Reviewerly/exHarmony huggingface dataset:
Currency Recognition Based on Deep Learning

Currency recognition is a critical task with diverse applications. Recent advancements in deep learning and neural networks have shown significance in improving its accuracy and robustness. This paper presents an approach that utilizes the convolutional neural network (CNN) for accurate and reliable currency recognition. The paper begins with an introduction to the background of the neural network, including its architecture and types, focusing on CNN and its significance in image-processing tasks. Then, the methodology section describes the data collection process, dataset preparation, and the construction of the CNN model. Next, the results and discussion section present the performance of the developed model, discusses the accuracy of the model, and figure out the model’s strengths and areas for improvement. Last, the conclusion summarizes the research findings and evaluates the effectiveness of the proposed neural network-based approach, while also suggesting directions for future research. Overall, the study demonstrates the potential of CNNs in currency recognition and provides insights for further development in this field.
Automatic Speech Recognition in Indonesian Using the Transformer Model

Automatic Speech Recognition (ASR) is useful for converting speech into text. ASR is needed to display automatic subtitles on movies or when conducting video conferencing. The use of deep learning in ASR applications is currently still dominated by Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN). Several previous studies used the Transformer deep learning model in building the ASR model. The accuracy results obtained are better than the LSTM or RNN models. However, ASR research using the Transformer model is still very limited. This paper will discuss the use of the Transformer model for ASR in Indonesian. The dataset used is the Indonesian language private speech dataset. The experimental results show that the Word Error Rate (WER) and Character Error Rate (CER) produced by the proposed model using the Indonesian language primary dataset are almost the same as using the English language public dataset where the resulting WER and CER are 27.34 and 7.96 for Indonesian and 25.28 and 6.02 for English.
Challenges on Cloud Computing Migration Strategy for Music Industry: A Systematic Literature Review

Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies.
Enhanced Few-Shot Learning for Indonesian Sign Language with Prototypical Networks Approach

The issue of few-shot learning (FSL), which requires using a small amount of training data to categorize instances from classes that have never been encountered before. We concentrate on categorizing images in Indonesian Sign Language using a few-shot image classification method utilizing Prototypical Networks. To accomplish this, we used a neural network to apply a non-linear transformation to the input data, with each class being represented by the means of its support set within the embedding space. The theory of few-shot learning serves as the foundation for the paradigm used in this investigation. According to our experimental results, the Shufflenet_V2 convolutional network model for Prototypical Networks produced the best accuracy, reaching 96.75%.
Research on Business Model of Organizational Decision Making Supported by Integrated Computer Digital Technology

There are many methods for automatic model selection in an object-oriented Intelligent Decision Support System (IDSS) model base management system. A decision support system (DSS) is a human-computer interactive system that uses data, information, and knowledge, especially model technology, to assist decision-makers at all levels in solving structured and semi-structured decision problems. The system adopts the overall architecture of the model, method library, database and session control system. This paper proposes constructing a model and method base using object-oriented technology and manages the model base concerning database management mode. The text features are decomposed into the target and initial state elements of the problem through the human-computer interaction interface. Then the intelligent recognizer is used to reason and select the model. Automatic model selection based on natural language understanding is realized by adding a problem identification module. The definition method of the model and the construction idea of the method library proposed in this paper is also helpful in designing other decision support systems.
Research on Computer Artificial Intelligence-assisted College Public English Teaching System

This paper firstly studies the objective and application prospect of the independent English teaching system under the network environment. The basic functional framework of autonomous English teaching system is put forward. The software has user management module, teaching auxiliary project module, characteristic project module, grade examination module, exchange platform module, online self-test module. Finally, under the guidance of theoretical knowledge, this paper completes the test of this system. Research shows that the system improves the efficiency of English teaching.
The Application of Computer Network Assisted Instruction Technology in College English Innovation Practice Class

In this paper, an accurate mathematical model of English teaching topic difficulty is established. Then the maximum likelihood method is used to adjust the difficulty coefficient automatically. The adjustment method of difficulty value is described by ASP Script language based on WEB. Finally, this paper uses the multimedia platform FLASHMX and Author ware to construct the multimedia computer English teaching software. The software can realize the main functions of text learning, interactive class and fast reading. The system judges the learning level of students by intelligent diagnosis mechanism and provides favorable basis for teachers to adjust the teaching content. It is found that this system has good application prospect and is worth popularizing.
Transforming Sign Language using CNN Approach based on BISINDO Dataset

