Reviewerly/exHarmony huggingface dataset:
THE USABILITY EVALUATION OF ONLINE TRAINING PLAGIARISM MODULE USING ICCEE APPROACH FOR ACADEMIC WRITING BEGINNERS

Plagiarism is one of the issues commonly observed in academia, particularly in academic writing. Academic writing is challenging, especially for beginners who have just begun to learn how to write a research-oriented article. Compared to other genres of writing, academic writing has its own rules and organization. Previous studies have shown that students tend to plagiarize when writing their academic papers. The existing modules that serve as a reference for academic writing are lacking in content which emphasizes the importance of academic integrity. Realizing this phenomenon, a module called the Online Training Plagiarism Module was developed using the Identify-Choose-Create-Engage-Evaluate (ICCEE) approach in order to help beginners to write good papers with integrity and avoid plagiarism. A survey approach was used in this study to evaluate the usability of the Online Training Plagiarism Module. A total of 70 first year postgraduate students participated in this study and those students can be categorized as beginners in academic writing A usability questionnaire adapted based on USE instrument was used to assess four aspects, which are usefulness, ease of use, ease of learning, and satisfaction. The findings show that the usability level of the proposed module is high among the beginners. Therefore, using the Online Training Plagiarism Module for academic writing beginners is both relevant and acceptable, allowing beginners to use it as a reference in academic writing courses.
Load Balancing in Cloud Computing: A Simulation-Based Evaluation

Cloud computing has become a popular paradigm for delivering on-demand computing resources over the internet. One of the key challenges in cloud computing is load balancing, which ensures that computing resources are efficiently utilized, and applications are highly available. Load balancing is a crucial aspect of cloud computing that ensures optimal resource utilization while providing high performance to end users. This paper presents a simulation-based evaluation of three load balancing algorithms: Round Robin, Equally Spread, and Location-Aware based on four key criteria: Balancing Efficiency, Complexity, Suitability, and Fault Tolerance using the CloudAnalyst tool. We investigate the performance of these algorithms in a simulated cloud environment. Our results demonstrate that Equally Spread outperforms the other two algorithms in balancing efficiency and fault tolerance, while Location-Aware is better suited for complex cloud environments. Our simulation-based evaluation provides valuable insights into the performance of different load balancing algorithms in cloud computing environments based on key criteria. It highlights the importance of choosing an appropriate load balancing algorithm based on the application's specific requirements and the cloud environment for efficient resource utilization and improved performance.
An Overview of Video Tampering Detection Techniques: State-of-the-Art and Future Directions

In the realm of video processing and forensics, finding video tampering is a critical challenge. The purposeful change or manipulation of video material to conceal or falsely depict the original information is referred to as “video tampering.” A significant demand for efficient video tampering detection methods has emerged in recent years as a result of the rise in false news, cyberbullying, and other types of criminal behavior. Modern approaches for detecting video manipulation may be generally divided into two categories: passive methods and aggressive methods. Whereas active methods entail putting digital watermarks or other identifiers on the video clip to aid tampering detection, passive methods rely on evaluating the video content for inconsistencies or artifacts generated during tampering. Techniques like sensor noise analysis, splicing detection, and forgery detection based on compression artifacts are examples of passive approaches. To find any discrepancies produced by manipulation, sensor noise analysis examines the noise patterns in the video. Splicing detection entails identifying the borders between several video segments and inspecting the content of each segment for irregularities. Analyzing the compressed video stream for irregularities caused during tampering is required for detecting forgeries based on compression artifacts. Techniques like digital watermarking, digital signatures, and fingerprinting are examples of active procedures. To detect any manipulation efforts, digital watermarking entails putting a digital watermark into the video clip. To confirm the legitimacy of the video clip, a digital signature is applied to it. To do fingerprinting, special characteristics from the video footage are extracted and saved in a database. In addition to the aforementioned methods, machine learning-based systems have also demonstrated promising outcomes in the identification of video manipulation. Using these methods, any tampering attempts in fresh movies are detected by training a machine learning model on a dataset of tampered and untampered recordings. Many issues still need to be resolved despite the improvements in video manipulation detection methods. They include the detection of tampering in live video broadcasts as well as the identification of deep fake movies, which are incredibly lifelike and challenging to spot. The creation of increasingly complex machine learning models, the fusion of several detection methods, and the investigation of novel methods for detecting tampering in live video feeds are some of the future goals for study in this area.
Capitalising the future of higher education: investors in education technology and the case of Emerge Education

