Reviewerly/exHarmony huggingface dataset:
Dynamic E-Authentication Attendance System Using QR Code and OTP

The use of QR code-based total generation and packages has grown to be famous in recent years, with QR codes being diagnosed as a realistic and appealing records presentation/processing mechanism among users around the world. The motive of the system is to broaden and enforce any other—thing authentication machine using QR codes, in addition, to create the ideal mechanism and method that may be greater handy and practical than one-hour devices used for similar functions. Today. The proposed version is designed to carry out login validation and validation steps with a couple of safety and community connections. The proposed model changed into carried out to broaden a—component identity verification gadget, in which the second element is the person's cope, which can be accessed from a PC or cellular tool, and an alphanumeric QR code generated this is used at one time. The password will be dispatched to the user by way of e-mail. The proposed model using Java and MySQL languages developed symmetric and asymmetric cryptography standards such as encryption/hashing and network database infrastructure, and it became tested as a prototype wherein the promised outcomes are located in phrases of performance, pace and safety necessities.
Proposed Convolutional Neural Network with OTSU Thresholding for Accurate Classification of Handwritten Digits

In recent years, various handwritten digit identification techniques were introduced for practical applications needing full identification correctness and dependability. It is an essential aspect of the growth of the economy and financial services. A CNN is a class of deep learning models with several convolutional, and max-pooling layers suggested in this research paper. In the preparation of the MNIST dataset, an OTSU threshold is also implemented. Since then, several machine learning and pattern recognition concepts have been tested on this dataset. To provide a sustainable solution and attain higher performance parameters, the preprocessing also aids the model’s performance. The CNN model has been assessed by utilizing a confusion matrix and other performance criteria depending on the confusion matrix values. The suggested CNN model has a 98% accuracy rate. With 98% precision, 97% sensitivity, 95% specificity, and 96% FI score, the CNN model also performs exceptionally well in other performance criteria.
VAARTA: A Secure Chatting Application Using Firebase

Chat applications are increasingly replacing earlier communication methods such as the cell phone, landline and telegraph. People can use chat applications in various fields such as networking, commercials, and education. The ability to send and receive text messages is the most basic requirement for chatting, but current editions include voice and video chats and also allow us to share multimedia as well. End-to-end networking has recently gained popularity for a variety of reasons, and as a result, it is now taken into account when developing chat programmes such as WhatsApp. This internet platform allows users to converse online which is more secure and efficient than other available traditional methods. The encryption scheme in the authentication algorithm is combined with a message authentication code to provide better security services to the user. VAARTA is combined with AES that uses an authenticated encryption method and helps in attaining a more secure environment, better applicability and maintain its performance even in stressful conditions. Being a lightweight programme, AES VAARTA is apt for use, making it appropriate for merging into an Android chat app. VAARTA is developed on Android Studio IDE using Java as its core programming language. The Firebase NoSQL database is being utilised by the VAARTA chat application.
Classification Performance of Extreme Learning Machine Radial Basis Function with K-means, K-medoids and Mean Shift Clustering Algorithms

Extreme Learning Machine (ELM) is a feed-forward neural network with one hidden layer. In its modification called ELM Radial Basis Function the input data is a priori clustered into a number of sets represented by their centroids. The matrix of distances between each sample and centroid is calculated and applied as input data to the neural network. This work conducts a comparison study of the ELM Radial Basis Function classification performance upon applying either k-means, k-medoids or mean shift clustering methods. Generated results are obtained from two datasets i.e. Wine Quality-White and Ionosphere. The computations are based on full datasets or on the same both sets reduced by a feature selection algorithm. The parameters of the classifiers such as number of neurons in hidden layer, the value of k in k-means and k-medoids, the value of radius in mean shift are optimized through an iterative procedure upon maximizing an accuracy or minimizing Mean Square Error and computation time. The different distance metrics for k-means and k-medoids, and mean shift with Gaussian or flat kernel function are also compared. The results obtained with Softplus and linear activation function (applied in most of the computations in this work) are juxtaposed with the results generated by other activation functions.
Natural Language Processing Workload Optimization Using Container Based Deployment

