Reviewerly/exHarmony huggingface dataset:
Challenges in natural language processing and natural language understanding by considering both technical and natural domains

As deep learning became more sophisticated, it significantly increased the use of AI in industry, academia, and other sectors. NLP is a part of the deep learning paradigm that offers different types of systems mainly related to human language understanding, meaning, and interpretations. Nowadays, NLP is used in several applications, including sentiment analysis, categorization of texts, translation, etc. Due to this new usage, new challenges occurred. This paper discusses the challenges of developing or creating an NLP model and the problems that will be occurred in NLU. Moreover, the paper illustrates issues in both technical and natural domains that should be considered upon deployment or creation of NLP models or NLU systems.
Deit Model for Iranian Traffic Sign Recognition in Advanced Driver Assistance Systems

Due to the important relationship between the impact of accurate detection of traffic signs in self-driving cars and driver assistance during car movement, it is very challenging and necessary to create a high-accuracy system for interpretation and immediate decision-making. In this research, by applying the new vision transformer Deit approach, a system is designed that can recognize Iranian traffic signs. We trained our model with two collections of traffic sign images (GTSRB and PTSD) that reached higher accuracy levels of 99.5% and 98.8%, respectively, in optimal conditions.
3D Image Annotation using Deep Learning and View-based Image Features

The act of assigning word labels to images using machine learning algorithms is called automatic image annotation. Automatic annotation of image is used in various applications like media, medical, industrial and archaeological fields. Several methods have been proposed for automatic annotation of images, but most of them are focused on 2D images. In this article, we propose a new approach for 3D image annotation using deep learning and view-based image features. The most challenging issue in the automatic annotation of 3D images is to extract suitable features for image representation. 3D images are generally presented in the form of polygon meshes that are not suitable for deep learning. To counter the problem, we represent 3D images as several view-based images that are captured from different views. This process converts a 3D image into a multi-channel 2D image that can be classified using image-based deep classification networks. We utilized various classification networks for 3D image annotation, and the results showed the F1 score of 0.97 for the best architecture.
Quality Assessment of Screen Content Videos

Perceptual quality assessment has always been challenging due to the difficulty in modeling the no-linear human visual system. With the diversity in the contents of multimedia signals, the conventional methods for traditional media seems no longer satisfying. One of these emerging media, is the screen content images/videos (SCINs), Containing texts and computer generated graphics, SCVs cannot be sufficiently expressed with features designed for natural sceneries. Therefore, new researches tried to devise objective quality assessment metrics, specificly for screen contents. Recently, a dataset was proposed for quality assessment of screen content videos. Since screen contents are full of structures that spread in cardinal directions, we were motivated to employ the horizontal and vertical subbands of the wavelet transform to characterize these types of visual contents. The features were incorporated in a full-reference method that showed promising results on the publicly available dataset for SCV quality assessment. The method can bo accessed via: https://github.com/motamedNia/QASCV.
Constitutional Arguments in Presidential Veto Messages

We analyze presidential vetoes throughout the U.S. history and use algorithmic natural language processing methods to identify, encode, and classify constitutional arguments raised by veto messages. Going beyond the familiar hypothesis that modern presidents employ constitutional arguments instrumentally, we will classify those arguments as institutional, i.e., designed to promote the institutional interests of the executive, and substantive, i.e., designed to advance constitutional interpretations in line with the president’s policy preferences. We hypothesize that institutional arguments in veto messages are more similar to mainstream constitutional arguments, more likely to find acceptance, and more consistent across parties, while substantive arguments are more likely to be addressed to the public (and thus less professionalized), and less consistent across parties. We will test those hypotheses by comparing the style and semantic structure of presidential constitutional arguments with other sources of constitutional arguments, such as OLC opinions, again using NLP and machine learning algorithms. The results will be validated against a qualitatively coded subset of vetoes, including all modern ones.
Prognostic Stage Classification for Invasive Breast Cancer by Analysing Affected Lymph Node