The aim of this chapter is to document and conceptualise the influence of new investment actors in higher education transformation. Digital education technology is rapidly expanding in higher education and profoundly changing teaching and learning processes, management of higher education institutions, and subjectivities of staff and students. We argue that investors are crucial actors in digitalising higher education by deciding which products and services will be developed and influencing the business models behind those products. In this chapter, we empirically focus on Emerge Education, a UK-based seed investor. It has already penetrated the higher education sector by investing in a portfolio of digital products and services, partnering with key organisations and stakeholders, creating guidelines targeted at university leaders, and offering advice to education startup entrepreneurs. By mobilising theoretical and methodological resources from the sociology of markets and critical data studies, the chapter presents an analysis of Emerge Education as an exemplar of how new education technology investors are seeking to transform higher education via digitalisation.
Letter Theory, Creating and Describing Mathematical Axioms of a System for Letters which Creates Most Simplified Language with Provable Sentences

This paper is a cross field work between theology, linguistics and mathematics (abstract algebra, topology, set theory, fractal geometry and number theory) which tries to establish a letter system to define word meanings by letter meanings and to restart mathematical theology on this planet which we think had been started by Abraham the prophet. We try to answer this question that can we create a meaning system which every alphabet letter has an specific meaning in, and when we combine them to create words we have a clear system to define every word meaning just by its letters meaning and order of that letters in the word? We will show that if we have a fractal system we can establish a letter system for it. The other question which I try to answer is that can we produce a letter system which provide us provable sentences?
Multimodal End-to-End Visual Document Parsing

The record meterials used in some industries including medical service and insurance, whose information has high commercial and scientific research value, are still mainly paper-based. Recent progress in deep learning makes it easier to parse visually-rich document. Compared with traditional manual input, the application of this technology contributes to improvement of work efficiency and reduction of the training cost of business personnel. In previous work, the task of visual document parsing was divided into two stages which are composed of Optical Character Recognition (OCR) and Natural language understanding (NLU). In order to solve a series of problems in OCR, such as high computational costs, multi-language inflexibility and backward propagation of OCR errors, OCR-free multimodal visual document understanding method based on deep learning has been proposed recently. Through fine-tuning the pre-trained model, it can perform well in many downstream tasks. However, such approach is still limited in the specific context by 1) the language and context in which the encoder and decoder are pre-trained; 2) the image input size of the pre-trained encoder. In view of the above two problems, in this paper, we put forward the corresponding solutions, as a result, our proposed scheme won the second place in the “Identification of Electronic Medical Paper Documents (ePaper)” (IEMPD) task in the Eighth China Health Information Processing Conference (CHIP 2022) in an end-to-end way.
Short Paper: Privacy Preserving Decentralized Netting

This paper proposes a secure decentralized protocol that a consortium of local banks can use to complete a netting process in a privacy-preserving fashion without relying on a single central bank. To do so, it makes use of two key ingredients - zero-knowledge proofs and secure multiparty computation (MPC). We use these two primitives to construct a multi-phase protocol and prove its security in the UC framework. We also study the feasibility of using Linear programming based solution to find optimal solution to the netting problem in gridlock scenarios. To do so, we implemented a proof of concept of our proposed protocol using MP-SPDZ [Kel20] (a framework for prototyping MPC applications) and analyzed the costs involved. Compared to prior work [Cao+20], we manage to achieve 40–50% higher percentage settlement of total transaction.
Clinical Coding Based on Knowledge Enhanced Language Model and Attention Pooling

Clinical coding is obtaining a standard ICD code based on the patient’s electronic medical record (EMR) information, including diagnosis, procedure, drug list, order, etc. It is essential in the medical record information management of the hospital. However, the medical record data have problems such as low quality, insufficient data, full of non-standardized jargon, irrelevant order information, and unbalanced data distribution, which result in poor performance of clinical coding. This task is a multi-label classification problem. Based on the medical pre-trained language model and our medical knowledge engineering (MetaMed KE), we proposed a Value-Level Attention Pooling (VLAP) to build a clinical diagnostic coding framework for Chinese electronic medical records. The framework includes three components: preprocessing module, the model, and the postprocessing module. Compared to existing algorithms, our framework dramatically improves the generalization ability and accuracy in the case of insufficient data and class unbalance. Thus, our method provides a reliable automatic solution for clinical coding in hospital medical record information management.
A Novel System Architecture for Anomaly Detection for Loan Defaults

