Reviewerly/exHarmony huggingface dataset:
A semi-supervised learning for teacher-student model based on structural reparameterization

Semi-supervised learning (SSL) has attracted much interest for its ability to increase model performance using unlabeled data in recent years. The mainstreaming SSL frameworks are built on the teacher-student model. However, the structural reparameterization (SR) model has not been thoroughly studied in the teacher-student model. The SR technology enhances model capacity and improves performance during the training phase. Therefore, we introduce the SR model in the SSL framework, where the SR model is adopted to construct the teacher-student model for image classification. The consistency regularization is applied to the teacher and student models, and the teacher model’s weights are updated based on the exponential moving average (EMA) strategy. To verify the effectiveness of our approach, we conduct experiments using the CIFAR-10 and Food-101 datasets. Compared to the supervised learning of the SR model, our SSL framework has achieved better performance on these datasets.
Unsupervised anomaly detection method based on deep learning and support vector data description

Anomaly detection in unlabelled and highly imbalanced high-dimensional monitoring data is one of the most urgent and challenging industry problems in the energy industry. Based on the powerful high-dimensional data analysis capabilities of autoencoders, the use of autoencoders for anomaly detection is becoming more and more popular. This paper proposes an anomaly detection method based on deep learning and support vector data description. First, feature engineering is built based on an optimized serial deep autoencoder; second, different feature combinations are studied and compared; finally, anomaly detection based on support vector data description. In this paper, experiments are carried out on the actual operating data of a real steam turbine to verify the effectiveness and accuracy of the proposed method.
CFSORT: improved SORT based on coarse to fine mechanism for object tracking

In recent years, object tracking attracts much attention as a upstream task in many specific area. Multi-object tracking is one of them and its challenge are ID switch and ID re-matching due to object occlusion and image quality. In this paper, two possible methods are introduced to improve the baseline. In ReID model, a data augmentation method is built. Its main idea is to discuss and reduce the influence of background by retraining the ReID model. As ReID focuses on the features of objects in most cases, backgrounds can affect the feature metric significantly. In this situation, different objects in different backgrounds are captured and extracted as train data. Specially, a pure background, or background without objects are used in triplet loss to maximum the similarity distance. Besides, an updated SORT is implemented as CFSORT. In CFSORT, features are extracted by retrained ReID and two new processes or units are created. Comparing with the baseline, CFSORT requires lower confidence objects and emphasis a “coarse to fine” process. Those two new units are corresponding to similarity and IOU measurement. Currently, all new detection will be treated equally at first matching action. Similarly, more detection and tracked objects will match in new IOU unit which is stricter than the normal one. As a consequence, the result indicates that CFSORT has an improved performance on several evaluation scores in different scales. Meanwhile, this work can also prove the importance of accepting low confidence.
A fast deduplication scheme for stored data in distributed storage systems

Data deduplication can effectively reduce data redundancy. However, its write performance is insufficient for existing storage systems, due to the additional calculation and I/O operations. In order to improve the deduplication speed in a distributed storage system, we propose FastDedup, a fast and effective deduplication scheme that focuses on the stored data. FastDedup improves deduplication speed through deduplication task distribution model and multi-container pool technology. Specifically, the deduplication task distribution model maintains the correctness for multiple deduplication nodes working simultaneously. The multi-container pool technology saves the operation time on the data merging stage. Evaluation results on three real backup datasets demonstrate that, compared to the unimproved technique, FastDedup increases deduplication throughput by 3.2% - 69.1%.
A Teaching Design for College English Writing Based on POA

POA theory plays a significant role in solving the problem of "separation of learning and using" among college English majors and improving their writing level.However, how to effectively use POA theory for teaching design and apply the theory to practice still puzzles some teachers.It is necessary to elaborate on the mediating role of teachers in the facilitation process for the purpose of improving the teaching quality of English writing, which can make a necessary supplement to the empirical research of this theory.
Smoothing and Decomposing a Time Series