To accurately identify the cancer stage, it is required to classify the tumor and lymph node stages from pathological and clinical data. Most of the existing model concentrate on cancer detection, whereas limited model performs the tumor and lymph node stage classification during diagnosis time. For personalized treatment prognostic stage classification of cancer is required at the time of diagnosis. The aim of the proposed study is to build a computational model which accurately classify the stages of tumor (T) and lymph node (N) at the time of detection. To do the classification the proposed model identify few crucial features (tumor size, nuclear grade, axially lymph node and so on) using recursive feature elimination (RFE). A modified feature set prepared by RFE with crucial and necessary feature of data to feed in the proposed model for classifying the tumor (T) and node (N) stages. Necessary features (such as mitotic count, tubule formation, estrogen receptor etc.) which are highly correlated with the crucial features are used to enhance the performance of the model. The performance of the proposed model evaluated on malignant data where stage-to-stage classification is performed such as tumor stages (T1, T2, T3) and node stages (N0, N1, N2, N3) uing ML and DL algorithm. Through this experiment it is observed that the proposed study outperform than other state-of-art models.
Malicious URL Classification Using Machine Learning

In this era of emerging web technologies, people are dealing with online resources at all times. Web phishing is a social engineering attack where people are tricked by cyber attackers and reveal important credentials. People type a search query and based on the query results, users generally enter into top few websites. Cyber attackers deploy different mechanisms like keyword stuffing, content cloaking to modify the rank of a webpage. In this paper, we have checked whether a website is content cloaked or not and then further classified the URL whether it is phishing or not. We have used SVM and K-NN classifier on a dataset of 11,72,598 URLs and achieved 90% accuracy score.
Deep Artificial Neural Network Based Blind Color Image Watermarking

Digital data is growing enormously as the year passes and therefore there is a need of mechanism to protect the digital contents. Image watermarking is one of the important tools for the human to provide copyright protection and authorship. For achieving the ideal balance between imperceptibility and robustness, a robust blind color image watermarking employing deep artificial neural networks (DANN), LWT and the YIQ color model has been presented. In the suggested watermarking method, an original 512-bit watermark is applied for testing and a randomly generated watermark of the same length is used for training. PCA is used to extract 10 statistical features with significant values out of 18 statistical features, and binary classification is used to extract watermarks here. For the four images Lena, Peppers, Mandril, and Jet, it displays an average imperceptibility of 52.48 dB. For the threshold value of 0.3, it does an excellent job of achieving good balance between robustness and imperceptibility. Except for the gaussian noise, rotation, and average filtering attacks, it also demonstrates good robustness against common image attacks. The results of the experiment demonstrate that the suggested watermarking method outperforms competing methods.
NFTrig: Using Blockchain Technologies for Math Education

NFTrig is a web-based application created for use as an educational tool to teach trigonometry and blockchain technology. Creation of the application includes front- and back-end development as well as integration with other outside sources, including MetaMask and OpenSea. The primary development languages include HTML, CSS (Bootstrap5), and JavaScript as well as Solidity for smart contract creation. The application itself is hosted on Moralis utilizing their Web3 API. This technical report describes how the application was created, what the application requires, and smart contract design with security considerations in mind. The NFTrig application has undergone significant testing and validation prior to and after deployment. Future suggestions and recommendations for further development, maintenance, and use in other fields of education are also described.
Benefits, Challenges, and Future Research Directions for Blockchain-Based Agri-Food Supply Chain

Agriculture is the foundation stone of the most basic lifeline, i.e., food for human beings. The food supply chain (FSC) fulfills the needs of the consumer. Food fraudulence is a major concern and affects the health of the consumer. The consumer is also cautious about quality and origin, as many lifestyle disorders are caused by adulterated food. So, traceability is a pivotal factor to ensure the origin, where it is produced, and specified quality. A novel technology needs to be implemented to build trust quotients in the food supply chain. Nowadays, agriculture has been smart using the Internet of Things (IoT). IoT captures information with sensors stored on a centralized system that can be tampered with or altered by any unauthorized access. Hence, it is not truly trustworthy. Blockchain technology has proved an efficient, decentralized, trustworthy system for each entity in the food supply chain network, which ensures originality, traceability, and quality of the product. Blockchain is a beneficial and effective technology for the FSC system. This paper provides a comprehensive survey on state-of-the-art Blockchain technology used for the food supply chain management system. This article highlights the benefits of using Blockchain technology. This paper also throws light on challenges and the relevant future directions that we can work in the food supply chain to make it more efficient and adaptable.
Extracting Data from an Image Data Set Using Image Processing Methodology

Many researchers find a problem finding a similar image or specific image type from the vast image repositories. A paradigm for multimedia data mining based on image fragmentation is provided in this research for the analysis of video semantics; more precisely, Retrieval utilizing basic properties like speed, color, and shape is supported by current content management systems. The proposed procedure extracts information using the image categorization technique, producing a more efficient output. Using image color pixel average methods helps to perform this operation more accurately; in addition to this, the categorized associations carry out a classification technique by giving each of them a class label and creating video indices based on their presence in the film. The experimental findings show that the suggested strategy is effective. The proposed system shows good and quicker retrieval of image data sets.
Teach Reinforcement Learning in Robotics Class: two case studies

