Reviewerly/exHarmony huggingface dataset:
Research on the Application of Computer Big Data Technology in English Distance Network Course

In this paper, an English teaching system based on remote network and embedded software is proposed. The purpose is to further improve the intelligence of English electronic education. This paper presents a design method of speech precision automatic comparison system based on multimode control. Firstly, the overall design framework of speech precision automatic comparison system is developed. TMS320VC5509A is chosen as the core control chip. Then the hardware design of the system is developed, including A/D circuit, external storage circuit, power-on loading circuit, system logic decoding control circuit and LCD interface design. Finally, the hardware assembly and software debugging of the system are completed. The experiment proves that this system has good reliability and good correction rate in English distance teaching.
Modern Educational Mobile Learning Mode and System Design Integrating Neural Network

With the popularity of mobile devices, the mobile learning model has become a hot topic of concern and has been widely used. In this context, fused neural networks are of great significance as an emerging research field. This paper focuses on the process of establishing a system model by combining genetic algorithm and neuron connection theory, designing and implementing the system, and performing simulation analysis on experimental data and interpretation of results. By studying the difference in the amount of information learners use the same content at different stages to verify its application effect, the results show that the memory usage of the system is between 0.1 and 0.2, the scalability is between 0.5 and 0.62, and the stability is between 0.82 and 0.89. Through the detection of these indicators, it can fully understand the resources occupied by the system during operation, and the system has scalability and can maintain stable operation in the face of demands of different scales.
EffiConvRes: An Efficient Convolutional Neural Network with Residual Connections and Depthwise Convolutions

Convolutional Neural Networks (CNNs) are a widely adopted category of deep learning models, particularly renowned for their efficacy in image classification tasks. This research paper introduces an innovative image classification model derived from the synergistic integration of EfficientNet, ConvNext, and ResNet architectures. Our proposed model utilizes residual connections, grouping of convolutional layers, and depthwise convolutions, which make it computationally efficient while maintaining high accuracy. We evaluate our model on three benchmark datasets, including CIFAR-10, CIFAR-100, and Pneumonia Chest X-ray dataset, and demonstrate that our model outperforms state-of-the-art methods on all three datasets.
An Extended Canberra Similarity Measure Method for Content-Based Image Retrieval

The Content-Based Image Retrieval (CBIR) method is a challenging task in research areas because of multimedia growth in internet. The search of relevance images on search engines is critical task for research community. In this manuscript, the Extended Canberra Similarity measure Method is proposed for measuring the similarity in image retrieval. The dataset utilized for research of content-based image retrieval is Corel 1K and median filter is utilized in preprocessing phase to remove the noise. The Generalized Search Tree (GIST) and Scale Invariant Feature Transform (SIFT) are utilized for the feature extraction and the extracted features are fused by average and weighted average method. At last, the Extended Canberra Distance (ECD) method is utilized for measuring the similarity among query image and image database. The performance of proposed method is estimated by performance metrics like precision, recall and f1-score. The proposed method attained precision of 94.12, recall of 17.64 and F1-score of 60.58 which is comparatively higher than other existing similarity measure methods such as Euclidean, D1 and Canberra.
Design of Cryptosystem Architecture Based on Quantum Key Distribution

Quantum key distribution (QKD) is a promising technique for addressing quantum threats. To enable easy-to-use cryptographic services based on QKD, we designed a cryptosystem that integrates classical cryptographic systems with key generation and distribution using a QKD network. The proposed cryptosystem comprises a centrally deployed cryptography management service platform and distributed key management systems. The paper presents the functional modules, main processes, interface design, and security requirements of the cryptosystem. This cryptosystem provides a secure and efficient way to offer cryptographic services for various business applications, such as identity authentication, application authorization and point-to-point or group session key distribution. By integrating QKD with classical cryptosystems, this approach can improve the security of traditional cryptographic systems by using the random and unconditionally secure keys generated by QKD. Furthermore, combining QKD with classical cryptographic systems can enhance the scalability of QKD and provide more efficient and cost-effective solutions for large-scale cryptographic services.
Protecting the Multimedia using Privacy Preserving Homomorphic Encryption Based Blockchain Model in IoT

