Reviewerly/exHarmony huggingface dataset:
Mobile Learning-Based Literature Retrieval Ability Evaluation Method and Teaching Practice

This paper discusses an evaluation method of the literature retrieval ability based on mobile learning. A WeChat applet is developed which consist of three modules, i.e., the student ability assessment module, the literature submission module and the literature scoring module. Then the proposed method applied in a scientific research training course as a teaching practice. The course evaluation of students is conducted in the form of “online grading and course report”. The method in this paper gives a new path for evaluating and improving the literature retrieval ability of students.
Styled-QReal: A real-time technique for QR code stylisation

Today QR codes are one of the most widely used scannable patterns, especially in the digital payments ecosystem in India. Traditionally QR codes are made up of randomly appearing black and white square pixels that don’t carry any visual meaning to the normal human being. Hence, this motivates the recent works on beautifying the visual aesthetics of these QR codes. In this paper, we have used the neural style transfer technique to propose an algorithm to generate scannable and robust stylised QR codes in real-time with artistic visual quality, suitable for real-world applications. Also, the experiments performed on the generated QR codes reveal that our QR codes have visual quality at par with existing methods while having better scanning robustness.
Research on Debonding Degree of Building Exterior Wall Bricks Based on ISSA-KELM Algorithm

In recent years, the building exterior wall tiles falling off caused a lot of damage to property and injuries, in order to achieve more accurate off, put forward using percussion method to extract the sound signal characteristics combined with pattern recognition method for building exterior wall tiles debonding degree were studied. In order to improve the prediction accuracy of the debonding degree of building exterior brick, a prediction method of the debonding degree of building exterior brick based on the improved sparrow search algorithm (ISSA) and optimized kernel extreme learning machine (KELM) was proposed. In order to overcome the disadvantages of the standard sparrow search algorithm(SSA), such as uneven population distribution and easy to fall into local optimum, the algorithm firstly uses Circle chaotic map to generate the initial population with uniform distribution, and then uses the elite opposition-based(EOBL) learning strategy to broaden the search domain of the algorithm, obtain elite sparrows, avoid algorithms falling into local optimality. The KELM parameters were optimized by ISSA algorithm, and a prediction model based on the degree of brick debonding outside KELM was established. The characteristic data of sound signal collected from building exterior brick of different degree of debonding were used to study. The results indicate that the proposed method is superior KELM, PSO-KELM, GWO-KELM and SSA-KELM algorithms, and has certain applicability to detect the degree of debonding of building exterior brick.
Handwritten Character and Digit Recognition with Deep Convolutional Neural Networks: A Comparative Study

Handwritten character or digit recognition involves automatically classifying handwritten characters or digits from images. Previous studies focused on specific datasets and did not thoroughly compare different CNN architectures. This paper addresses these limitations by presenting a comparative study of six popular CNN architectures (VGG16, Xception, ResNet152V2, InceptionResNetV2, MobileNetV2, and DenseNet201) on three diverse datasets: English Handwritten Characters, Handwritten Digits, and MNIST. The experimental results demonstrate that the InceptionResNetV2 model with data augmentation achieves the highest accuracy across all datasets, with accuracies of 93.26%, 97.16%, and 99.71% on the English Handwritten Characters, Handwritten Digits, and MNIST datasets, respectively.
Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey

Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article.
Vehicle Target Detection in Aerial Images Based on Improved YOLOv5

With the increasing application demand of UAV aerial images in land surveying and mapping, agricultural production, traffic management, target detection of UAV aerial images is of great significance. It is difficult to detect small targets due to the problems of low resolution, complex background, dense targets and small scale in aerial images. In this paper, YOLOv5s is used as the benchmark model to realize vehicle target detection in aerial images of unmanned aerial vehicles. Firstly, we selected VisDrone2019-MOT dataset to detect targets, and Retinex image enhancement algorithm is used for image preprocessing. By increasing the image contrast and improving the resolution of aerial images, the vehicle target is more prominent, and the detection accuracy is improved. Secondly, aiming at the unfavorable factors such as high vehicle target density, small vehicle size, limited resolution, complex background and illumination influence, the data selected in the training process, and the high-resolution aerial image with high density and high complexity from vertical perspective is selected to construct Dataset. Finally, the prepared data set is used to train and detect the vehicle target detection model of UAV aerial images with YOLOv5s as the benchmark model, and the detection accuracy of the final test set is as high as 86.4%. The image processing method proposed in this paper can achieve high accuracy of vehicle target detection in UAV aerial photography.
On Testing and Evaluation of Artificial Intelligence Models

Artificial intelligence (AI) modes have been successfully applied in several areas like computer vision, and intelligent manufacturing. They have great potential to improve the efficiency and security of power control systems. Before selecting and applying an appropriate AI model in a commercial power control system, it is necessary to test all candidate AI models and evaluate their performance. However, there are few studies on the systemic process of testing AI models and evaluating their performance. To this end, this paper presents a novel AI model testing and evaluation framework and introduces the AI model performance evaluation metrics and methods. Then, the AI model’s testing process is described, and the methods of AI model training, model testing, result analysis, and model evaluation are described in detail. To facilitate the understanding of AI model evaluation methodology and for the task of malicious traffic identification, we give a specific case to show the testing and evaluation performance of the proposed text CNN and other benchmark deep learning models in terms of efficiency and accuracy.
Design and Implementation of Industrial Auxiliary System Based on Hololens

