Reviewerly/exHarmony huggingface dataset:
Development and Application of Mechanical Design Engineering Database Based on Simulated Annealing Algorithm

In the process of mechanical engineering design, it is necessary to involve multiple links such as design, analysis, simulation, experiment, etc. There will be a large amount of data and information exchange between each link. The use of database technology can effectively improve the quality of data management, reduce data redundancy, and improve data sharing. Mechanical design engineering data is the basic and core data in the entire mechanical product design process. These engineering data have the characteristics of various types, complex relational structures, dynamic modification of patterns, and large amount of data. Traditional data models cannot fully meet the needs of engineering data description and management. In order to meet the needs of practical applications, people have proposed a variety of data models to meet the needs of different fields. These data models either extend the traditional relational model, or adopt the object-oriented model and other kinds of special databases, which show great power in the field of engineering application. The method used in this paper is the simulated annealing algorithm. Compared with the conventional optimization method, the simulated annealing algorithm has good convergence and strong adaptability, and is a good method to realize reactive power optimization.
Analysis of Text Emotional Tendency and Prediction of Internet Public Opinion Trend Based on Support Vector Machine Algorithm

Due to the participation of people in online public opinion, users have their own preferences and ideas, which leads to strong chaos in online public opinion. Currently, ML (machine learning) algorithms ignore the chaotic characteristics of online public opinion, so the established model can't fully and accurately describe the changes of online public opinion, and the prediction accuracy needs to be further improved. In this paper, the SVM (support vector machine) algorithm is used to analyze the sentiment tendency of text and predict the trend of Internet public opinion, and the pre-model of sentiment tendency identification and Internet public opinion is established. On the basis of the traditional Gaussian kernel function, we add parameters to increase the displacement change and amplitude adjustment of the kernel function, so that it can accurately control the performance characteristics of the kernel function. The document's overall emotional tendency can be obtained through this accumulation. The results show that the recognition rate of this method is up to 95.828%, and the prediction error is controlled within the effective range (5.028%). This method overcomes the shortcomings of traditional emotional word weighting methods, such as low performance and inability to adapt to multi-viewpoint topics, and can get better recognition accuracy.
Sequential Learning from Noisy Data: Data-Assimilation Meets the Echo-State Network

This paper explores the problem of training a recurrent neural network from noisy data. While neural network based dynamic predictors perform well with noise-free training data, prediction with noisy inputs during training phase poses a significant challenge. Here a sequential training algorithm is developed for an echo-state network (ESN) by incorporating noisy observations using an ensemble Kalman filter. The resultant Kalman-trained echo-state network (KalT-ESN) outperforms the traditionally trained ESN with least square algorithm while still being computationally cheap. The proposed method is demonstrated on noisy observations from three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.
Enhancing Mobile Security: A Systematic Review of AI and Blockchain Integration Strategies for Effective Analysis

Mobile environments are becoming more and more vulnerable to a plethora of security threats and issues, such as data leakage, unauthorized access, and other malicious activities, posing thereby significant risks to users’ privacy and security. Therefore, robust security mechanisms are essential to cope with vulnerabilities, attacks, and privacy challenges related to mobile environments. However, traditional analysis techniques are not able to deal with increasing malware attacks in such environments, and they are expensive and time-consuming for developers. In addition, the integrity of analysis results is another challenge to take into account during the security analysis process. From these perspectives, the integration of Artificial Intelligence (AI) algorithms and Blockchain (BC) technology is regarded as a promising approach for an automated and efficient solution to enhance security analysis in mobile environments. In this paper, a systematic literature review has been conducted to study the proposed schemes integrating both technologies to tackle security analysis in mobile environments, by identifying the key strengths and limitations of each approach based on various features of the two technologies, with an emphasis on possible countermeasures. The findings of the given study can help the researchers to set the directions for future research in this field.
Car Grant Fraud Prevention using Ethereum Blockchain

Technological advancement brings both opportunities and challenges to several sectors, including transportation. In Malaysia, the car grant system managed by the Road Transport Department (JPJ) is crucial for accurate vehicle registration and ownership but is vulnerable to fraud, like car grant forgery and unauthorized transfers. This study suggests using the Ethereum blockchain to enhance the integrity of the car grant system within the JPJ to curb these fraudulent activities. Ethereum’s decentralized and distributed ledger technology, which offers transparency, immutability, and security, is perfect for this purpose. The suggested system uses smart contracts, self-executing agreements with preset rules, to ensure secure car grant registration and transfers. These contracts execute operations securely, preventing unauthorized changes. A prototype was developed and tested to evaluate the feasibility and efficacy of the system, including transaction speed, security, scalability, and user experience. The results gave valuable insights into the system’s strengths and weaknesses, allowing for further optimization. This secure and transparent method could revolutionize vehicle registration and ownership procedures, enhancing trust, efficiency, and accountability in the Malaysian transportation sector.
Detection of Suspicious Activities at Remote Locations by using UAVs and Computer Vision