Data auditing is becoming increasingly important as more customers want to farm out large programme data produced by Net of Things (IoT) strategy to the cloud. This is because it allows clients to confirm the accuracy of the data they are outsourcing. The majority of current data auditing techniques, however, are unable to ensure complete data integrity and cannot satisfy the security requirements of useful multimedia facilities. Moreover, when the cloud service provider (CSP) tampers with the outsourced data, clients are not promptly compensated due to the lack of fair arbitration. This study suggests a blockchain-based homomorphic encryption-based learning method for multimedia data that protects privacy. In our system, customers often only need to do contract is only triggered to perform a public audit when the private audit's verification is unsuccessful. With this suggested approach, clients may avoid paying audit costs and can be compensated automatically and promptly if the CSP corrupts their outsourced data. Furthermore, when clients want reimbursement from the CSP, our plan can stop bogus claims. We evaluate the security advantages and finish implementing the prototype. The testing findings demonstrate how safe, effective, and useful our blockchain-based data auditing system is.
A Short Video Classification and Retrieval Scheme Based on Transfer Learning

Short videos have become a new form of media, developing rapidly and showing great prospects. Given the exponential increase in the volume of short videos, manual labeling has become increasingly challenging and costly, rendering it feasible only for select short video classification tasks. These issues constrain the efficacy and efficiency of short video data applications at a large scale. To address this issue, this paper proposes to use the residual neural network model as the main deep learning model to extract and classify the features of short videos. To improve accuracy and minimize training time, a short video classification and retrieval system is developed using transfer learning-based model training, enabling the system to leverage ResNet’s deep network model to enhance classification performance while reducing training time and sample size, and achieved high accuracy on small sample datasets.
Research on Action Pattern Recognition Based on DeepConvLSTM

Pattern Recognition is an important way to improve the quality of life and promote scientific research. Through the big data captured by the three-axis sensor, this paper focuses on the establishment of relevant models for the accurate classification of 19 motion modes , and analyses of the generalization ability and overfitting. This paper establishes DeepConvLSTM model by combining the original deep learning convolutional neural network (CNN) and long- term short-term memory recursion (LSTM). The model code about DeepConvLSTM was written by Python and relevant data was imported. In terms of methods, this paper evaluates the generalization ability of the model by using setaside method and K-fold cross-validation method respectively from the algorithm level. In terms of indicators, this paper selects accuracy, accuracy rate, recall rate and F1 value. Finally, the conclusion is drawn as follows: when epoch increases to 40, Train loss and Val loss basically fall together, indicating that the accuracy and stability of the model are excellent.The paper use logistic regression model to solve the classification problem of human behavior. It is found that the classification effect of DeepConvLSTM model was better than that of LR model with higher accuracy and no overfitting phenomenon. in addition, Recall is used to analyze the sensitivity of the model. After several tests, it is found that the model shows good sensitivity and stability when the training times reaches 60. On this basis, considering the cost and efficiency of training comprehensively, the number of training is set as 60 in this paper, and the sensitivity and robustness of the model are both good. In conclusion, this paper establishes a scientific action pattern recognition model according to the conditions of the subject and the data given. The model is simple and easy to popularize. After verification and analysis, the model in this paper has strong accuracy, robustness and sensitivity, and has certain practical significance as the paperll.
General Risk Index : A Measure for Predicting Violent Behavior Through Written Communication

One of the most challenging threats to the security of society is attacks from violent lone offenders. Identifying potential offenders is difficult since they act alone and do not necessarily communicate with others. However, several targeted violent attacks have been preceded by communication published on social media and the internet. Such communication is a valuable component when conducting risk and threat assessments.In this paper, we introduce a diagnostic measure of the risk of violent behavior based on text analysis. Using automated text analysis, we extract psychological variables and warning indicators from a given text and summarize these in an index that we denote as the general risk index. When developing the general risk index, we analyzed data (text) from 208 288 users on 32 online environments with diverse ideologies/orientations, including 76 previous violent lone offenders. A receiver operating characteristics (ROC) analysis showed that, when using the general risk index, it was possible to correctly classify between 90% and 96% of the cases depending on the comparison sample. These results support the predictive validity of the general risk index, suggesting that the risk index can be used to identify individuals with an increased risk of committing violent attacks that need further investigation.
Decentralized Personalized Online Federated Learning

Vanilla federated learning does not support learning in an online environment, learning a personalized model on each client, and learning in a decentralized setting. There are existing methods extending federated learning in each of the three aspects. However, some important applications on enterprise edge servers (e.g. online item recommendation at global scale) involve the three aspects at the same time. Therefore, we propose a new learning setting Decentralized Personalized Online Federated Learning that considers all the three aspects at the same time.In this new setting for learning, the first technical challenge is how to aggregate the shared model parameters from neighboring clients to obtain a personalized local model with good performance on each client. We propose to directly learn an aggregation by optimizing the performance of the local model with respect to the aggregation weights. This not only improves personalization of each local model but also helps the local model adapting to potential data shift by intelligently incorporating the right amount of information from its neighbors. The second challenge is how to select the neighbors for each client. We propose a peer selection method based on the learned aggregation weights enabling each client to select the most helpful neighbors and reduce communication cost at the same time. We verify the effectiveness and robustness of our proposed method on three real-world item recommendation datasets and one air quality prediction dataset.
Explainable Artificial Intelligence Techniques for Image Classification Models in Diverse Domains

