Reviewerly/exHarmony huggingface dataset:
Pedestrian detection method and Research of Attention Mechanism in Traffic Scene

Aiming at the occlusion problem when pedestrian detection algorithms are applied in traffic scenes, an occlusion-aware pedestrian 2D saliency texture operator TS-LBP detection algorithm combined with dual attention mechanism is proposed. The algorithm first uses the differential feature-aware fusion method to mine the complementary information between multi-modal features to optimize the channel features; then uses the detection framework with an efficient anchor-free mechanism to greatly reduce the computational complexity of the model and improve the detection speed. The experimental results show that the method in this paper is fast and accurate, and has a good detection effect.
Image classification method based on local receptive field extended S-Mobilenet model

Aiming at the problem that the lightweight deep neural network mobilenet will reduce the classification accuracy, the hollow convolution kernel is introduced into a convolution layer of mobilenet model, and a S-Mobilenet model based on local receptive field expansion is proposed. The model is divided into three structures according to the location of the hole convolution kernel. Without increasing the number of parameters, it can expand the local receptive field of the convolution kernel and improve the classification accuracy. The experiment was carried out on caltech-101 data set, caltech-256 data set and animal classification database of Tubingen University. The results show that S-Mobilenet model can obtain better classification accuracy than Mobilenet, which can be improved by 2% at most.
The Field of Intelligent Recognition that be Advance by Machine Learning

Over the past few years, there has been a sharp increase in software projects using artificial intelligence and machine learning. Although artificial intelligence (AI) and machine learning (ML) are often used interchangeably, they are not the same. With the development of artificial intelligence and machine learning, technologies have gradually become an indispensable part of people's life.). In this essay, the recognition pattern will be mentioned as well as optical character recognition (OCR), intelligent character recognition (ICR) and intelligent word recognition (IWR). And we can easily find that all these three kinds of recognitions have been completely integrated into our daily lives, and convenient our life a lot. The comparison of these three recognitions will also be mentioned in this essay, like what are the differences in performances and usages between them. What’s more, we also can learn the process of these three recognitions.
Aplikasi Sistem Informasi Pengelolaan Paket Wisata (Studi Kasus SATU BIRU Travel)

This research is motivated by the lack of utilization of disseminating information on tour packages on SATU BIRU Travel which has not been computerized. These constraints can make it difficult for consumers to find information on tour packages that have been made. In this study, the data collection techniques used were observation, interviews and literature study. The method used to build this information system is the waterfall and object-oriented design using UML (Unified Modeling Language) and Black Box testing. The process in this study includes the process of managing tour packages, customer registration, customer status, financial income reports, implementation reports. The result of the research that will be implemented in the tour package management information system (the case study SATU BIRU Travel) is a system that can provide tour package information, customer registration, customer status, financial income reports, implementation reports, so that customers are expected to make it easier to order tour packages.
Closing the Gaps in Cloze Letters to Editor: A Verbal Protocol Study

Originally, cloze procedure was introduced by Taylor (1953) in order to assess reading comprehension in the first language. The usefulness of close procedure has often been asserted as it is considered easy to administer and evaluate (Sadeghi, 2021) L1 and L2 reading comprehension. The present study was conducted to find out the impact of cloze instruction on the success in guessing abilities of L2 learners. The impact of function words and content words on the correct guesses of the deleted words was also explored. The theoretical framework given by Bengleil and Paribakht (2004) was adopted with some modifications. The study employed the texts of letters published in newspaper Pakistan Observer. Both control and experimental groups were asked to think aloud their thoughts processes during their hunt for the missing words. The instructional model CALLA___ presented by Chamot and O’ Malley (1994) _ was used. While the experimental group received cloze instruction, the control group was taught in the traditional way.The findings have revealed that after receiving instruction treatment the experimental group outperformed the control group in finding out the appropriate words for the blanks. Furthermore, the subjects of study made more successful attempts for the deleted function words than the deleted content words in the cloze texts.
Correction: Garniwa et al. Application of Semi-Empirical Models Based on Satellite Images for Estimating Solar Irradiance in Korea. Appl. Sci. 2021, 11, 3445

The authors wish to make the following corrections to this paper [...]
Uji Performa Sudu Turbin Berbahan Polimer Serat Karbon

