Reviewerly/exHarmony huggingface dataset:
Neural Network Grading: Automated Evaluation of Theoretical, Mathematical, and Diagrammatic Responses

Neural network-based computerised paper evaluation has the potential to completely transform the current paper evaluation procedure. The system may grade student papers automatically with the aid of machine learning algorithms, which can decrease human mistake and boost efficiency and speed in the grading process. The ability of neural networks to handle a variety of questions, including both subjective and objective questions, is one of the main benefits of utilising neural networks for computerised paper evaluation. The approach outlined in this paper utilizes Optical Character Recognition (OCR) to process images and extract various components. Various techniques are used to prepare the OCR for specific types of questions. Theoretical questions are processed using the Artificial Neural Network algorithm, while mathematical expressions and diagrams are evaluated using the Convolutional Neural Network. In order to achieve this, the CNN must recognize specific characteristics in the diagrams and mathematical expressions and assign scores based on the accuracy of the solution provided. The neural network is trained to recognise patterns and features in the data using the backpropagation technique. This method alters the weights as well as the biases in the neural network to lessen the difference between the expected output and the actual result. Until the network's performance improves and it can precisely assess the student's performance, this process is repeated across a number of epochs. This strategy is especially successful since it enables the system to examine a vast amount of data and anticipate future events based on patterns in that data.
ID-MixGCL: Identity Mixup for Graph Contrastive Learning

Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different “views“ of the same graph in order to learn node/graph representations. The underlying assumption of these studies is that the graph augmentation strategy is capable of generating several different graph views such that the graph views are structurally different but semantically similar to the original graphs, and thus the ground-truth labels of the original and augmented graph/nodes can be regarded identical in contrastive learning. However, we observe that this assumption does not always hold. For instance, the deletion of a supernode within a social network can exert a substantial influence on the partitioning of communities for other nodes. Similarly, any perturbation to nodes or edges in a molecular graph will change the labels of the graph. Therefore, we believe that augmenting the graph, accompanied by an adaptation of the labels used for the contrastive loss, will facilitate the encoder to learn a better representation. Based on this idea, we propose ID-MixGCL, which allows the simultaneous interpolation of input nodes and corresponding identity labels to obtain soft-confidence samples, with a controllable degree of change, leading to the capture of fine-grained representations from self-supervised training on unlabeled graphs. Experimental results demonstrate that ID-MixGCL improves performance on graph classification and node classification tasks, as demonstrated by significant improvements on the Cora, IMDB-B, IMDB-M, and PROTEINS datasets compared to state-of-the-art techniques, by 3-29parcent absolute points. The dataset and source code can be found at: https://github.com/zhanggehang/ID-MIXGCL.
Analysis of Negative Sampling Methods for Knowledge Graph Embedding

Negative sampling is an approach used to speed up knowledge graph embedding learning and maximize the efficacy of embedding models for supported tasks such as link prediction and entity resolution. Negative sampling is essentially important to improve accuracy, reducing bias, increasing efficiency, and improving representation. This paper scrutinizes the aftermath of increasing the number of negative samples per positive available for two basic negative sampling techniques for tensor factorization and translational embedding models on the benchmark dataset Fb15k. For both uniform and Bernoulli sampling, pattern showing changes in performance based on increasing numbers of negatives per positive is worth noticing.
Development of Resources Library System Based on J2EE Layered Architecture

J2EE is essentially a distributed server application design environment that provides a component-based approach to designing, developing, assembling, and deploying enterprise-level applications. It provides a good mechanism for building application systems with scalability, flexibility and ease of maintenance. Based on the J2EE layered architecture, this paper designs the Chinese classics reading resource library system. The core functions of the system are composed of a resource storage subsystem, a resource maintenance subsystem, a resource utilization subsystem, and a resource management subsystem, which provide an integrated platform for the recitation of Chinese classics. At the same time, in order to improve the efficiency of resource use, a user-based collaborative filtering recommendation algorithm mathematical model is constructed to recommend among users with the same interests, so as to solve the user's resource selection problem. It is found that the most effective solution to solve the problems of cold start, sparsity, scalability, diversity and interpretability of traditional collaborative filtering algorithms is to use auxiliary information such as rating data, social network information and other information, combined with deep learning and data mining technologies, to improve the recommendation effect and enhance user satisfaction.
Bias Mitigation in News Recommender Systems Using Preference Correction

