Reviewerly/exHarmony huggingface dataset:
Computational Storage for an Energy-Efficient Deep Neural Network Training System

Near-storage data processing and computational storage have recently received considerable attention from the industry as energy- and cost-efficient ways to improve system performance. This paper introduces a computational-storage solution to enhance the performance and energy efficiency of an AI training system, especially for training a deep learning model with large datasets or high-dimensional data. Our system leverages dimensionality reduction effectively by offloading its operations to computational storage in a systematic manner. Our experiment results show that it can reduce the training time of a deep learning model by over 40.3%, while lowering energy consumption by 38.2%.
An encoder-decoder based generation model for online handwritten mathematical expressions

据被用于训练 Transformer-TAP(track， attend， and parse)、 TAP 和 DenseTAP-TD(DenseNet TAP with tree decoder)模
A method of radical form and hierarchical structure based handwritten Chinese character error correction

Abstract： Objective Handwritten Chinese character error correction (HCCEC) is developed to handle the complex hierarchical structure， multiple writing styles， and large-scale character vocabulary of Chinese characters recently.The HCCEC is focused on two aspects for assessment and correction.The assessment can be used to determine whether a given handwritten isolated character is correct or not.The correction can be used to locate and correct specific character-misspelled errors.However， HCCEC has its unique chateristics beyond handwritten Chinese character recognition (HCCR) on three aspects as mentioned below： first， such categories of misspelled characters are endless to deal with more inquality Chinese characters， which puts a high demand on the generalization ability of the model.We assume that the training samples are right characters， in which both right characters and misspelled ones are involved in test set.The transfer learning ability of the model is still challenged to handle unclear misspelled characters.Therefore， HCCEC is melted into a generalized zeroshot learning (GZSL) problem further.Compared to zero-shot learning， GZSL-related test set contains seen and unseen 中图法分类号： TP391.
Appendix B: Codes

The codes that reproduce the results in this book are available as Electronic Supplementary Material (ESM). In the first section of this appendix, we describe the list of codes in the ESM used to reproduce the results of each chapter, written both in Fortran 90 and C++.
A Geometric Structure Based Non Local Mean Image Denoising Algorithm

In order to improve the denoising effect of common image denoising algorithms such as non-local mean image denoising, this study improved the similarity calculation formula of image blocks in the algorithm, incorporated the evaluation calculation steps of image block direction similarity and geometric information similarity, and neural network structure, and formed an improved non local mean image denoising algorithm. To test the image denoising performance of the improved algorithm, an experiment was designed and conducted in this study. The experimental data shows that the improved non local mean image denoising algorithm outperforms other comparative algorithms in terms of information entropy, peak signal-to-noise ratio, and average gradient, which represent the denoising effect. When the noise variance is 25, the normalized information entropy, peak signal-to-noise ratio, and average gradient values of the improved algorithm are 0.58, 0.62, and 0.43, respectively. The improved nonlocal mean image denoising algorithm also outperforms other algorithms in the evaluation index of structure similarity, mutual information amount, root mean square deviation, and absolute error over the original information retention. When the noise variance is 45, the normalized structure similarity, Mutual information amount, root mean square deviation, and absolute error of the algorithm are 0.88, 0.78, 8.2, and 9.7, respectively. However, the computational efficiency of the improved non local mean image denoising algorithm is slightly slower than that ofthe traditional non local mean image denoising algorithms. It can be seen that the improved algorithm designed in this study has a superior denoising performance compared to common methods, and can retain the core information of the original image. However, its computational efficiency is slow, and it can provide better image denoising services for applications with low computational efficiency requirements, such as meteorological prediction, agricultural planting, and other fields.
The Effect of Social Media on Students’ Motivation in Reading Printed Materials

There is tendency that today's students are less interested in reading books and more likely to rely on other forms of passive media (movies, TV shows, Internet, etc.) for their information needs. The purpose of this research is to investigate whether or not students' increasing use of digital technologies, particularly social media, has affected their motivation for reading printed texts. A total of 80 students from three schools in Bone regency are surveyed for this study. The data demonstrates that students in senior high school in Bone regency spend more time-consuming digital media than they do reading traditional print materials. As opposed to reading a book, they enjoy using technology or interacting on social media. Therefore, the purpose of this research is to highlight the students’ motivation of reading printed materials in senior high schools in Bone regency.
Perancangan Film Animasi 3 Dimensi Berjudul “Jangan Menyerah” dengan Metode Pose to Pose