Over the past few decades, Deep Learning (DL), which is a subpart of Machine Learning (ML), has gained recognition as the dominant technique in diverse domains such as Computer Vision, Natural Language Processing, and Image Recognition. However, despite the exceptional performance of DL models, they are often referred to as of “Black Box“ nature, due to their limited ability to explain the decisions. This has led to a need for explainability of DL models. Therefore to address this issue of lack of explainability in DL models, Explainable Artificial Intelligence (XAI) techniques have been improving in recent era. Unlike DL, XAI has the ability to provide both decision-making and model explanations for data. This research review comprises articles that utilize XAI techniques for image classification tasks. The purpose of this paper is to showcase the utility of the most recent XAI techniques employed in Image Classification Models (ICM). This paper will assist researchers to develop a better intepretation of XAI techniques for image classification as well as related challenges and future research directions.
EMG-based Feature Engineering Approach for Load Classification

We demonstrate a machine learning-based Human Activity Recognition (HAR) model in terms of load classification. A self-collected dataset using surface electromyography (sEMG) and BIOPAC MP36 was employed for developing the proposed model. The sEMG signals were obtained while 12 volunteer subjects lifted weights of 1 kg, 3 kg, 5 kg, 7 kg, and 10 kg. In the first step, we utilized an IIR elliptic filter to de-noise the collected sEMG signals. Next, a region of interest extraction technique based on automated relative energy empirical mode decomposition (AREEMD) was used to remove the extraneous data. Then, feature extraction was carried out by employing several pairs of statistical, time domain, frequency domain, and cepstral features. A 10-fold cross-validation technique was used to test the validity of the chosen features on a variety of classifiers, including support vector machine (SVM), decision tree (DT), ensemble, neural network (NN), and K-nearest neighbor (KNN). SVM using a cubic kernel outperformed other classifiers in terms of accuracy, providing the highest classification accuracy of 98.5% for five-class load classification.
Differentially Private Frequency Tables Based on Random Sampling

Previous research proposes a random sampling-based techniques [4], [16] to produce differentially private k anonymized data based on random sampling. However, their approach considers all variables in an original dataset as quasi-identifiers with full domain transformation to guarantee differential privacy. Since all the equivalence classes in anonymized data contain identical records with the same set of attribute values, that anonymized data is not useful for the analysis of microdata. However, we consider random sampling a promising approach to guaranteeing differential privacy on frequency tables since k-anonymized data in which all variables are treated as quasi-identifiers is equivalent to a frequency table.In this paper, we evaluate the feasibility of producing deferentially private frequency tables based on random sampling. Our experiments show that we can produce frequency tables of high data utility when we set small $\epsilon$ to choose a low sampling rate for a dataset with a large number of records.
Interpretable Solar Flare Prediction with Sliding Window Multivariate Time Series Forests

Recently, the synergy of physics-based feature engineering and data-intensive methods, including machine learning and deep learning, has ushered in a new era in the analysis and prediction of space weather forecasting, specifically for solar flare prediction. These sophisticated approaches play a pivotal role in understanding the complex mechanisms leading to solar flares, with a primary focus on forecasting these events and mitigating potential risks they pose to our planet. While current methodologies have made substantial advancements, they are not without limitations, and one particularly glaring limitation is the neglect of temporal evolution characteristics within the active regions from which solar flares originate. This oversight impairs the capacity of these methods to capture the intricate relationships among high-dimensional features of these active regions, thereby constraining their practical utility. Our study focuses on two key objectives: the development of interpretable classifiers for multivariate time series data and the introduction of an innovative feature ranking method using sliding window-based sub-interval ranking. The central contribution of our work lies in bridging the gap between complex, less interpretable “black-box” models typically employed for high-dimensional data and the exploration of pertinent sub-intervals within multivariate time series data, with a specific emphasis on solar flare forecasting. Our findings underscore the efficacy of our sliding-window time series forest classifier in solar flare prediction, achieving a True Skill Statistic of over 85%. Our approach is capable of pinpointing the most critical features and sub-intervals relevant to any given learning task. These results indicate significant progress toward improving the interpretability and accuracy of flare prediction models, further advancing our understanding of these impactful events.
Dynamic Parameter Initialization and Optimization for Vehicular Image Recognition via Meta-Learning and Model Pruning