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Classical Regression with ARMA Residuals

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Summary Statistics of Stationary Time Series

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
The Algebra of Differencing and Backshifting

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Machine Learning Methods for Time Series

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
ARIMA(p, d, q)(P, D, Q)F Modeling and Forecasting

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Introduction to Time Series Data

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Vector Autoregression

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
AUDIO ASSISTANT BASED IMAGE CAPTIONING SYSTEM USING RLSTM AND CNN

There are millions of people in the world who suffer from complete or partial loss of vision.As we know, these visually impaired or partially sighted people face a lot of problems reading or identifying any local scenarios.To vanquish this situation, we developed an audio-based image captioner that will identify the objects in an image and form a meaningful sentence that gives the output in the aural form.This project is developed so as to facilitate the blind in knowing what's happening around them and help them in their daily chores.It provides audio captions for images.Initially the system is given an image, which is analyzed to form textual descriptions, which are then converted into audio form.The project uses the deep learning techniques such as CNN for image feature extraction and LSTM for generating captions by analyzing the image features.For training a dataset consisting of 8000 images is used.The model performance is evaluated using bleu score.
Human Recognization Activity and Maximum Motion Representation in Surveillance Video

Recognizing human actions in the real-world environment finds vital applications, Hence, machine vision studies in this field become crucial.This research aims to extract human activity from video sequences.The number of activities are required for human action recognition, including the gathering of visual data, the identification and presentation of robust features, and the training of classifiers with strong discriminative abilities.The action recognition method employed in this research is based on maximal motion identification.The Region of Interest (ROI) difference picture is utilized to extract motion information, and the Block Based Motion Intensity Code (BBMIC) is extracted as a feature.The Weizmann action dataset, which includes 10 actions (including "walk," "run," "jump," "side," "bend," "wave1," "wave2," "pjump," "jack," and "skip"), was used in the trials.A variety of the tree-based classifiers, including Random-Tree, the Random-Forest, and the Decision-Tree (J48), were also used.According to the experimental findings, this method uses the Weizmann dataset to recognize the activities with an overall quality rate of 94.20%.This is more effective than other tree-based algorithms.Performance of the suggested strategy is on par with that of well-established, established procedures.
ONLINE QR CODE ATTENDENCE SYSTEM

In today's fast-paced world, attendance tracking plays a crucial role in various domains, including educational institutions, corporate organizations, and events management.Traditional attendance systems often suffer from inefficiencies, manual errors, and time-consuming processes.To address these challenges, we propose an innovative solution: the Online QR Code Attendance System.The Online QR Code Attendance System leverages the power of QR codes and mobile technology to automate attendance tracking, improving efficiency and accuracy.The system comprises a user-friendly mobile application and a centralized database for data management.Students or participants are provided with unique QR codes, which can be generated and accessed through the mobile application.When attending a class or an event, individuals simply scan their QR codes using their smartphones, which instantly records their attendance.The captured data is then securely transmitted and stored in the centralized database, ensuring real-time tracking and easy access to attendance records.The system offers several advantages over traditional methods.It eliminates the need for manual attendance sheets and reduces the risk of errors associated with manual data entry.Moreover, the real-time nature of the system enables instant attendance updates, ensuring accurate and up-todate information for instructors, administrators, or event organizers.Additionally, the Online QR Code Attendance System provides comprehensive student management features, allowing administrators to view attendance records, generate reports, and analyze attendance trends.This data-driven approach facilitates better decision-making processes, such as identifying attendance patterns, addressing absenteeism, and assessing student or participant engagement.By implementing the proposed system, educational institutions, organizations, and event managers can streamline their attendance tracking processes, improve efficiency, and enhance overall productivity.The automation and ease-of-use offered by the mobile application empower users to focus on their core activities, rather than administrative tasks.
A Taxonomy for Deep Learning in Dynamic Adaptive Video Streaming Over HTTP