BERT stands for Bidirectional Encoder Representations from Transformers. It is a pretrained model that sets condition for both the left and the right context to pre-train deep bi-directional representations from the unlabeled text. This allows us for a broad perspective of NLP works to be finetuned by the use of just one marginal output layer. The GLUE Benchmark is a set of resources which is used to train, assess and analyze the natural language in order to understand the systems with the final goal of stimulating research into the growth of general and reliable NLU systems. BERT, or Bidirectional Encoder Representations from Transformers, is a pre-training method for deep learning networks. It was developed by Google AI researchers and first released in late 2018. BERT has quickly become one of the most popular methods for natural language processing (NLP) tasks such as text classification, translation, and sentiment analysis. The aim of this research is to fine tune the BERT model so that it can perform GLUE tasks in NLP workloads (Dewangan et al. in IET Commun 15:1869–1882, 2021 [1]) such as CoLA (Corpus of Linguistic Acceptability), SST-2 (Stanford Sentiment Treebank), MRPC (Microsoft Research Paraphrase Corpus), QQP (Quora Question Pairs2), MNLI (Multi-Genre Natural Language Inference), QNLI (Question-answering Natural Language Inference), RTE (Recognizing Textual Entailment) and WNLI (Winograd Natural Language Inference). These are all important NLP tasks that have been used to evaluate a variety of different models. So far, the results have been promising. The BERT model has achieved state-of-the art performance on many GLUE tasks when compared to other pre-trained models such as XLNet and GPT2. This suggests that the BERT model may be a good choice for applications where natural language understanding is required.
Database of Fragments of Medieval Codices of the 11th–12th Centuries – The Uniqueness of Requirements and Data

This paper presents a new offline dataset called the Fragments of Medieval Codices (FOMC). It contains medieval Latin handwritings coming from 11th–12th century and can be used to evaluate the performance of offline writer identification and to find the handwriting similarity between the writers, or to test the handwritten optical character recognition systems. It consists of 117 fragments of handwritten documents of medieval codices and contains in total over two thousand very high quality images. The collection was assembled using the IIIF standard. We describe the collecting and processing steps performed to develop the dataset and define several evaluation tasks regarding the use of this dataset.
Using Simple Text Mining Tools to Power an Intelligent Learning System for Lengthy, Domain Specific Texts

This paper describes the development of a system for tracking the opportunities for vocabulary and conceptual learning within lengthy subject area texts. Specifically, we leveraged text mining tools to observe the cumulative frequency of words throughout a novel and chapters from two college-level textbooks. We describe how cumulative lexical occurrence and frequency information may be made accessible and useful to researchers and educators without the need for them to have deep knowledge of NLP or other computational linguistic skills. We applied these tools to three different types of texts, a narrative novel, and two textbooks and provide some preliminary results. Finally, we describe potential methods of using this information to structure learning activities utilizing topical analytics and automatic item generation techniques. By leveraging cumulative lexical frequency throughout a text, researchers and educators may be able to isolate words that may be both difficult and important in comprehending any given text, as well as building domain-specific knowledge.
Analyzing Response Times and Answer Feedback Tags in an Adaptive Assessment

While many learning and assessment models focus on the binary correctness of student responses, previous studies have shown that having access to extra information—such as the time it takes students to respond to a question—can improve the performance of these models. As much of the previous work in this area has focused on knowledge tracing and next answer correctness, in this study we take a different approach and analyze the relationship between these extra types of information and the overall knowledge of the student, as measured by the end result of an adaptive assessment. In addition to looking at student response times, we investigate the benefit of having detailed information on the responses in the form of answer feedback tags from the adaptive assessment system. After using feature embeddings to encode the information from these feedback tags, we build several models and perform a feature importance analysis to compare the relative significance of these different variables. Although it appears that the response time variable does contain useful information, the answer feedback tags are ultimately much more important to the models.
Automatic Slide Generation Using Discourse Relations

Slides are frequently used in educational situations such as lectures and presentations as a means of quickly and concisely conveying information. Automatic slide generation from documents has recently been studied to reduce the workload of creating slides. However, the generated slides in existing studies have their text as bullets on the same level, without any indentation or text layout as in slides created by humans. We propose automatic slide generation with a layout that reflects the structure of input documents. Specifically, we utilize a fine-tuned BERT classifier to predict the discourse relation of each sentence pair in a document. The evaluation results show that the trained discourse relation classifier achieves higher accuracy than the model used in the existing method. Moreover, combining keyword extraction and automatic text summarization, the proposed method is able to automatically generate slides with layouts considering discourse relations in a document.
Warming up the Cold Start: Adaptive Step Size Method for the Urnings Algorithm