The rapid growth of technology has a significant role in developing technologies that can support communication for hearing or speech impaired individuals that enables a real-time translation into visual or written language visually. These rapid growth and advancement create an opportunity to connect the gap between impaired individuals and others for the sake of fostering the society. Sign language is commonly used by hearing or speech impaired people to communicate with others. BISINDO is Indonesian Sign Language used by deaf individuals in Indonesia. Based on BISINDO proposed a convolutional neural network model to detect and recognize sign language to accommodate a communication between impaired people. CNN is a deep leaning algorithm designed specifically to handle processing and analyzing visual data in terms of images and video. It consists of various convolutional layers that extract and analyze local features and patterns to allow object recognition and image classification. In addition to that CNN is the best possible and ideal algorithm due to their capabilities of capturing information, learning patterns and extraction with variety of hand gestures while achieving them in real time. Hence CNN is used in our model, Since the main objective of this paper is to create the most accurate and efficient model from an existing dataset. We obtained the results by using CNN, by gathering necessary data, data processing, model training and testing. Based on CNN we are able to achieve an accuracy of 82.56%.
A Framework for Human Action Recognition Using Control Features Fusion and Weighted Entropy Based Feature Selection

Human behaviour research, human-computer interface, and unavoidable processing all now heavily examine human activity recognition (HAR). Recently, deep learning (DL)-based algorithms have been successfully used to predict various human movements using time-series data from cell phones and wearable sensors. Although DL-based techniques fared exceptionally well at activity detection, they still have trouble handling time series data. Time-series data still has a few problems, such as difficulty in feature extraction, highly unbalanced data, etc. Additionally, the majority of HAR techniques include manual feature orchestration. Bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) are combined in this article. The proposed multibranch CNN-BiLSTM network performs redid feature extraction from the unprocessed sensor input. The model is prepared for learning area characteristics as well as long-term conditions in successive data thanks to the use of CNN and BiLSTM. In this study, we will fine-tune the weight of Convolutional Neural Network (CNN) using a novel self-improved Seagull optimisation algorithm (SI-SOA) technique in order to increase the human activity categorization accuracy. The proposed SI-SOA approach will be conceptual amalgamation of the standard Seagull optimization algorithm (SOA).
LPIXray: A Large-scale Logistics Prohibited Item X-ray Dataset for the Application of Deep Learning in Security Inspection

Detecting prohibited items in X-ray images is very important for logistics public safety. For object detection, deep learning with the support of a significant volume of data has enormous advantages and has gradually become a mainstream research method. However, there are relatively few public datasets on prohibited item X-ray images, and the existing datasets have the problem of fewer prohibited items in quantity and variety. To address the above challenges, this work contributes LPIXray dataset: a Logistics Prohibited Item X-ray dataset. The LPIXray contains 60950 X-ray images of prohibited items, 84785 instances, and is carefully divided into 6 classes with 18 subclasses. These X-ray images cover almost all common prohibited item categories, and each category has a wealth of data annotation. The LPIXray dataset is 6 or even 20 times larger than previous datasets, far exceeding previous data in terms of annotation quantity and category definition. Aiming at the problem that some X-ray images of prohibited item are difficult to collect and the samples are unbalanced, a data Augmentation approach based on the General Adversarial Network (GAN) and image fusion is designed. Finally, we conduct experimental verification on the LPIXray dataset based on You Only Look Once (YOLO) model.
Sign Language Detection Using Mediapipe and Long-Short Term Memory Network

Although humans generally use verbal communication methods to interact with each other, some have limitations to the use of this technique due to hearing or speaking impairment. This category of people is known as the deaf, and they use sign language to communicate. BISINDO is one of the sign languages used in Indonesia, but due to its unpopularity, communication between the deaf and ordinary people becomes difficult. Therefore, this study developed a deep learning model to identify sign language movements using the Long Short-Term Memory Network architecture. The model was designed in three scenarios produced with the best performance identified in the first scenario and analyzed with several tests. The result showed that the first model had training and validation accuracies of 98.352% and 97.273%. In testing using the data-test, the model has an accuracy of 99% and a ROC-AUC score of 99.995%. Finally, in the actual data testing, the model has an accuracy of 89% and a ROC-AUC Score of 98.390%.
Research on Computer Aided Digital Design and 3D Printing Technology

