusses the tonguing. The bamboo flute&apos;s bright and crisp, high and smooth, graceful and beautiful timbre and rich artistic expression are inseparable from the use of various skills in the performance. Among the skills, tonguing is an important performance skill.
Underwater Distance Measurement Using Machine Learning

We develop a ROV to capture sea urchins. As a next step, we move the ROV automatically. To achieve this function, we need to know the distance between ROV and sea urchins. In this paper, a binocular stereo vision ranging system based on OpenCV-Python is designed and used with Viola-Jones algorithm for binocular recognition. Zhang’s algorithm method is used to calibrate the binocular cameras to obtain internal and external parameters, which provide necessary data for the subsequent image processing and stereo matching. In this paper, OpenCV-Python is completely used to build the system construction to achieve the simplicity of the platform, which has better robustness and high accuracy. Stereo matching adopts SGBM algorithm, which is relatively fast and real-time in stereo matching, and can be better applied to ROV sea urchin detection.
3D Semantic Mapping: a Benchmark and Baseline Method

In this paper, a simple and effective real-time 3D semantic mapping method is proposed. The proposed method take per-frame bounding box detections and sensor (camera) extrinsic transformation estimates as inputs and produces a set of static 3D bounding boxes in world coordinate system as 3D semantic mapping results. Each object has a Kalman filter as its state estimator and intersection over union of 3D bounding boxes is used for data association. To evaluate the proposed method, a new benchmark is derived from the KITTI object tracking evaluation. Ground-truth semantic maps are constructed based on oxts data and labeled 3D bounding boxes of KITTI. Three novel semantic map-centered metrics: DAOD, AAOD, and PRVO are proposed. Experiments are conducted to evaluate the proposed method. Experiments show that the metric and benchmarking dataset can serve as a new benchmark platform for easier comparison of new methods.
Visual Servoing Corresponding to Various Obstacle Placements and Target Object Shapes Based on Learning in Virtual Environments

In this paper, we propose an end-effector positioning method that can handle various obstacle placements and target object shapes. In the proposed method, two Convolutional Neural Networks (CNNs) are used to obtain ideal movement and avoidance movement, then these outputs and other conditions such as movable ranges of each joint are used to calculate a final movement by means of Quadratic Programming (QP) method. First, training data is collected in a virtualized environment in the physics simulator. This reduces the load on the actual experiment. On the other hand, the same environment is also used while the robot is actually performing visual servoing. This method enables visual servoing that is not affected by changes in the texture of a real environment. We confirmed that the proposed method was successful even if the target object is largely hidden by obstacles.