3D animation film is a development of 2D animation which is developed through computer technology which is growing rapidly. However, in using computer technology to design a 3D animated film that is still very rigid, it can affect the performance of the animation because the animation does not look real like humans and objects. The purpose of this study is to explain the design of animating an animated film using the method that has been made, namely pose to pose for each pose used based on the animation design that has been made for the film entitled "Jangan Menyerah". Method: This study uses the Multimedia Development Life Cycle (MDLC) method with six stages, namely concept, design, material collection, manufacture, testing, and finally distribution. The concept of the title of the film is not to give up on moral and short film formats. The characters used use the pose to pose method with keypose movements, extreme movements and in between movements. After that, testing and distribution is the final process in making works that are published through social networks. Conclusion Making an animated film designing a 3-dimensional animated film entitled "don't give up" using the pose to pose method, the journey of a child who wants to achieve his goals, can be achieved by following the multimedia development life cycle stage with the last process being distribution on SNS such as YouTube and Instagram
Retraction Notice: The Application of Virtual Metacognitive Network Model in Preschool Guiding Art Network Teaching

The rapid development of the Internet age has led to the reform of the education industry and has given birth to the rapid development of the blended teaching model. By analyzing the problems existing in the teaching process of preschool education major art disciplines in the blended teaching mode, the author starts from the concept of metacognition and the characteristics of online teaching, and analyzes the new connotation of metacognition in the online teaching environment, and here On the basis of this, a three-dimensional meta-cognition training structure based on technology platform, teachers and learners in the network environment is proposed, trying to provide a comprehensive perspective for the meta-cognitive training of learners, combined with the blended teaching model in the preschool education professional art discipline. Based on the advantages of application, the implementation suggestions of blended teaching are put forward in a targeted manner, in order to provide reference for higher vocational colleges to adopt blended teaching mode for teaching.
Retraction Notice: Java Implementation of Business Management System and Information Analysis Platform for Economic Innovation Projects

This article studies the JAVA implementation of business management system and information analysis platform for economic innovation projects. This article discusses the analysis, design and implementation of the software. In terms of demand analysis, the specific functions of industrial project planning, project implementation and effect evaluation were discussed, and corresponding use case models were established. The paper describes the overall software architecture of the Suqian Industrial Economic Innovation Project Management System. Under this framework, the main components of the software and the data structure of the main objects inside are described, and the corresponding class diagrams and processing are given.
Sign Language Recognition and Conversion to Audio using Image Processing and Machine Learning

Sign language is one of the oldest and most natural forms of language for communication. Sign language is used by speech-impaired persons for communication. For a regular individual, sign language interpretation is exceedingly challenging. This project effort proposes a real-time method for finger spelling-based American sign language using neural networks. The hand is first put through a Gaussian filter in this manner to get rid of the noise. A classifier is used to predict the class of the hand gestures from the filter’s output. It displays the textformatted output for a specific sign from that class. Python’s pyttsx3 module is used to convert text to speech. Key Words: American sign language, neural networks,Gaussian filter,pyttsx3
Exploring the Role of Libraries in Promoting Child Mental Health in Bangladesh: A Study on Some Selected Libraries

Aims: The research aimed to figure out the existing roles of libraries in promoting child mental health. The study also focused on the child's mental health importance, the warning sign of children, common disorders and promotional strategies for child mental health.&#x0D; Methodology: To figure out these roles’ the researcher adopted qualitative research. For this study, the researcher collected data by a scheduled interview, the respondents were chosen mostly from the public libraries and one from Shishu academy library. In line, the text analysis technique was used to identify the different types of roles.&#x0D; Results: The study's key findings are presented from three perspectives: firstly, the socio-economic determinants of child mental health. Secondly, libraries' role as services providers, as bibliotherapists and as motivators which affects children’s mental health, and develops their thinking capability. Finally, libraries can flourish in their role by adequate books, health equipment, and by collaborating with the school library to organize workshop.&#x0D; Conclusion: The study's conclusion is to draw the attention of the important stakeholders to the significance of children's mental health and the librarian's role in encouraging it through specialized read-aloud programs, helpful resources, and a welcoming library environment. Stakeholders should take proactive measures to promote and fund these programs by acknowledging the important effects that early exposure to literature and positive reading experiences can have a great impact on a child's mental well-being. This will help to establish a nurturing and enriching environment.
Time Series Regression Modeling with AR(1) Errors