This paper proposes an optimal design scheme for the motor control system of FDM3D printers based on computer digital technology. Taking the Cortex-M4 microcontroller as the main research object, a computer simulation system based on RT-Thread is established. Combining S-type acceleration with Bresenham's linear interpolation method for stepping motor control. The corresponding program modules are designed. The content includes data storage, human-computer interaction, serial communication and debugging, and control of stepper motors. Through the experiment on the action commands issued by the mobile phone, it was found that the 3D printer can respond quickly and correctly. The system has the characteristics of high efficiency, precision and real-time.
Investigating T5 Generation Neural Machine Translation Performance on English to German

This paper investigates the translation performance of the Text-to-Text Transfer Transformer (T5) model on English-to-German machine translation. The study aims to compare the performance of different generation methods, evaluate the quality of the dataset and tokenizer format, and explore the advantages and limitations of the T5 model. The evaluation metrics used are BLEU and BERTScore, which provide different scoring algorithms. The results show that the Greedy Inference method achieves the highest scores in both metrics, indicating its effectiveness in this task. The study contributes insights into the T5 model's performance for English-to-German translation and highlights the need for further improvements in lexical context and translation accuracy. Our experiments showed that the Greedy Inference method consistently achieved the highest scores in both BLEU and BERTScore metrics, indicating its effectiveness in generating translations for this task.
Comparison and Evaluation of Clustering Algorithms

This paper compared the effects of different clustering models and found some ways to evaluate the cluster models. Clearly, the Gaussian mixture model (GMM) and hierarchical clustering model (HAC) were studied. To determine the number of clusters, the Silhouette Coefficients line graph was plotted in model GMM, where the best number of cluster groups was found to be 4. However, the clustering effect is poor, as the scatter diagram shows. Next, a hierarchical clustering model was built, which has a better cluster effect. It is difficult to determine the clustering number in HAC, so the number of clusters in GMM is applied to HAC. It can be seen from the scatter plot that the clustering effect for model HAC is better. In summary, this essay combined the advantages of GMM and HAC, which makes it easier to determine the number of clusters. In addition, this essay used a way to evaluate the clustering models: draw the clusters after dimensionality reduction (PCA) in a 2D scatter plot.
Empirical Evidence of Blockchain Technology Adoption for Indonesian Unbanked Farmers’ Financial Inclusivity

The adoption of Blockchain technology is seen as a potential solution to solve financial inclusion issues in the world. However, current research on blockchain technology to solve financial inclusion has not yet highlighted the importance of this technology. Most of the existing research remains primarily grounded in conceptual frameworks. This study uses a qualitative methodology to examine the perspectives and insights of different stakeholders on empirical evidence of blockchain adoption in addressing financial inclusion challenges faced by unbanked farmers in certain demographic regions of Indonesia. The interviews were specifically designed to address how and why blockchain technology may be utilized by banking institutions to gather and authenticate data pertaining to farmers. This discourse examines the vulnerabilities inherent to current banking systems. The interviewees involved the unbanked farmers, subject matter experts (SMEs) in blockchain technology who also have experienced the implementation of blockchain technology in a rural farmer, an SME in the finance industry, and has implemented blockchain technology.
Sentiment Analysis on Tokopedia Product Reviews using Natural Language Processing