Adaptive learning systems (ALS) tailor educational material to the level of the users. In ALS ability should be continuously estimated based on the users’ responses to adaptively selected practice items. However, the large-scale, adaptive, and dynamic nature of ALS poses challenges for traditional estimation methods. The Urnings algorithm [1] has been recently proposed to address these challenges. However, the original algorithm does not address the cold-start problem which ALS suffer from: Initially, it is difficult to adapt item selection to the users’ abilities based on limited available information. We develop a modification of the Urnings algorithm aiming to alleviate the cold-start problem by increasing the step size of the algorithm when a systematic change in the ratings is detected, and decreasing it when the ratings are relatively stable. The results of our simulation studies showed that the modified algorithm moves away from the initial values faster, responds to sudden changes in ability better, and results in overall higher accuracy than the original algorithm.
Data Augmentation with GAN to Improve the Prediction of At-Risk Students in a Virtual Learning Environment

In this paper, we explore the use of data augmentation through generative adversarial networks (GANs) for improving the performance of machine learning models in detecting at-risk students in the context of e-learning institutions. It is well known that balancing datasets can have a positive effect on improving the performance of machine learning models, especially for deep neural networks. However, undersampling can potentially result in the loss of valuable data, so data augmentation seems to be more meaningful solution when the dataset is relatively small. One of the most popular data augmentation approaches is the use of GAN networks due to their ability to generate high-quality synthetic samples that belong to the distribution of the original dataset. On the other hand, detecting at-risk students is a hot topic in learning analytics, and ability to detect these students early with high accuracy enables e-learning institutions to take necessary steps to motivate and retain students during the course. We apply this approach to the OULA dataset, a commonly used dataset in learning analytics that includes labeled at-risk students. The OULA dataset is not highly-imbalanced, making it more challenging to improve model performance through these techniques.
Learning from Auxiliary Sources in Argumentative Revision Classification

We develop models to classify desirable reasoning revisions in argumentative writing. We explore two approaches – multi-task learning and transfer learning – to take advantage of auxiliary sources of revision data for similar tasks. Results of intrinsic and extrinsic evaluations show that both approaches can indeed improve classifier performance over baselines. While multi-task learning shows that training on different sources of data at the same time may improve performance, transfer-learning better represents the relationship between the data.
Identifying Usability Challenges in AI-Based Essay Grading Tools

Automated Essay Scoring (AES) efforts have recently made it possible for platforms to provide real-time feedback and grades for student essays. With the growing importance of addressing usability issues that arise from integrating artificial intelligence (AI) into educational-based platforms, there have been significant efforts to improve the visual elements of User Interfaces (UI) for these types of platforms. However, little research has been done on how AI explainability and algorithm transparency affect the usability of AES platforms. To address this gap, a qualitative study was conducted using an AI-driven essay writing and grading platform. The study involved participants of students and instructors, and utilized surveys, semi-structured interviews, and a focus group to collect data on users’ experiences and perspectives. Results show that user understanding of the system, quality of feedback, error handling, and creating trust are the main usability concerns related to explainability and transparency. Understanding these challenges can help guide the development of effective grading tools that prioritize explainability and transparency, ultimately improving their usability.
Simulating Learning from Language and Examples

Simulations of human learning can be used as computational models for evaluating theories of learning. They can also be taught interactively to author intelligent tutoring systems. Prior simulated learner systems have learned inductively from worked examples and correctness feedback. This work introduces a mechanism where simulated learners can also learn from natural language. Using a neural grammar parser with additional symbolic processing steps, we simulate the production of loose interpretations of verbal instructions. These interpretations can be combined with worked examples to resolve the ambiguities of either form of instruction alone. We find that our system has practical benefits over an alternative method using github Copilot and slightly better accuracy.
Conducting Rapid Experimentation with an Open-Source Adaptive Tutoring System