In recent times, there have been numerous grievances lodged regarding the illicit appropriation of land by various entities and individuals of significant influence and organizations occupying the land without detection and, upon being discovered, refuse to vacate the premises, claiming ownership and prolonged residency. Given that remote areas are often neglected and difficult to access, there is a pressing need for surveillance in such regions to thwart illegal activities and unauthorized land acquisition. This paper presents a solution in which a drone equipped with a camera was utilized for surveillance. The images captured by the camera were transmitted to a remote computer for analysis using the proposed method, which involves object detection and classification. The program's primary function is to notify the user of any suspicious/trespassing activity at the remote location, enabling prompt action. The strategy produced substantial outcomes.
Web-based Sentiment Analysis System Using SVM and TF-IDF with Statistical Feature

Social media's tendency for instant reactions can be harnessed by companies and organizations to gather feedback. Nevertheless, effectively analyzing vast amounts of social media data poses a challenge. This issue can be addressed through the use of sentiment analysis technology. In this study, a sentiment analysis model is developed, employing Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms. The study aims to investigate the impact of feature engineering on TF-IDF, by incorporating statistical features into the SVM model's sentiment analysis performance. The experimental results reveal that the prediction model utilizing the conventional TFIDF approach achieves an SVM model with an F-measure score of 84.55%. Through the implementation of feature engineering, by adding max, min, and sum features, the model's performance shows a noticeable improvement, with an increase of 0.65% in the F-measure score difference. Consequently, the proposed feature engineering method positively enhances the capability of the SVM-based sentiment analysis model. To facilitate the acquisition of sentiment analysis results through user interfaces, the trained SVM model is integrated into a web-based sentiment analysis application. By doing so, the findings of this study contribute to streamlining the process of obtaining sentiment analysis results from social media data.
Real-Time Customer Emotion Analysis in E-Commerce based on Social Media Data: Insights and Opportunities

In this era of social media, understanding consumer emotions in real-time is crucial for businesses to adapt and enhance their offerings promptly. This study delves into real-time consumer emotion analysis in the e-commerce industry using social media data, propelled by the publicly available GoEmotion dataset sourced from Reddit. This dataset encompasses 58,000 comments meticulously labeled for 28 emotion categories, providing an abundant source for sentiment analysis and emotion classification, especially relevant to the e-commerce sector. Released in 2020, this dataset offers a contemporary view of consumer emotions on social media. Human annotators diligently labeled each comment, resulting in a dataset that accurately mirrors users' emotions across diverse Reddit topics. Advanced machine learning techniques, including convolutional neural networks and Bidirectional Encoder Representations from Transformers (BERT), were employed to categorise the emotions expressed in the comments. The practical result of this research is a machine learning model integrated with a user interface, delivering actionable insights derived from real-time Twitter data to e-commerce companies. This integration allows businesses to tailor their products and services in alignment with consumer sentiments expressed on Twitter. Notably, the model achieved a substantial improvement in macro-average precision (0.54) compared to previous work (0.40). The BERT model demonstrated exceptional proficiency in predicting specific emotions like gratitude, love, and admiration, boasting F1-scores of 0.82, 0.57, and 0.48, respectively. Additionally, the CNN model excelled in predicting “gratitude,” demonstrating impressive precision, recall, and F1-scores. The study aims to identify the most prevalent emotions and empower businesses to customise their products and services accordingly.
Adaptation of a BERT Model to the India Restaurant data using Rule-based Approach for Aspect-based Sentiment Analysis

Aspect-Based Sentiment Analysis (ASBA) concept has been worked extensively as a popular technique in consumer research. The commercial applications though limited are being tested. The research mainly focuses on using the existing ASBA model to be adopted, tested and validated for use in the context of consumer reviews for restaurants in Indian geography. We used the rule-based approach instead of the supervised or unsupervised based machine learning approach to adapt the ABSA model with a hypothesis that the rule-based approach would yield F Square estimates equivalent to supervise or unsupervised learning approach.Our research indicated the rule-based approach was equivalent to the supervised approach of training and presented better F square estimates than the unsupervised approach. However, it required more manpower and effort to build the rule-based adaptation while the adopted model will demand constant revisions and maintenance based on the change in the consumer vocabulary.
Implementing a Chatbot Music Recommender System based on User Emotion