E-commerce is one of the shopping places that is currently widely accessed and used, especially in Indonesia. It acts as an online marketplace, allowing users to purchase or sell products and provide reviews for the purchased products. Those product reviews are crucial in determining the product quality and the user’s overall satisfaction with the shop. The reviews of a product can determine whether a buyer will continue to purchase or abandon it. Sentiment analysis can be implemented to determine whether product reviews are positive or negative. Then, online store owners can use the results to evaluate their services. Sentiment analysis can be done automatically using artificial intelligence models, i.e., machine learning or deep learning. This research aims to get the best machine learning and deep learning models to perform sentiment analysis on Indonesian language product review datasets by combining models and the resulting performance comparisons. The authors proposed several natural language processing (NLP) models using TF- IDF and Word2Vec with logistic regression and support vector classification (SVC) classifiers. This research also implemented IndoBERT’s pre-trained model to check its performance on sentiment analysis. Performance indicators used in this research are accuracy, precision, recall, and F1-Score. While the machine learning model is behind the deep learning model in accuracy, recall, and F1-Score. It has the highest precision performance.
How Augmented Reality Works: Technical Issues, User Issues, Content Development, and Curriculum Adaptations as Challenges Through Bibliometric Analysis

Augmented reality has been effectively used in the domain of education. Nevertheless, the development of technology is not without its problems, particularly when it comes to aligning it with existing demands and prevailing trends. The primary objective of this research endeavor is to provide a comprehensive account and identify pertinent concerns pertaining to the challenges encountered in the use of augmented reality (AR), particularly within the realm of education. A bibliometric study was performed using analytical tools available on the Scopus.com website, and the Vos viewer application was used for the viewing of bibliometric data. The study's results indicate that there are many concerns that stakeholders should take into consideration while using augmented reality in educational settings. There are four distinct hurdles in the adaptation of Augmented Reality (AR) technology to meet the demands of learning environments. These challenges include technical issues, user-related concerns, the production of AR content, and the adaptation of curricula to effectively incorporate AR into educational settings.
EU-BERT: Efficient Unified Named Entity Recognition via Multi-exit BERT

Named entity recognition (NER) is an important Natural Language Processing (NLP) task with wide application, such as document analysis, knowledge graph and query understanding. Although pretrain-based language models such as BERT has achieved great results on NER task, the large amount of parameters of BERT makes it slow during inference, which limits its usage in industry. Experiments show that the metric of traditional early exiting has great defects. In this work, we propose EU-BERT, an Efficient Unified named entity recognition framework via multi-exit BERT, to accelerate BERT inference on NER task. To handle nested and discontinuous NER tasks, we adopt W2NER [1], a unified NER framework using table filling technique. EU-BERT proposes a better metric for early exiting and uses contrastive learning to enhance its ability. Experiments on 9 benchmark NER datasets demonstrate that our method can improve the performance of multi-exit BERT on NER task while maintaining its inference speed.
The Construction of Mongolian Lexical Semantic Net about Horse Culture and Economy

Horse culture is an important part of the Mongolian nomadic culture. Although there are many current related works about horse culture, not many resources can be queried and applied directly through the network. In the new economic development era, it is an important way to promote and protect the traditional ethnic culture combining the advantages of information technology, using computer technology. This article introduces one of the important contents of Mongolian nomadic culture— —the importance of collecting horse culture-related terms and how to construct the lexical semantic database by using information technology. This paper mainly applies semantic field theory of modern semantics to carry on semantic category storage of the related terms in horse culture and then takes advantage of different lexical semantic relationships to query and learn about related terms of horse, thus providing lexical semantic resources for the relevant personnel who want to learn more about horse culture.
Detection of Abnormality in Cognitive Test Scores Using Metric Learning

We investigate deep learning neural networks to detect anomalies in cognitive test scores that are generated by either a trained human coder or a classification program based on the input of clock drawing images. We have developed a system, SN_AD (Siamese Networks for Abnormality Detection) in ordinal classification. SN_AD consists of three Siamese networks, one models class k, and the other two model the two neighboring classes, class k-1 and k+1, and an anomaly detection algorithm, OCAD (Ordinal Class Anomaly Detection). The Siamese networks were trained using metric learning and used to generate class distance distributions. The OCAD algorithm calculates the distances of each test image x to the distributions of class k, k-1, and k+1 using the Siamese networks and detects class score anomalies based on statistical analysis of x being out-of or indistributions of class k and its two neighboring class k-1 and k+1. Experiments were conducted on two sets of images collected through Clock Drawing Test (CDT) in the National Health and Aging Trends Study (NHATS), which is commonly used to detect cognitive impairment. The experimental results show that the proposed system, SN_AD is effective in metric learning of class-specific distributions, detecting coding inconsistencies, and correcting coding errors.