This paper presents a novel approach for mitigating biases in news recommendation systems while also incorporating a user's personal preferences to provide a personalised recommendations. The proposed hybrid content and collaborative-based filtering system ranks news categories according to weights based on user history, resulting in a recommendation system that recommends news articles from a purely unbiased perspective. The method of “preference correction” is incorporated to further recommend articles that the user will also prefer to read, improving the overall user experience. This approach can be applied to any news dataset that collects user impressions and has the potential to enhance awareness among readers and burst filter bubbles.
Feature Selection Based Ensemble Support Vector Machine for Financial Fraud Detection in IoT

Although innovations like the Internet of Things, blockchain have simplified the process of making financial transactions, they have also given rise to new forms of fraud that have set back the advancement of Internet and IoT finance. The recent development of an ML-based financial fraud transaction system has highlighted the necessity for optimisation strategies to address the large dimensionality of the feature vector and the problem of class imbalance in any dataset. In this research, we offer a novel tactic to financial fraud detection that combines the bio-inspired optimisation algorithm with a two-stage ensemble support vector machine (ESVM). The dataset is first preprocessed through three stages that use the Bird Mating Optimisation Algorithm (BMOA) to ensure data balance. Finally, Least Square SVM (LS-SVM) and Latent Variable SVM (LV-SVM) are used for classification in the course of fraud detection. Furthermore, a comparison study between the suggested method and the current methods has been conducted. The suggested method effectively classified fraud transactions with an accuracy of 98%, far higher than the state-of-the-art approaches. The suggested method improves classification accuracy while simultaneously decreasing the number of incorrectly classified transactions and the expenses associated with misclassification. Compared to existing machine learning methods, the suggested BMOA-ESVM approach performs better in the tests.
Anomaly Detection of ECG Time Series Signal Using Auto Encoders Neural Network

The electrocardiogram (ECG) time series signal is a vital diagnostic tool for detecting cardiovascular diseases. The signals have widely been used in the healthcare industry to assess the health of the human heart. Typically, medical professionals manually analyze the resulting time series signal to identify any arrhythmia in the patient. Numerous research efforts have been made to automate the analysis of ECG signals. In order to differentiate normal ECG signals from those indicated of arrhythmia, most of these methods first need substantial preprocessing of the ECG data to obtain vectorized features, and then the building of a classifier. Due to the training data and expertise requirements, these methods cannot detect arrhythmias that were not included in the training set. It is suggested that utilizing an anomaly detection approach may be a more suitable method for analyzing electrocardiogram (ECG) signals. In this study, feedforward auto encoder neural network architecture is employed to develop a predictive model for healthy ECG signals. An advantage of using auto encoders is that it learns useful representation of ECG signal which is used as feature for downstream tasks such as classification. Notably, the models were solely trained on normal ECG data and did not require any prior knowledge of abnormal signals. Encouragingly, the results of this study indicate that auto encoder models have potential as a viable method for detecting anomalies in ECG signals.
MetaFRS- Federated Learning based Cold Start Recommendation System using Meta-Learning

The field of recommender systems has seen significant advancements recently, but several challenges remain. In this paper, we address two challenges in recommendation systems. Firstly, conventional recommendation systems require uploading private data to a central server for training, which inevitably impacts user privacy. To tackle this issue, we use Graph Federated Learning (GFL), a novel paradigm for distributed learning that ensures privacy preservation by enabling training on distributed data and eliminating direct data sharing. However, distributed recommender systems have a performance gap compared to non-distributed ones due to incomplete user-item interaction graphs. As a result, these systems struggle to utilize indirect user-item interactions effectively. Secondly, the cold start scenario, where a recommender system lacks sufficient data to make accurate recommendations for new users or items. Therefore, we propose MetaFRS - Federated Learning based Cold Start Recommendation System using Meta-Learning to overcome these limitations. Our system incorporates a graph neural network that uses attention mechanisms and an aggregation layer to summarize various orders of indirect user-item and user-user interactions. Meta-learning algorithm is employed to address the issue of sparse interactions in cold start scenarios and incomplete user-item graphs in a distributed setup.
Fine Tuning Pretrained Transformers for Abstractive News Summarization