Intelligent Tutoring Systems have been an area of particular relevance and importance to AIED. In this tutorial, we introduce uses of a new tool to accelerate the speed at which the community can innovate and experiment in the general area of computer tutoring systems. We showcase the field’s first fully fledged and open-source adaptive tutoring system (OATutor) with a completely creative commons licensed problem library based on popular open-licensed algebra textbooks. We demonstrate, with hands-on tutorials, how the system can be deployed on github.io and used to help AIED researchers rapidly run A/B experiments, including how to add or modify content in the system, analyze its log data, and link OATutor content to learning management system with LTI. Our open-sourcing of three textbooks worth of questions and tutoring content in a structured data format (JSON) also opens up avenues for AIED researchers to apply new and legacy educational data mining and NLP techniques.
Emotionally Adaptive Intelligent Tutoring System to Reduce Foreign Language Anxiety

Quality education is associated with two necessary factors: learners’ cognitive and emotional states. An adaptive system that takes the emotional state into account can enhance learning. We implemented an emotionally adaptive intelligent tutoring system that detects foreign language anxiety and provides appropriate intervention as needed. Note that the current experiment is still ongoing, and the results reported here are based on the data we currently have. We compared the adaptive approach with fixed feedback strategies. Our preliminary results revealed a statistically significant effect of using adaptive feedback to reduce foreign language anxiety. Also, we found a statistically significant improvement in the learning gains with a moderate effect size when using adaptive feedback.
Enhancing the Automatic Identification of Common Math Misconceptions Using Natural Language Processing

In order to facilitate student learning, it is important to identify and remediate misconceptions and incomplete knowledge pertaining to the assigned material. In the domain of mathematics, prior research with computer-based learning systems has utilized the commonality of incorrect answers to problems as a way of identifying potential misconceptions among students. Much of this research, however, has been limited to the use of close-ended questions, such as multiple-choice and fill-in-the-blank problems. In this study, we explore the potential usage of natural language processing and clustering methods to examine potential misconceptions across student answers to both close- and open-ended problems. We find that our proposed methods show promise for distinguishing misconception from non-conception, but may need further development to improve the interpretability of specific misunderstandings exhibited through student explanations.
A SHAP-Inspired Method for Computing Interaction Contribution in Deep Knowledge Tracing

Deep knowledge tracing (DKT) consists of predicting the probability of correctly answering a test or quiz question using the history of a particular learner’s previous question-answer interactions. The probability of a correct answer is computed using a complex recurrent neural network. In this work, an approach similar to Shapley Additive exPlanations (SHAP) to better understand DKT was used. The number of skills a learner must master to lead to improved learning outcomes in an explainable manner was first reduced. Then, the impact of subsequences rather than every single interaction is studied, as simpler results are expected to be easier to understand. Results help to highlight both subsequences in which the student acquired knowledge and in which its progress stagnated.
Evaluating a Conversational Agent for Second Language Learning Aligned with the School Curriculum

Interaction and its underlying processes have long been considered foundational to second language learning. Language learners, however, are often presented with few opportunities for meaningful, interactive practice. The rapid advancements in speech and language technologies in recent years have given rise to a number of conversational agents seeking to address this need within the interdisciplinary research area of dialogue-based computer-assisted language learning (CALL). While dialogue-based CALL has been shown to foster language proficiency development, it is crucial that a system is evaluated from multiple dimensions, from the technological sufficiency of the underlying components to learning and intercultural outcomes. In this paper, we present the plans and status of three studies targeting different aspects of a conversational agent, Aisla, developed for use in German secondary schools and aligned with the linguistic and intercultural goals of the English as a foreign language curriculum.
Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models

Large language models have recently been able to perform well in a wide variety of circumstances. In this work, we explore the possibility of large language models, specifically GPT-3, to write explanations for middle-school mathematics problems, with the goal of eventually using this process to rapidly generate explanations for the mathematics problems of new curricula as they emerge, shortening the time to integrate new curricula into online learning platforms. To generate explanations, two approaches were taken. The first approach attempted to summarize the salient advice in tutoring chat logs between students and live tutors. The second approach attempted to generate explanations using few-shot learning from explanations written by teachers for similar mathematics problems. After explanations were generated, a survey was used to compare their quality to that of explanations written by teachers. We test our methodology using the GPT-3 language model. Ultimately, the synthetic explanations were unable to outperform teacher written explanations. In the future more powerful large language models may be employed, and GPT-3 may still be effective as a tool to augment teachers’ process for writing explanations, rather than as a tool to replace them. The explanations, survey results, analysis code, and a dataset of tutoring chat logs are all available at https://osf.io/wh5n9/ .