Given the rise in loan defaults, especially after the onset of the COVID-19 pandemic, it is necessary to predict if customers might default on a loan for risk management. This paper proposes an early warning system architecture using anomaly detection based on the unbalanced nature of loan default data in the real world. Most customers do not default on their loans; only a tiny percentage do, resulting in an unbalanced dataset. We aim to evaluate potential anomaly detection methods for their suitability in handling unbalanced datasets. We conduct a comparative study on different classification and anomaly detection approaches on a balanced and an unbalanced dataset. The classification algorithms compared are logistic regression and stochastic gradient descent classification. The anomaly detection methods are isolation forest and angle-based outlier detection (ABOD). We compare them using standard evaluation metrics such as accuracy, precision, recall, F1 score, training and prediction time, and area under the receiver operating characteristic (ROC) curve. The results show that these anomaly detection methods, particularly isolation forest, perform significantly better on unbalanced loan default data and are more suitable for real-world applications.
Federated Genetic Programming: A Study About the Effects of Non-IID and Federation Size

The privacy and security of users’ data have been a concern in the last few years. New techniques like federated learning have appeared to allow the training of machine learning models without sharing the users’ personal data. These systems have a lot of variables that can change the outcome of the models. Studies have been made to explore the effect of these variables and their importance. However, those studies only focus on machine learning models, namely deep learning. This paper explores the impact of the federation size in unbalanced data using a genetic programming model for image classification. The results show that the federation size can have impact on the contributions of each client, and the dataset size can influence the quality of the individuals.
Research on Multiple Cooperative Governance Mechanism of Telecom Fraud under the Background of Internet+

Under the background of internet plus, the high development of network technology has not only greatly promoted the social economy, but also attracted the peep of some lawless elements, and led to the characteristics of transnational, accurate, organized, professional and hidden telecom fraud cases in China.Therefore, the current telecom fraud gangs are gradually showing the embryonic form of industrial operation.If they are not stopped in time, it will inevitably lead to its large-scale expansion, which will cause serious losses to the personal property safety of the people.Therefore, this paper first analyzes the development status of domestic telecom crimes in recent years.Then, it summarizes the main characteristics of telecom fraud cases and the difficulties in detection, and puts forward the management measures of telecom fraud cases based on the multi-collaborative governance mechanism, hoping to play a certain reference role and realize the source governance of telecom fraud crimes.
Relational Exploration of different Machine Learning methods for HSI Classification

Hyperspectral imaging (HSI) captures spectral data across several electromagnetic radiation bands, making it popular.Agriculture, environmental monitoring, mining exploitation, and military surveillance use HSI.Hyperspectral imaging (HSI) data is complex and highdimensional, making analysis and interpretation difficult.Machine learning (ML) techniques for HSI categorization analyse and analyse large amounts of data, revealing relevant patterns and characteristics.This study compares hyperspectral images classification machine learning methods.This article summarises HSI classification studies using SVM, RF, CNN, and DBN.The research evaluates HSI categorization methods.PCA and ICA are used to assess HSI categorization accuracy.Feature extraction reduces data dimensionality and improves machine learning model discrimination.HSI classification research suggests more study.The work emphasises the need for robust and flexible machine learning methods to handle hyperspectral imaging data's complexity.Machine learning and feature extraction are suggested to improve hyperspectral picture categorization.
A Fuzzy Autoencoder-Based Self-Organizing Approach to a Hierarchical Fuzzy Logic System

This study on Autoencoders focuses mostly on the challenge of dealing with unpredictability in neural networks.Recently, auto encoders and deep stack of autoencoders (DSAE) have found applications in machine learning.Recent years have seen significant progress in solving the challenges of decreasing dimensionality and data compression.Autoencoders, like conventional neural networks, were deterministic architectures that struggle with coping with data uncertainty, despite their importance in many practical applications.In this research, we provide a fuzzy method for automatically incorporating qualitative fuzzy data information into the input layer, hence lessening the degree of ambiguity in Autoencoder stacks.To this end, we may add a fuzzy layer-0 to our Autoencoder stack and use it to supplement the crisp data set with some fuzzy information.The method is potentially applicable to any neural network design since it is transparent for both the network and the user.The provided findings are quite promising, and represent a significant advancement, particularly when working with noisy data..
Association Rule Mining for Market Basket Analysis on Online Retail Store