This study is dedicated to solving the problem of image recognition in vehicular networks, with a particular focus on two key scenarios: traffic sign and vehicle classification. To achieve this, we developed an advanced deep learning model that integrates both meta-learning and model pruning techniques. Meta-learning is used for the efficient initialization of model parameters, thereby enhancing the model’s generalization capabilities. Model pruning, on the other hand, optimizes the computational efficiency of the model by removing unnecessary parameters to reduce its complexity. After rigorous theoretical analysis and experimental validation, the results on two crucial datasets showed significant performance improvements. Compared to the baseline model, which did not apply meta-learning and model pruning, our model improved the accuracy rates by 2.7% and 2.9%, respectively. This progress not only proves the effectiveness of these two optimization techniques in vehicular network applications but also provides valuable insights for future research in this field. Overall, this study not only addresses specific image recognition issues in vehicular networks but also demonstrates the potential and effectiveness of applying advanced techniques like meta-learning and model pruning in real-world scenarios. These achievements contribute to the advancement of image recognition technology in vehicular networks and offer new perspectives and practical methods for further research in the same domain.
GemInsight: Unleashing Random Forest for Diamond Quality Forecasting

Diamonds are considered as one of the rarest and most expensive gemstones in the world. The quality of any diamond always determines its market price. Diamond quality generally depends upon several characteristics. The total internal reflection and refractive indices of the diamond, which depend on its cut, are used in the conventional diamond industry to determine the quality of the diamond. As the cost of this gemstone is very high, hence accurate grading of diamond is quintessential. In this work an automated diamond grading solution is proposed using Machine Learning. For better identification of the diamond cut, the recommended system uses its different qualitative parameters. For model training and evaluation, diamond dataset residing in the Kaggle repository has been utilised. Random Forest Model has shown promising accuracy of 79%.
Research on Popular Machine Learning Algorithms

Artificial Intelligence technologies have been developed rapidly in the past 20 years and have been widely applied in various industries. Machine Learning, as the most important way to achieve artificial intelligence, is evolving at a huge speed. Keeping track of the regular development is quite a difficult work, especially for beginners. In this paper, we are going to briefly discuss about the most popular machine learning algorithms and their variants or improved versions. These algorithms are usually classified as supervised learning or unsupervised learning methods, or other grouping methods based on functionality. Deep Learning is covered because deep learning methods have led to revolutionary progress in computer vision and machine learning. We also talk about the industries that these models can be applied in. This article is intend to provide basic references of these commonly used algorithms in the field for beginners, help them to get a general understanding of the algorithms and the scenarios, and guide to select appropriate algorithm or model to start solving a practical data problem.
A Study on Deep Learning Frameworks to Understand the Real Time Fault Detection and Diagnosis in IT Operations with AIOPs

Business operations in the recent Era are highly complex and demand high throughput technology to address the needs. Artificial Intelligence for IT Operations (AIOPs) plays a vital role in the detection of problems in real-time IT operations and provides solutions for businesses. This study outlines the understanding of deep learning models in fault detection and diagnosis in IT operations using AIOPs. Data was acquired from IT experts who are playing significant roles in the companies through a survey questionnaire which helps to analyze deep learning models used for business. Subsequently, hypothesis testing was done to assess the proposed framework for enhancing accuracy with reduced downtime. The results show that the framework achieves good accuracy, precision, and recall rates in comparison to classic machine-learning techniques. The proposed framework has the potential to realize problem detection and diagnosis in IT operations with a hybrid deep learning model and AIOPs to increase efficiency.
Applied Data Science for Leasing Score Prediction

We describe the design, the architecture, and the evaluation of the Leasing Score Prediction (LSP) system - a credit scoring and credit rating system for the leasing sector deployed at the Italian association of leasing companies. Due to its challenging objectives, the design and complexity of the LSP system represent a unique contribution to the best practices in the field. We cover requirements by managers, users, and regulations about rigorous backtesting, statistical validation, calibration, explainability, robustness and uncertainty self-assessment. LSP relies on a machine learning model trained on a mixture of data distributions contributed by many associated leasing companies. We describe the technical solutions adopted and report on their performance evaluation, including the management of the data shifts due to the COVID-19 pandemic.