Sultan Wind Turbine is a vertical type wind turbine researched by New Renewable Energy Lab which is used as development for wind energy utilization. In this wind turbine, there are problems in the form of blade resistance which is not strong enough to withstand wind velocity when the wind blows reaches 4 m/s. The blade, which is one of the parts that have the highest liability on the turbine, can be improved in terms of performance and their overall strength. The blade material which currently used is fibreglass, and Styrofoam can be substituted using a better material to improve its performance. Carbon fiber and ABS filament can be substituted as a blanket and airfoil to be used as materials for manufacturing new blades. This research will focus on improvised blades in terms of performance and strength. With an experimental and quantitative approach, the data obtained from the blades that have been improvised with carbon fiber and ABS filament, resulting in blades that are superior from their predecessors.
Adi Shankara’s Saundarya Lahari: A Quest of Spirituality in Devotion and Physical Body

Saundarya Lahiri (Waves of Beauty) by Adi Shankara glorifies the Devi Shakti (power of goddess) and her physical beauty. He projects that the perfect integration of the corporal and the physical aspects lead to the zenith of spiritual realm. Therefore, Bhakti (devotion) divulges the deep veneration of Devi Shakti through Sringaric (amorous) expressions in Saundarya Lahari. It proclaims that inner evolution and the feeling of sublime in the physical beauty of Devi Shakti radiate the sublimity of the spirituality as well. As a profound poetic creation in Sanskrit, it is distinct from the established belief and religious practices, and establishes the truth that physical beauty of Shakti is also the beauty of the spiritual. To explore these dynamics of Saundarya Lahari, this article applies the qualitative approach to research and the interpretive method. It also sheds light on how the genial gravity of bhakti and physical beauty in their totality leads to the better application of critical spiritual pedagogy in teaching.
Rancang Bangun Smoke Generator pada Kecepatan Angin Rendah dengan Wind Tunnel Rangkaian Terbuka

Flow Visualization is the process of making the physical flow of a fluid visible. The air fluid used is transparent, so the flow pattern must be shown by several special methods. There are three methods for visualizing flow: surface flow visualization, particle tracking method, and optical method. Turbulence is one of the most important subjects related to testing the test section in a wind tunnel, at different wind speeds turbulence levels and other flow quality parameters must be kept as close as possible to free flow conditions. The flow visualization test is carried out using a slow motion camera at speeds of 1 m/s, 2 m/s, 3 m/s, 4 m/s and 5m/s which will form a fluid flow in the form of lines of smoke coming out of the nozzle holes inside. Test section. From the research results, the authors can conclude that the flow obtained forms a laminar flow and good suction power from various wind speed variables.&#x0D;
Improved African buffalo optimization-based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Abstract Wireless sensor network (WSN) plays a crucial role in the Internet of Things (IoT), which assist to produce seamless information that have a great impact on the network lifetime. Despite the substantial application of the WSN numerous challenges like energy, load balancing, security, and storage exist. Energy efficacy is regarded as an integral part of the design of WSN; this can be achieved by clustering and multi-hop routing technique using metaheuristic optimization algorithm. This paper concentrates on design of metaheuristics cluster-based routing technique for energy-efficient WSN (MHCRT-EEWSN). The presented MHCRT-EEWSN technique mainly concentrates on the improvements of energy efficiency and lifespan of the WSN via clustering and routing process. For effectual clustering process, the MHCRT-EEWSN model utilizes whale moth flame optimization (WMFO) technique can be utilized by the use of fitness function involving intra-cluster distance, inter-cluster distance, energy, and balancing factor. Besides, the MHCRT-EEWSN model employs improved African buffalo optimization (IABO) based routing technique. To select optimal routes in WSN, the IABO algorithm designs a fitness function comprising multiple parameters like residual energy and distance factor. The experimental validation of the MHCRT-EEWSN model can be tested by making use of a series of simulations. A wide-ranging comparative study shows the promising performances of the MHCRT-EEWSN model than other recent methods.
A Comprehensive Survey of Deep Learning Multisensor Fusion-based 3D Object Detection for Autonomous Driving: Methods, Challenges, Open Issues, and Future Directions