Automated text summarization is now more important than ever due to the exponential rise of internet text data. It takes time to sift through large amounts of content, especially when manually creating accurate summaries of big publications. Automating the summarization process is crucial to increase the effectiveness of machine learning models. There are two ways to create summaries: abstractive summarization, which includes analyzing the original text to create a summary, and extractive summarization, which chooses pertinent sentences from the original text. The T5, GPT2 and BART pre-trained transformer models for abstractive news summarization will be compared in this study. For our investigation, we have used the CNN/Dailymail dataset, which includes summaries created by humans for assessing and contrasting the summaries produced by various models. In order to better understand how transformer models perform for text summarization tasks, we analyze which model performs better for abstractive news summarization with Fine Tuning.
Improving Security of IOT Device Using Blockchain Technology

The implementation of blockchain technology in Internet of Things (IoT) projects has become a hot topic in recent years. Blockchain offers a secure, decentralized, and tamper-proof way of storing and transmitting data, making it an ideal solution for IoT projects where large amounts of sensitive data are being transmitted and stored. This paper aims to demonstrate the practical implementation of blockchain technology in an IoT environment. The paper will focus on the development of a system that uses blockchain to secure data transmission and storage between IoT devices. The system will be tested using real-world IoT devices and will be evaluated based on its security, scalability, and performance. The outcome of this project will be a proof-of-concept that demonstrates the viability of blockchain in IoT projects and provides insights into the challenges and opportunities that come with this integration.
Advanced Email Spam Detection: A Machine Learning Solution

Email categorization is crucial in business and academia, filtering spam emails that risk phishing, fraud, and theft. This study employs a hybrid approach, considering group and individual strengths and weaknesses in email classification. It prioritizes identifying spam, particularly phishing, using data mining and the UCI spam base dataset. The primary objective is to enhance automated learning for accurate detection and filtering, differentiating legitimate messages from spam. Radial Basis Function Neural Networks (RBFNN) are central to this work, a key component of artificial neural networks (ANNs) for data classification. The paper encompasses various algorithms: Support Vector Machine (SVM), Neural Network, Naive Bayes, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, Harris Hawk’s Optimizer (HHO), and Extreme Learning Machines (ELM). This research advances email classification, bolstering digital communication's security and efficiency.
Prediction of Yoga Pose from YouTube Dataset using Skeleton Feature Extraction Based ISDL Model

The ancient Indian discipline of yoga has spread over the world as a means of improving one's physical, mental, and spiritual health. Computational probing has a huge potential in all areas of society, thanks to the rapid development of related technologies. It has been shown that pose detection methods may be utilised to both recognize the postures and help people do yoga with greater precision. Due to the difficulty of detecting posture in real time and the limited availability of datasets, posture recognition is a difficult task. This study use a deep learning model to categorise yoga postures according to their difficulty. This research suggests a methodology for automatic detection and classification based on deep learning. The steps of the proposed technique are called preprocessing, feature extraction, and classification. At first, a preprocessing stage is executed to remove any excess noise from the edges. The next step is an equalisation based on the histogram to isolate the usable parts of the image. Skeleton-based models are used to extract features; an improved synergic deep learning (ISDL) model is then used to categorise input photographs into different postures; and last, the Archimedes optimisation algorithm (AOA) is used to further refine the model. The provided SDL model is validated using data extracted from YouTube videos. Experiments showed that the projected ISDL model outperformed the state-of-the-art models in accuracy.
Have your data and share it too: Private data analytics at scale

As the importance of data protection and privacy legislation is increasingly recognized worldwide, protecting sensitive information and individual privacy presents a major challenge for modern big data analytics systems. The ever-growing list of major data breaches (and associated fines) clearly demonstrates the inadequacy of earlier ad-hoc solutions to the problem, as well as the need to effectively bridge legal and technical/systems interpretations of data privacy.As the importance of data protection and privacy legislation is increasingly recognized worldwide, protecting sensitive information and individual privacy presents a major challenge for modern big data analytics systems. The ever-growing list of major data breaches (and associated fines) clearly demonstrates the inadequacy of earlier ad-hoc solutions to the problem, as well as the need to effectively bridge legal and technical/systems interpretations of data privacy. In this talk, I will present different modern privacy enhancing technologies (including federated learning, secure computing, differential privacy, and synthetic data), and discuss how they can enable formal, cryptographic notions of privacy in large-scale data analytics. The focus will be on our recent efforts to build systems and tools to support querying and machine learning over sensitive medical data. Several open challenges and directions for future research will also be discussed.
Enhancing Solar Power Forecasting in Multi-Weather Conditions Using Deep Neural Networks