Deep Learning (DL) has become a fundamental technology in the field of Dynamic Adaptive Video Streaming over HTTP (DASH), enabling significant advancements in video streaming systems. This taxonomy presents a novel framework for categorizing and organizing the diverse applications and methodologies of DL in DASH. The taxonomy encompasses various aspects of DL, including video representation, quality of experience (QoE) estimation, bitrate adaptation, buffer management, content- and context-aware adaptation, and network optimization. By providing a comprehensive overview of DL in DASH, this taxonomy serves as a valuable resource for researchers and practitioners, facilitating a better understanding of the different DL techniques and their applications in enhancing video streaming performance and user experience.
Research on the visual design of Sichuan dialect emoticons

The regional features contained in dialects cannot be compared to those of official languages, although dialects are viewed as less elegant. The younger generation perceives dialect as more "native." Moreover, Putonghua, the official language of China, has gained popularity in recent years. How to preserve and advance regional dialects has become a study-worthy issue. This study examines the visual design of fusion emoticons in the Sichuan dialect. The initial objective is to examine how the Sichuan dialect implements emoticons in graphic design. The second objective is to investigate the cross-border incorporation and growth of the Sichuan dialect emoticon graphic design. The research methodology combines design-based action research and case analysis. The result is a summary of the emoticon design style that incorporates Sichuan dialect, regional characteristics, and local drama and culture. In the design practice, the researchers blended the Sichuan dialect and the panda, a unique local animal, as the major body of the combined emoticon. In the design elements extracted from history and culture, the characters of the Romance of the Three Kingdoms, which has a long history in Sichuan, were chosen for exploration; in the attempt at local drama, dynamic emoticons were created by combining the image of a Sichuan opera stage performance with Sichuan dialect. This discussion will contribute to the propagation of Sichuan dialect-based regional culture. From the perspective of cultural development and economic benefits, this paper examines the opportunities presented by the cross-border and deep integration of Sichuan dialects and emoticons in order to promote people's understanding of Sichuan dialects from multiple perspectives, provide a model for the design of the integration of idioms and emoticons, and broaden people's options for daily use
Forecasting Banknote Circulation During the COVID-19 Pandemic Using Structural Time Series Models

As part of the Eurosystem’s annual banknote production planning, the national central banks draw up forecasts estimating the volumes of national-issued banknotes in circulation for the three years ahead. As at the end of 2021, more than 80 per cent of euro banknotes in circulation (cumulated net issuance) had been issued by the national central banks of France, Germany, Italy and Spain (‘4 NCBs’). To date, the 4 NCBs have been using ARIMAX models to forecast the banknotes issued nationally in circulation by denomination (‘benchmark models’). This paper presents the structural time series models developed by the 4 NCBs as an additional forecasting tool. The forecast accuracy measures used in this study show that the structural time series models outperform the benchmark models currently in use at each of the 4 NCBs for most of the denominations. However, it should be borne in mind that the statistical informative value of this comparison is limited by the fact the projection period is only twelve months.
Application of Decision Tree Algorithm in Educational Data Mining

Decision tree algorithms in Educational Data Mining (EDM) emerges as a powerful method for student classification according to their learning station.This article emphasizes the necessity to classify students due to the changing scenario of China's higher education system, which has shifted from elitist to popularization, and lead to university students' differences in learning abilities.For the specialty characteristics of educational data, this study adopts decision tree algorithm based on ID3 to classify students.This article presents an analysis of the application of decision tree algorithm in the course "Hydraulic and Pneumatic Technology Course Design," which is a mandatory course for Mechanical Design Manufacture and Automation Major.This study identifies five splitting features that affect the students' ability to achieve success in this practical course, and build the decision tree model.The learning samples for this algorithm mode are collected from Bachelor students of the University of Shanghai for Science and Technology (USST).According to the categorization results teachers make informed decisions based on the insights provided by the algorithm to improve the learning experience and academic performance of students.Similarly categorization results also provide personalized guidance for students, which is beneficial in ensuring their success and ultimately improving overall educational outcomes.