&lt;p&gt;Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the driving environment. Object detection is critical in allowing the perception system to understand the environment. The perception systems, especially 2D object detection and classification, have succeeded because of the emergence of deep learning (DL) in computer vision (CV) applications. However, 2D object detection lacks depth information, which is crucial to understanding the driving environment. Therefore, 3D object detection is fundamental for the perception system of autonomous driving and robotics applications to estimate the objects’ location and understand the driving environment. The CV community has been giving much attention recently to 3D object detection because of the growth of DL models and the need to know accurate locations of objects.&lt;/p&gt; &lt;p&gt;However, 3D object detection is still challenging because of scale changes, the lack of 3D sensor information, and occlusions. Researchers have been using multiple sensors to solve these problems and further enhance the performance of the perception system. This survey presents the multisensor (camera, radar, and LiDAR) fusion-based 3D object detection methods. The fully autonomous vehicles need to be equipped with multiple sensors for robust and reliable driving. Camera, LiDAR, and radar sensors and their corresponding advantages and disadvantages are also presented. Then, relevant datasets are summarized, and state-of-the-art multisensor fusion-based methods are reviewed. Finally, challenges, open issues, and possible research directions are presented.&lt;/p&gt;
Binocular Vision-Based Recognition Method for Table Tennis Motion Trajectory

The latest development in computer technology in sports has increased the popularity of ping pong among people. It calls for designing an intelligent table tennis robot using computer technology to cooperate with table tennis enthusiasts and professional table tennis players to practice. Much research work has already been done in this area. However, this study explores the use of binocular vision to precisely identify, locate, and anticipate the flight trajectory and landing position of table tennis balls. Major issues addressed in this study include identifying fast-moving ping pong balls, calibrating the camera, obtaining the camera’s internal and external settings, and localizing the ping pong balls in three dimensions. A new target recognition method is proposed in combination with the actual needs of the combat ping pong robot. The method combines colour segmentation, background subtraction, and ellipse fitting, which can detect the tail range of flying ping pong balls and find the centre position of the balls. Based on the ellipse fitting analysis of the image, characteristics of the tail of the flying ping pong ball are studied. This study can aid in tracking the trajectories of high-speed flying objects, which is helpful for both aerospace and military industries.
Design of English Translation Mobile Information System Based on Recurrent Neural Network

To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder. This method promotes machine autonomous learning of features and transforms corpus data into word vectors by constructing end-to-end. By mapping the source language and target language directly through the recurrent neural network and selecting semantic error to construct objective function during training, the influence of each part in semantic can be well balanced, and the alignment information is fully considered, which provides powerful guidance for deep recurrent neural network training. The results of the neural network test define the standard BLEU score by 1.51–11.86. Our test scores and BLEU scores at all levels show that data in equivalence play an important role in modeling. Summary. the English translation model based on the neural repetitive fusion is efficient and stable.
The Current Situation and Problems of Major Offerings in Higher Vocational Colleges Based on Industry 4.0: A Case Study of Higher Vocational Colleges in Xiamen

As the concept of lifelong education continues to infiltrate the education system, the examination of degree programs for junior college graduates has become a promotion path for graduates of higher vocational colleges to continue their education. It optimizes the development space for graduates of higher vocational colleges. While the enrollment of junior college graduates for degree programs is expanding, the education system of degree programs for junior college graduates and higher vocational colleges in China has also ushered in a new round of challenges and development opportunities. At present, the education system for degree programs for junior college graduates is not sound enough, and the interface with higher vocational colleges is not close enough. Moreover, there are problems such as poor communication of students moving from higher vocational colleges to universities for undergraduate education and fluctuating quality of personnel training and education. Therefore, it is of significance to study the problems of major offerings in higher vocational colleges. In our study, we took higher vocational colleges in Xiamen as an example. And we systematically study and then analyze the universities offering degree programs for junior college graduates and their majors. Finally, the problems in the connection between the majors of higher vocational colleges and those of universities with degree programs for junior college graduates are discussed, and suggestions are given for the study. Moreover, the universities should respond to the real-time development policy of the country and cultivate interdisciplinary personnel with solid theoretical foundation, innovation skills, research capabilities, professional expertise, and practical skills for social development.
A Comprehensive Survey of Deep Learning Multisensor Fusion-based 3D Object Detection for Autonomous Driving: Methods, Challenges, Open Issues, and Future Directions