Renewable energy, with its abundant and sustainable potential, has emerged as a critical component of the global energy landscape. The need for accurate power forecasting is evident due to the demands of integrating renewable energy into existing grids, increasing load management, managing resources efficiently, and reducing environmental impact. To address this need and introduce a new era of even more accurate solar power predictions, this research paper presents a comprehensive investigation. In this pursuit, we introduce an innovative Deep Neural Network (DNN) and thoroughly assess its performance against benchmark machine learning models, including Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and Support Vector Regression (SVR). To train and test the models real-world solar power generation data from the Quaid e Azam Solar Park is used. To achieve more accurate models the data is divided into diverse weather conditions sunny, cloudy, and rainy days. Each model is trained and tested separately. The results decisively demonstrate that the proposed DNN outperforms all other models, offering a promising avenue to revolutionize solar power forecasting and enhance the harnessing of renewable energy sources.
A Survey on Segmentation Techniques in Fractal Classification Systems

Fractal-based classification systems have gained significant attention in recent years due to their ability to model complex data structures efficiently. These systems rely on the segmentation of data into self-replicating patterns, which are integral to their classification process. This survey explores the various segmentation techniques such as image pyramids, wavelet transform, region growing, k-means clustering, and fractal dimension-based segmentation employed in fractal classification systems, providing an in-depth analysis of their strengths, weaknesses, and applications. Each technique is evaluated based on its suitability for different data types and domains, highlighting their advantages and limitations. Finally, the survey outlines the current challenges and future directions in the field of fractal-based classification systems, emphasizing the need for improved segmentation techniques to handle large and complex datasets. By providing a comprehensive overview of segmentation methods in the context of fractal classification, this survey aims to contribute insights for improved pattern recognition in diverse applications.
Enhancing Grade Prediction in WE Learn Mobile Classroom Using Clustering-based Decision Tree

With the advent of the big data era, various industries are facing the trend of informatization, and the education industry is no exception. Traditional teaching methods are no longer the only option, and various educational platforms are emerging, leading to the continuous increase of data resources in the education industry. However, the value of these massive amounts of data has not been fully developed due to some restrictions in early data mining technology and personal data protection. Currently, most educational platforms can only store and query data simply, and these undeveloped data also lead to some educational platforms lacking complete functions in personal services. To solve this problem, this paper proposes a decision tree model improved by using clustering algorithms. Firstly, we conducted feature engineering and used clustering algorithms to divide students’ English scores. Next, we brought these feature data into the model, verified the influencing factors of English scores, constructed a decision tree model, and achieved the prediction of WE Learn’s students’ course scores. Finally, we comprehensively evaluated the model using common model evaluation indicators, MAE and RMSE. The results show that the constructed model can provide some reference value for the English education and teaching work of art students on the WE Learn platform.
Using Machine Learning Regression Model to Predict the Optimum Election Algorithm for Parallel and Distributed Computing Systems

Parallel and distributed computing has become a necessity when it comes to high-performance computing, artificial intelligence, big data analytics, machine learning, deep learning, signal processing and bioengineering. System engineers connect distributed systems, with large number of nodes, that then compute the solution for a problem concurrently. The distributed system has a leader that manages the overall coordination of these connected nodes. This research provides a novel approach to predict the most efficient and time-feasible election algorithm, that must be ran on a distributed system, using linear regression machine learning approach. This research implements the prediction process for three election algorithms and successfully predicts the most efficient and least time-consuming algorithm with 94.98% accuracy. Initially, the dataset for the regression model training was created by executing the three election algorithms parallelly using MPI running 16 parallel processors (cores). The research also presents three regression line equations, trained and modelled on this dataset, that can be used to predict the execution time for the three algorithms for a system with any number of nodes. The results are duly visualized, and regression lines are plotted for comparison.
Evaluating the Remix Solidity Static Analysis Plugin Using SB Curated Dataset

Smart contracts once deployed, their effects are irreversible. With the wide use of smart contracts in various applications, it becomes necessary to make sure contracts are free from any vulnerabilities before deploying them. Software for automated evaluation of smart contracts have become very popular to check for vulnerabilities in them. In the current work, we have tested the performance of the Solidity Static Analysis plugin that is integrated with Remix IDE. We have used the SB curated vulnerability labeled smart contracts database to test the tool for its effectiveness. The results are compared with the performance of other tools to measure the effectiveness of the plugin. We have suggested tools that can be combined with the Remix Solidity Static Analysis plugin to increase its effectiveness in security inspection of smart contracts.