The use of chatbots has become increasingly popular in recent years, as more organisations try to improve and streamline their customer service operations. One area which has been gaining momentum is the use of chatbots for music recommendation. Such systems utilise AI technologies to deliver personalised music recommendations to users via conversational interfaces. Chatbot music recommender systems present several benefits namely, they provide a personalised and natural experience which can be engaging for the users. Moreover, the users can engage in a dialogue whereby the system can better interpret the user context and preferences. This work presents the development of a chatbot personalised music recommender system, based on Natural Language Processing (NLP) techniques, coupled with a web interface that can provide song recommendations based on the user’s emotions.
Improved Convolutional Neural Network and Transfer Learning with VGG16 Approach for Image Classification

Transfer learning may boost modeling speed. This research unifies the improved VGG16 model. Skipping VGG16’s entirely connected layer and tying it to the layer following it improves CNN’s architecture and reduces its processing burden. CNNs are classifying cat and dog photos. With a small dataset, CNN training may take time and resources. Transfer learning uses ImageNet models to handle similar challenges in new contexts. Transfer learning enhances CNN cat-dog classification. The proposed technique selects a suitable pre-trained model, freezes its layers to retain acquired properties, adds layers to learn task-specific features, and tunes it using the cat and dog dataset. Data augmentation improves model performance and prevents overfitting. Hyperparameter optimization and validation enhance model accuracy and speed. This method enhances cat and dog photo classification even without adequate data to train CNNs with an accuracy of 97%.
Image Classification of Occluded and Non-Occluded Cats and Dogs Datasets with Machine Learning

Pet classification performance can be significantly affected by occlusions, such as pillows, hands, blankets, and shadows. Existing research on pet recognition and classification focuses on poses, breeds, and natural scene variations, but there has been limited work on occlusion-based pet classification. This project aims to improve the performance of deep neural networks for pet classification by using data augmentation, transfer learning, and fine-tuning techniques. Two independent datasets of occluded and non-occluded images of cats and dogs are used to evaluate the proposed algorithms. The investigation explores the impact of transfer learning on cat and dog classification using two proposed models: a fine-tuned Convolutional Neural Network (CNN) and a pre-trained VGG16 model. By leveraging advanced techniques such as transfer learning and fine-tuning, this research demonstrates the potential for significantly improving pet image classification performance, especially in scenarios involving occluded images. These findings contribute to the broader field of computer vision and provide valuable insights for further research in pet image analysis and classification.
Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition And Phoneme To Grapheme Translation

This research optimizes two-pass cross-lingual transfer learning in low-resource languages by enhancing phoneme recognition and phoneme-to-grapheme translation models. Our approach optimizes these two stages to improve speech recognition across languages. We optimize phoneme vocabulary coverage by merging phonemes based on shared articulatory characteristics, thus improving recognition accuracy. Additionally, we introduce a global phoneme noise generator for realistic ASR noise during phoneme-to-grapheme training to reduce error propagation. Experiments on the CommonVoice 12.0 dataset show significant reductions in Word Error Rate (WER) for low-resource languages, highlighting the effectiveness of our approach. This research contributes to the advancements of two-pass ASR systems in low-resource languages, offering the potential for improved cross-lingual transfer learning.
Design and Implementation of Ideological and Political Pedagogy System Based on Service-Oriented Architecture

According to the requirements of intellectualization in the current ideological and political course teaching process, based on the full analysis of the specific needs and objectives of ideological and political mobile teaching, this paper develops an ideological and political pedagogy system based on SOA (Service-Oriented Architecture). Realize the mobile learning of ideological and political course, and elaborate the system's design framework and implementation process in detail. The actual test shows that the system runs stably and performs well, and can effectively promote the ideological and political pedagogy activities, improve the enthusiasm of teachers and students, provide an effective solution for the intellectualization and multimedia of ideological and political pedagogy, and has strong practical and reference value.
Simulation of Industrial Economic Development Model Based on Fuzzy Clustering Algorithm