The rise of association rule mining has been attributed to the increasing amount of transactional data that has been collected and analyzed.This technique is commonly used to identify relationships and patterns in this data.This paper aims to provide an overview of the various aspects of this process and its potential applications.The literature review on association rule mining was conducted to analyze its applications in various industries.A case study was then carried out to evaluate its effectiveness in analyzing bank loan data.The findings of the study revealed that this process can be utilized to identify meaningful relationships and patterns in the data and produce recommendations and predictions.Due to the complexity of the process, it is often difficult to implement effective association rule mining techniques.This paper aims to provide a comprehensive overview of the various aspects of this process and its potential applications.In addition, it also explores the influence of certain parameters on the performance of the algorithms.The study's findings provide valuable insight into association rule mining's application in analyzing bank loan data, and it highlights the opportunities and challenges that this process presents.The conclusions of the research will be beneficial for data mining researchers and practitioners, and will help them understand more about this process and its possible applications.
CA-CMT: Coordinate Attention for Optimizing CMT Networks

Vision Transformer (ViT) has been proposed as a new image recognition method in the field of computer vision. ViT applies a Transformer structure with excellent performance in the field of natural language processing to recognize images. Unlike existing Convolutional Neural Network (CNN) models, ViT can achieve State-Of-The-Art (SOTA) image recognition without inputting Inductive Biases into the model, demonstrating that the Transformer is a useful structure in the field of computer vision. However, ViT requires large datasets such as ImageNet-21K and Joint Foto Tree (JFT) for learning. In addition, it takes a lot of time to train. Moreover, there is a problem that location information is lost by inputting images in patch units. To improve such issues, many models are being proposed. In this paper, a new model is proposed by restructuring the Convolutional neural networks Meet vision Transformers (CMT) model by applying Coordinate Attention Block, a CNN model, to improve problems of the Vision Transformer family of models. The proposed model combines Transformer, which has shown excellent performance in Long Range, and CNN, which has shown excellent performance in Local Feature, to achieve higher performance than existing models. We also compared performance of the proposed model with those of existing models with relatively small datasets such as Canadian Institute For Advanced Research-10 (CIFAR-10), Self-Taught Learning-10 (STL-10), and Tiny-ImageNet to facilitate researchers’ access to the evaluation. Despite being restructured from the smallest CMT-Tiny model, the proposed model showed better accuracy than CMT-Tiny, CMT-XS, CMT-S, and CMT-B models with CIFAR-10, STL-10, and Tiny-ImageNet datasets. The proposed model showed an accuracy of 90.21% with the CIFAR-10 dataset, higher than existing CMT models except for the CMT-S model with an accuracy of 90.6%. It had the lowest loss value of 0.3967. The proposed model is expected to be utilized as a backbone in Object Detection and Segmentation fields in the future.
Boosting for regression transfer via importance sampling

Current instance transfer learning (ITL) methodologies use domain adaptation and sub-space transformation to achieve successful transfer learning. However, these methodologies, in their processes, sometimes overfit on the target dataset or suffer from negative transfer if the test dataset has a high variance. Boosting methodologies have been shown to reduce the risk of overfitting by iteratively re-weighing instances with high-residual. However, this balance is usually achieved with parameter optimization, as well as reducing the skewness in weights produced due to the size of the source dataset. While the former can be achieved, the latter is more challenging and can lead to negative transfer. We introduce a simpler and more robust fix to this problem by building upon the popular boosting ITL regression methodology, two-stage TrAdaBoost.R2. Our methodology, S-TrAdaBoost.R2, is a boosting-based ensemble methodology that utilizes importance sampling to reduce the skewness due to the source dataset. We show that S-TrAdaBoost.R2 performs better than competitive transfer learning methodologies 63% of the time. It also displays consistency in its performance over diverse datasets with varying complexities, as opposed to the sporadic results observed for other transfer learning methodologies.
MACHINE LEARNING AND THE REUTERS COLLECTION-21578 IN DOCUMENT CLASSIFICATION

Currently, it is very easy to produce documents, which means that there is too much information, and all this information produced is almost impossible to organize if automatic methods are not used. The automatic classification of documents can be defined as an action executed by an artificial system on a set of structured or unstructured documents. This action is performed by using the words contained in the documents to define the class to which the test document belongs. This paper presents several classification experiments using the Reuters-21578 database in order to observe the performance of naive Bayes classifiers, support vector machines (SVM) and logistic regression. The results obtained show the performance of the classifiers, their behavior when applying cleaning techniques to reduce the size of the documents and different classification scenarios.