When ordinary regression analysis is performed using time-series variables, it is common for the errors (residuals) to have a time-series structure. This violates the usual assumption of independent errors in ordinary least squares (OLS) regressions. Consequently, the estimates of the coefficients and their standard errors are incorrect if the time-series structure of the errors is ignored. In this study, an investigation of a regression model with time-series variables, particularly a simple case, was conducted using the conventional method. The ‘AirPassengers Dataset’ was downloaded from the R repository used for the analysis. Ordinary least squares and Cochrane-Orcutt procedures were used as methodologies. The results show that the adjusted regression model with autoregressive errors outperformed the ordinary regression model.
Application of ASSURE Model in International Chinese Language Smart Education

The aim of international Chinese language education is to realize digitalization, apply smart technologies, and become ubiquitous by 2025. Artificial intelligence and digital resources are the two engines required to achieve this development goal. Based on the principles of international Chinese smart education, this paper combines L2 Chinese teaching and learning rules with the ASSURE model and applies the six application steps of that model to enable L2 Chinese smart teaching by using artificial intelligence and digital resources: (1) Analyze Learners, (2) State Objectives, (3) Select Methods, Media and Materials, (4) Utilize Media and Materials, (5) Require Learner Participation, (6) Evaluate and Revise. The ASSURE model provides a full and clear a procedural guide for digitized L2 Chinese instructional design. The purpose of this article is to help L2 Chinese teachers at all levels incorporate artificial intelligence and digital resources into their repertoire, use them as teaching tools, and subsequently guide L2 Chinese learners in using them as learning tools.
Impact of TLS Overhead on Segmented Network for Cloud Database Systems

Cloud database serves flexible, affordable, and scalable database system. Even the cloud database is secure with Transport Layer Security (TLS), but the performance overhead that TLS introduces when executing operations on one of the major No SQL databases: Mongo DB in terms of latency. To explore TLS performance overhead for Mongo DB, we performed two tests simulating common database usage patterns. We first investigated connection pooling, where an application uses a single connection for many database operations. Then, we considered one request per connection in which an application opens a connection, executes a process, and immediately closes the connection after completing the operation. Our experimental result shows that applications that cannot endure significant performance overhead should be deployed within a properly segmented network, rather than enabling TLS. Applications using TLS should use a connection pool rather than a connection-per-request.
Perancangan Sistem Pakar Gejala Penyakit Pada Burung dan Kucing Menggunakan Metode Forward Chaining Berbasis Android

This study discusses the problems experienced by pet owners, especially cats and birds in dealing with diseases experienced by their pets. Based on the results of the questionnaires that have been conducted, as many as 74.3% of pet owners have minimal knowledge about what types of diseases can attack their pets (cats and birds), 80% of the distance traveled to the vet clinic is quite far, 60% of the existing veterinarians are still relatively minimal, and pet owners have their own busy lives and activities. This study aims to create an expert system in the form of an android-based application, which can assist pet owners in obtaining information about the results of disease symptoms in pets, what kind of treatment can be given, drug suggestions for the disease. The method for tracing the solution used is the Forward Chainig method, where the analysis is carried out after the information is given to then produce conclusions in the form of the name of the disease. The kar system application is made using the Java and PHP programming languages. The expert system application created in this study can help pet owners, especially cats and birds, in getting information about the symptoms of diseases that attack their pets. This is based on the results of testing the beta test results of all the features in the application running well and a success rate of 100%, and based on the results of the effectiveness test of the acceptance of this expert system application, it is very good at 89.35%.
PENGARUH MODEL PROBLEM BASED LEARNING BERBANTUAN LIVE WORKSHEET TERHADAP MOTIVASI DAN HASIL BELAJAR IPA PESERTA DIDIK KELAS 4 SEKOLAH DASAR

The learning process at SD Negeri 1 Wana is included in the low criteria where the motivation and learning outcomes of students are still below the KKM. Science lessons are lessons that are indispensable in students' lives so that in learning it requires seriousness and a good sense of enthusiasm. The success of science learning in grade 4 of Wana Elementary School needs to be marked by good learning outcomes. Thus, it is necessary to make efforts to increase students' motivation and learning outcomes by using appropriate learning models and tools. quantitative approach and includes Quasi Experiment research as well as in this study using test questions and questionnaires.&#x0D; Based on the results of the study, there was an effect of the PBL learning model assisted by the LKPD live worksheet on motivation in the very good category. Based on the research results, the PBL learning model assisted by the LKPD Live Worksheet is feasible to use to improve student learning outcomes.
Evaluation of Business Model and the Supplier Selection of XYZ Company Using AHP Method