Reinforcement learning (RL), as a promising approach to realize Artificial Intelligence (AI), has been widely applied in developing robots. However, the current K12 Robotics Course is still designed to focus on traditional robot technology without RL. In order to foster the transition between education and industries, a robot course integrating RL is designed in our research. We implemented the course and carried out case studies in a robot course in a school and an experience class for K12 students. The results show that most students can successfully complete the robot task based on RL under reasonable course difficulty, and most students agree the proposal of teaching RL in K12 Robotics Course. This suggests researchers and policy makers to work on updating K12 Robotics Course by adding RL section, thus to keep the AI curriculum system and content modern.
Light Field Technology Induced Variable Depth of Field for the Enhancement of Photography Learning

While learning photography, learners, especially beginners usually encounter difficulties, such as realizing the depth of fields (DoFs) and/or selecting a good shooting angle. The emergence of Light Field Technology (LFT) offers a new possibility to address the above problems. The unique imaging principle of LFT enables the reconstruction of the scene to be captured with dynamic visual effects including dynamic depth of focus and dynamic viewing parallax. In photography, the dynamic depth of focus by LFT can be utilized to simulate the variable DoFs and the dynamic viewing parallax can be employed to simulate alternative shooting angles. In this research, we established a virtual environment for photography learning by utilizing the above-mentioned dynamic visual effects with real-time gesture interaction. A user study was conducted to evaluate the learning efficiency of the learners in our virtual learning environment, the results reveal that these learners show higher learning efficiency than the learners in the control group.
Attention-LGBM-BiLSTM: An Attention-Based Ensemble Method for Knowledge Tracing

Knowledge tracing plays a vital role in measuring students’ learning behaviors. In this paper, we propose a novel ensemble model: Attention-LGBM-BiLSTM for knowledge tracing. We utilize the attention mechanism combined with LGBM (Light Gradient Boosting Machine) to obtain a feature of the most importance. Combined with the first-round outputs of LGBM, it is imported into BiLSTM (Bidirectional Long Short-Term Memory), thus obtaining the final classification results. We implement and evaluate the model based on the largest open-source dataset, EdNet, in education area. The results show that the accuracy, AUC, and Fl-score of the model are higher than its baselines. An ablation test is also conducted to prove its effectiveness.
Using chatbot as a goal-setting facilitator in an online course: students’ performance and teacher’s perception

Many institutions have been adopting a fully online teaching and learning mode due to the COVID-19 pandemic. This requires a high level of self-regulation from students. Students’ goal-setting skills play a key role in their self-regulated learning, especially at the beginning stage of learning. This study explored the use of a chatbot to guide students to set learning goals for an online undergraduate course. The social presence framework was adopted to facilitate students’ interaction with the goal-setting chatbot. A mixed-method case study design was used to evaluate students’ behavioral engagement, perceived social presence, goal-setting, and their level of satisfaction with the chatbot. The results indicated students’ positive goal-setting experience with the chatbot. Moreover, their degree of satisfaction with the chatbot significantly influenced the learning goals they set for the online course. Students’ and teacher’s interviews were conducted to gain insights into future chatbot design.
Predicting Guesses and Slips Through Question Encoding with Complexity Hints

One commonly researched adaptive learning method, knowledge tracing, supposes that a learner answering an exercise correctly does not immediately imply that they have mastered the associated knowledge component, and vice-versa for answering incorrectly. Alternative explanations to learner performance such as guesses and slips reduce the accuracy of assessment items in determining learning achievement. One possible contributor to the learner’s tendency to make guesses or commit slips is the unnecessary complexity of a problem. In this paper, the potential usefulness of complexity hints in predicting mastery of algebra problems is explored. Natural language processing methods were used to derive a complexity estimate of each assessment problem and applied to estimate guess and slip probabilities using individualized neural network models. An added important advantage of complexity estimation over previous methods for knowledge tracing is its potential contribution to assessment design by revealing which problems tend to be more confounding than helpful to learner progress. This potentially may lead to new lines of research in natural language processing applications, knowledge tracing, and curriculum quality assurance.
A Teaching and Learning Design in Nursing Education Using Mobile AR Technology