&lt;p&gt;Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the driving environment. Object detection is critical in allowing the perception system to understand the environment. The perception systems, especially 2D object detection and classification, have succeeded because of the emergence of deep learning (DL) in computer vision (CV) applications. However, 2D object detection lacks depth information, which is crucial to understanding the driving environment. Therefore, 3D object detection is fundamental for the perception system of autonomous driving and robotics applications to estimate the objects’ location and understand the driving environment. The CV community has been giving much attention recently to 3D object detection because of the growth of DL models and the need to know accurate locations of objects. However, 3D object detection is still challenging because of scale changes, the lack of 3D sensor information, and occlusions. Researchers have been using multiple sensors to solve these problems and further enhance the performance of the perception system. This survey presents the multisensor (camera, radar, and LiDAR) fusion-based 3D object detection methods. The fully autonomous vehicles need to be equipped with multiple sensors for robust and reliable driving. Camera, LiDAR, and radar sensors and their corresponding advantages and disadvantages are also presented. Then, relevant datasets are summarized, and state-of-the-art multisensor fusion-based methods are reviewed. Finally, challenges, open issues, and possible research directions are presented.&lt;/p&gt;
Automatically Correcting Noisy Labels for Improving Quality of Training Set in Domain-specific Sentiment Classification

Classification model performance can be degraded by label noise in the training set. The sentiment classification domain also struggles with this issue, whereby customer reviews can be mislabeled. Some customers give a rating score for a product or service that is inconsistent with the review content. If business owners are only interested in the overall rating picture that includes mislabeling, this can lead to erroneous business decisions. Therefore, this issue became the main challenge of this study. If we assume that customer reviews with noisy labels in the training data are validated and corrected before the learning process, then the training set can generate a predictive model that returns a better result for the sentiment analysis or classification process. Therefore, we proposed a mechanism, called polarity label analyzer, to improve the quality of a training set with noisy labels before the learning process. The proposed polarity label analyzer was used to assign the polarity class of each sentence in a customer review, and then polarity class of that customer review was concluded by voting. In our experiment, datasets were downloaded from TripAdvisor and two linguistic experts helped to assign the correct labels of customer reviews as the ground truth. Sentiment classifiers were developed using the k-NN, Logistic Regression, XGBoost, Linear SVM and CNN algorithms. After comparing the results of the sentiment classifiers without training set improvement and the results with training set improvement, our proposed method improved the average scores of F1 and accuracy by 20.59%.
Heterogeneous Coded Distributed Computing with Nonuniform Input File Popularity

This paper studies the heterogeneous coded distributed computing (CDC) where input files required for job access have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files and a nested coded shuffling strategy to effectively explore coded multicasting opportunities. We then formulate the joint optimization of the proposed file placement strategy and shuffling design variables into a mixed-integer linear programming (MILP) problem. To reduce the computational complexity, we propose a simple two-file-group-based approach to obtain an approximate solution. Numerical results show that the proposed two-file-group-based approach achieves nearly the same performance as solving the MILP problem using the conventional branch-and-cut method but with substantially lower computational complexity.
PENGARUH PENGGUNAAN MEDIA PEMBELAJARAN DALAM PEMBELAJARAN DARING TERHADAP MOTIVASI BELAJAR SISWA SMK PERPAJAKAN RIAU

The purpose of this research is to increase students' motivation in participating in the Japanese class of XII grade students of SMK Perpajakan Riau. The material used in this study was a video learning material chapter 3 about self-introduction or Hajimemashite from the Sakura textbook used by Japanese language teachers for teaching. The method used in this research is quantitative research with data collection techniques using a absence via google glassroom. Based on the results of data management, data observation and data analysis, it can be seen that the effect of learning using learning media in the form of PowerPoint and video is more effective. This effectiveness can be used as an indicator of the learning process that uses learning media to further increase student motivation to attend class better than the learning commonly used by teachers in these schools. Researchers hope that this research can improve the online learning methods used by teachers so that they can attract student interest and motivation in online learning so that students will be interested and diligent in participating in learning without feeling bored with the material provided by the teacher in the future, especially during this covid-19 pandemic
VisRecall: Quantifying Information Visualisation Recallability via Question Answering

Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. In this work, we propose a question-answering paradigm to study visualisation recallability and present VisRecall — a novel dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions of five question types. Furthermore, we present the first computational method to predict recallability of different visualisation elements, such as the title or specific data values. We report detailed analyses of our method on VisRecall and demonstrate that it outperforms several baselines in overall recallability and FE-, F-, RV-, and U-question recallability. Our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.