This study aims to see PT XYZ problem in the big picture, the company's business model is analyzed using Business Model Canvas, and the supplier selection is calculated using Analytical Hierarchy Process (AHP) method.The criteria considered by the managers when picking the right supplier are quality (71.68%), delivery system (20.9%), and procurement system (7.4%).The result proposed the best suppliers for duck meat according to the preferences are supplier 2 (52%), supplier 3 (21.9%),supplier 1 (13.5%), and random selection (12.6%).The result proposed the best suppliers for boneless chicken according to the preferences are supplier 1 (51.3%),supplier 3 (19.6%),supplier 2 (18.7%), and random selection (10.4%).The result proposed the best suppliers for mineral water according to the preferences are supplier 3 (36.4%),supplier 1 (23.9%),supplier 2 (23.5%), and random selection (16.4%).Compared to the previous practice of selecting suppliers based solely on an "approximate" system, AHP questionnaires have proven that managers consider selecting one particular supplier better than random selection, as evidenced by the value of random supplier selection having the lowest weight in all three categories of materials which are calculated using AHP.
Real Time Video Summarization Using Anchor Free Model for Capturing Events

Video summarizations can quickly and effectively collect the necessary data from huge surveillance datasets by identifying key video data in the surveillance stream.In this work, we utilize anchor free method to summarize the video of a surveillance camera.The anchor-free approach ignores temporal proposals that are pre-defined.It also predicts important values and segment locations directly.The localization of an action using temporal points is aided by an anchor-free action localization module.This module defines an action instance as a point with lengths to the starting and ending boundaries, reducing pre-defined anchor limitations on action localization and duration.For realtime working, this work created video shots and passed each shot to our model to generate a summary of the video.The evaluation is done on this model, on custom dataset, which is specially created for our application
Natural Language Processing and Bi-Directional LSTM for Sentiment Analysis

In e-commerce, one of the most critical and important aspects of the business model is customer reviews.Customer reviews reflect the satisfaction of customers with respect to the products and services offered.E-commerce is driven by significant amounts of data which poses a huge challenge of collection and evaluation to have an insight before decision-making and business strategy implementations.The field of natural language processing and machine learning techniques have provided significant leaps in helping the analysis of big data and business analytics.Also, Recurrent Neural Networks (RNN) evolved in so many powerful algorithms and one of those is the Bi-LSTM variation of RNNs.Bi-LSTM has been identified in the literature as a suitable machine learning classification algorithm for natural language processing due to its sequential learning process.This study is an implementation of the lemmatization natural language processing technique coupled with the Bi-LSTM machine learning classification technique for customer review sentiment analysis.The application of these two techniques has reported a significant performance accuracy in sentiment analysis of customer review data.The results in this study are reported as 96.06%, 91%, and 90% for accuracy, precision, and recall respectively.
Using Knowledge Graphs and LLMs to Enhance Natural Language Understanding on Voice Assistants

Both, Large Language Models (LLMs) and Knowledge Graphs (KGs) are used in various Natural Language Understanding (NLU) tasks.However, each has some benefits and disadvantages.This paper explores the pros and cons of each, and demonstrates how the two used together can help overcome some of the shortcomings.It also identifies specific applications of KG-enhanced LLMs for music-related user experiences on voice assistants.Finally, it enlists the challenges in KG-enhanced LLM applications.
Simulated mental imagery for robotic task planning

Traditional AI-planning methods for task planning in robotics require a symbolically encoded domain description. While powerful in well-defined scenarios, as well as human-interpretable, setting this up requires a substantial effort. Different from this, most everyday planning tasks are solved by humans intuitively, using mental imagery of the different planning steps. Here, we suggest that the same approach can be used for robots too, in cases which require only limited execution accuracy. In the current study, we propose a novel sub-symbolic method called Simulated Mental Imagery for Planning (SiMIP), which consists of perception, simulated action, success checking, and re-planning performed on 'imagined' images. We show that it is possible to implement mental imagery-based planning in an algorithmically sound way by combining regular convolutional neural networks and generative adversarial networks. With this method, the robot acquires the capability to use the initially existing scene to generate action plans without symbolic domain descriptions, while at the same time, plans remain human-interpretable, different from deep reinforcement learning, which is an alternative sub-symbolic approach. We create a data set from real scenes for a packing problem of having to correctly place different objects into different target slots. This way efficiency and success rate of this algorithm could be quantified.