Augmented Reality (AR) technology has recently been applied to Nursing Education at Te Pūkenga - New Zealand Institute of Skills and Technology. This paper uses mobile AR technology to present an alternative teaching and learning design in nursing education. An experimental case study was completed in a revision preparation for pre-registration students for their final Bachelor’s and their State Registration Examinations. The revision sessions have been standard and classroom based for many years, and the HoloPatient applications was introduced as part of the revision process in June 2022, to prove a hybrid teaching method i.e., based on classroom teaching with HoloPatient applications remotely externs to students from their mobile phones. This case study approached a teaching platform to enable students to study synchronously in the classroom and at home. Such a design has a strong market demand in the covid-19 environment, when teaching face to face became problematic. Students faced technical problems due to a lack of face-to-face tutorials, lack of online platforms resources/ essential equipment, and continuity of classes. Indeed AR can be a useful solution for this problem. This case study has resulted in a recommendation for further development of Mobile AR in Nursing Education e.g. simplifying the Hololens headset configuration and stabilizing smart phone connection to build a user-friendly hybrid teaching platform.
Exploring the Possibility of Working in Japan for IT Engineers Who Do Not Speak Japanese

Japan is facing a shortage of human resources, and Japanese universities are becoming more open, providing many programs taught in English to attract international students to study in Japan. More international students who do not speak Japanese face the language barrier when trying to find a job in Japan. In this study, we have proven through various methods that even IT engineers who do not speak Japanese can perform IT-related tasks with native Japanese IT engineers. Moreover, use translation software enough to help them solve communication problems at work.
Conversational System for Clinical Communication Training Supporting User-defined Tasks

Effective clinical communication is essential for delivering safe and high-quality patient care, especially in emergent cases. Standard communication protocols have been developed to improve communication accuracy and efficiency. However, traditional training and evaluation require substantial manpower and time, which can be infeasible during public crises when training is most needed. This research aims to facilitate autonomous, low-cost, adaptive clinical communication training via artificial intelligence (AI)-powered techniques. We propose a conversational system for clinical communication training supporting user-defined tasks. Two data augmentation (DA) methods, term replacement and context expansion, are proposed to allow non-professional users to create Al models with a small number of samples. Equipped with biomedical ontology and pre-trained language models, our system is able to simulate clinical communication scenarios, provide timely evaluation, and adapt to new tasks with minimal editing. Various experiments demonstrate that our proposed algorithms can achieve satisfactory performance using a small amount of training data. Real-world practice in local hospitals shows that our system can provide expert-level evaluation and deliver effective clinical communication training.
Support Awareness of Anomaly in Coding Behavior using Code Revision Data

With the aim of better teaching effectiveness in programing courses, this study provides a framework to easily understand students’ coding progress using the data which is produced while they are coding. In particular, we explore the methods to find students who get stuck at coding and make little progress. In HTML courses for programing novices, we collected code revision history including the amount of code update and save times of students. In this paper, we discuss the usefulness of code revision history to understand students’ coding progress by describing coding patterns revealed by the analysis of the data, characteristics of coding progress and scalability for programing courses.
Student-Oriented Reform of Microelectronics Courses under the Background of Emerging Engineering

In order to meet the needs of the country, society and enterprises for high-skilled talents who have mastered the advanced process knowledge of modern microelectronics enterprises and integrated circuit design technology, combined with the innovative concept of "emerging engineering", we should actively promote the practice teaching reform of microelectronics and promote emerging engineering. Diversified development of microelectronics professional personnel training. Combined with the innovative concept of "emerging engineering", this paper carefully analyzes the new trend of engineering development, the learning characteristics of digital natives, and the problems existing in the practical teaching of microelectronics. Starting from promoting students’ personalized learning, an intelligent learning system is proposed to improve students’ practical innovation ability and explore and cultivate modernization.
Using Virtual Reality to Enhance the Teaching of Kiln Firing of Ceramic Products

Ceramic kiln firing is an important practical component of product design courses. However, in classroom teaching, students learn poorly due to realistic factors. This research project developed a virtual experiment platform for ceramic product kiln firing based on constructivist learning environment elements using virtual reality technology to enhance students’ learning. This study conducted a preliminary case study with 20 product design students at the South China University of Technology to assess the learning effects. The study used pilot tests, questionnaires, and interviews to evaluate the usability of the virtual experiment platform in terms of three dimensions: effectiveness, efficiency, and satisfaction. The results show that the constructivism-based virtual experiment platform improves the teaching effectiveness of kiln firing of ceramic products and can serve as a helpful supplement to classroom teaching.