As human society enters the era of the knowledge economy, the "industrial economic development of Fuzzy Clustering Algorithm (FCM), which promotes the development of science and technology and social progress, has undergone profound changes in both connotation and form, and has gradually become an important part of the national development strategy." In order to investigate the effects of various policy alternatives on the energy industry, a system simulation model of the expansion of the energy industry from the position of a low-carbon economy is created based on DPSIR theory from the perspectives of energy, economy, environment, policy, and society. There are other clustering algorithms available today, but the fuzzy c-means clustering algorithm is the most widely used. Many improved techniques are presented based on this algorithm to produce a better clustering effect. The clustering effectiveness index must be evaluated for the effect after clustering. Clustering analysis is a critical component of unsupervised pattern recognition. Fuzzy clustering has become the industry standard for clustering analysis because it establishes the ambiguous description of the sample for the category and can more precisely reflect reality.
Discriminative Speech Recognition Rescoring With Pre-Trained Language Models

Second pass rescoring is a critical component of competitive automatic speech recognition (ASR) systems. Large language models have demonstrated their ability in using pre-trained information for better rescoring of ASR hypothesis. Discriminative training, directly optimizing the minimum word-error-rate (MWER) criterion typically improves rescoring. In this study, we propose and explore several discriminative fine-tuning schemes for pre-trained LMs. We propose two architectures based on different pooling strategies of output embeddings and compare with probability based MWER. We conduct detailed comparisons between pre-trained causal and bidirectional LMs in discriminative settings. Experiments on LibriSpeech demonstrate that all MWER training schemes are beneficial, giving additional gains upto 8.5% WER. Proposed pooling variants achieve lower latency while retaining most improvements. Finally, our study concludes that bidirectionality is better utilized with discriminative training.
Effectiveness and Usability of Virtual Reality in Physical Education: A Case Study on Dance Teaching

Virtual reality (VR) is an innovative technology that has been applied in a variety of disciplines, including healthcare, engineering, gaming, psychology and education. This research explores the usability and effectiveness of VR in physical education. The research intends to use new features in training. and look into the effects of VR applications in physical education. by utilizing artificial intelligence motion capture tools, such as Rokoko Studio. This study conducted a detailed questionnaire-based survey with high school students participating in VR classes. The survey assessed their attitudes toward and ex-periences with VR in physical education. The results showed moderate agreement with the mean score of 4.28 on attitude towards VR application. VR is well-accepted as a tool for training instruction, offering advantages in motivation and engagement. Participants reported a desire to use VR again due to its user-friendliness and interesting nature. The combination of avatars and video in a virtual environment has been shown to enhance the learning process, despite some limitations.
Benefits of Virtual Reality in Ballet Didactics

The paper presents a holistic approach, merging technology with the rich artistry of ballet, to innovate and redefine ballet education. The project extends to the broader realm of education, particularly ballet. The international research team will explore how cognitive skills related to body-kinesthetic and musical intelligence can be enhanced using 3D spatial context technologies. In this pilot study paper, a preliminary investigation is presented leveraging motion-tracking analytics to discern the nuances across distinct ballet exercise intensities. This inquiry serves as a foundational step toward the formulation of an intricate 3D VR ballet curriculum. The ultimate goal of the project is to generate a virtual animated character that can give visual information and can interact in real-time with a real dancer, thereby supporting the dance learning process. During this preliminary study, participants' kinesthetic motions were meticulously captured and scrutinized. Initial findings from this user study ascertain that a 3D VR environment equipped with motion-tracking analytics distinctly enhances the acquisition of ballet movements and can serve as an efficacious tool underpinning the VR framework of ballet education.
IoT Applications in Sports and Fitness: Enhancing Performance Monitoring and Training

IoT (IoT) has transformed sports, fitness, and more. This article discusses IoT in sports and wellness performance monitoring and preparation. Wearables, sensors, and data analytics help athletes, coaches, and fitness enthusiasts gather, analyze, and use real-time data. Training may improve, reducing injuries and performance. This article covers IoT applications in sports and fitness, covering necessary improvements and their implications on performance checking and preparation. Smartwatches, fitness trackers and biometric sensors record signs, movement patterns, and physiological data. It also analyzes how IoT-enabled devices may broadcast real-time data, enabling athletes and trainers to monitor and assess performance indicators and make decisions remotely. This research investigates sports and fitness IoT systems and data analytics. Predictive models and machine learning algorithms can analyze enormous volumes of data, find patterns, and provide personalized advice to enhance performance and training. It also explores how VR and AR might be utilized with IoT to create realistic, interactive teaching environments. Data privacy and security protect sensitive personal information. This research highlights how IoT applications in fitness and sports may revolutionize the game. IoT may assist athletes, coaches, and fitness enthusiasts in training, performing, and analyzing their performance. IoT will change how it screens, train, and succeed in proactive sports and health.