In order to solve the problem of untimely and incomplete information acquisition of production site staff, this paper proposes an industrial auxiliary system design method based on Hololens, analyzes the overall design framework of industrial auxiliary system, constructs the functional modules of the system, and takes the flexible assembly line as an example for research and development. Based on the information visualization demand analysis of the manufacturing integration platform, the overall architecture and functional modules of the industrial auxiliary system are designed. The wearable AR device is used as the information display device, and human-computer interaction is carried out by means of gesture interaction and voice interaction. Wireless communication is adopted to realize information transmission between AR equipment and production line equipment. The registration and tracking method based on improved ORB algorithm is adopted to realize the auxiliary assembly function. The system has good immersion and interactivity, and effectively improves the efficiency of information acquisition of production site personnel. The practical application effect is significant.
Learning with Explainable AI-Recommendations at School: Extracting Patterns of Self-Directed Learning from Learning Logs

Educational explainable AI (XAI) applications are gaining research focus and have distinct needs in the domain of Education. This research presents Educational eXplainable AI Tool (EXAIT), a system for math quiz recommendations, along with an explanation. EXAIT was implemented in a Japanese public high school where students received the top 5 math problems based on Bayesian Knowledge Tracing (BKT) algorithm in a learning analytics dashboard. It aimed to help them complete their summer vacation assignments having 240 questions. On click, the students were redirected to an eBook platform to submit their accuracy and confidence level in each problem. We conducted a study with a quasi-experimental design and divided into 3 groups based on compliance of use. RecoExp group received and used explanations regarding why an item was recommended and how it aims to maximize learners' knowledge-gaining path. RecoCon was the control group that received just the recommendations and used it and RecoNone group did not use the system at all during the time period. We provide a framework to analyze learning logs from EXAIT and extract emerging self-directed learning patterns. Analyzing 222 students' EXAIT logs, we found learners who had checked explanations while selecting recommendations had significantly higher performance. Further differential process mining highlighted significant active daily engagement transitions of the RecoExp group in the self-directed activity.
A Preliminary Study of Wisdom Teaching for “Information Technology” Course

Smart education has become an important direction of future education, but the improvement and practice of wisdom teaching is a long process. In this paper, we present the available teaching platforms that support wisdom teaching of Jinan University, and carry out teaching implementation, show the effect of the initial exploration of the wisdom teaching of the “Information Technology” course.
Code Recommendation Based on Deep Learning

Software developers, especially programming beginners, often need to complete some unfamiliar coding tasks, generally by reusing the previous code or searching the existing code snippets, and from a large number of redundant search results to screen out the code with similar structure to complete the coding task. In order to quickly and accurately recommend high-quality code snippets for programmers, this paper Sentence-BERT model. Firstly, the method obtains a large amount of high-quality data to build the code base, and proposes a code snippet recommendation method based on generates sentence vectors with rich semantics for the natural language description corresponding to the code snippets and the natural language query input by users based on the deep learning model SBERT. Then, the code snippets are recommended by comparing the dot product similarity. The effectiveness of the proposed method is verified by comparing the three commonly used evaluation indexes of hit rate, average reciprocal ranking and average accuracy with the methods in the current related research. Experimental results show that this recommendation method can effectively improve the accuracy and effect of code snippet recommendation.
Artificial Intelligence Indulgence in Banking and Financial Sectors

Banking and financial institution are evolving more rapidly than other industries; artificial intelligence plays a virtual role. It offers applications such as cyber security and fraud detection, chat bots, aids in loan and decision making, and protects customer information in a proactive manner. This study suggested that artificial intelligence be thoroughly incorporated into the financial industry in order to enhance service quality and accessibility and to create novel roles that encourage healthy competition among the competitors. This study has recommended that AI be fully integrated into the financial sector to improve service quality and accessibility, as well as to develop unique roles that promote healthy rivalry among rivals, this paper aims to discuss the significance of technologies in the zone of banking industry. Based upon the analysis it has been concluded that these technologies assist to cyber security and fraud detection, chat bots’ loan and credit decision, tracking market trends, data collection and analysis, customer experiences, risk management, regulatory compliance, predictive analytics, process automation.
Bi-Mapping Between RDF and Property Graphs

The existing solutions of RDF mapping to Property Graph can only complete one-direction mapping, which leads to the difficulty of knowledge reuse in the domain of knowledge graph. This paper proposes a solution based on mapping structure to convert RDF to Property Graph, and compares it with Neosemantics from three aspects: computability, information preservation and semantic preservation. It is proved that the solution proposed in this paper has better universality and higher efficiency in the process of bidirectional mapping and improves the circulability and reusability of knowledge in the knowledge storage domain.
Junior Lecturers' Perspectives of Emerging Technologies as a Viable Teaching and Learning Tool in Higher Learning Institutes

The global Covid-19 pandemic witnessed unprecedented challenges in all aspects of our life, including higher education. One of the key challenges was the fact that lessons were delivered almost on the fly, with both lecturers and students adjusting to the flow of the teaching and learning (T&L) process. This consequently witnessed the integration of a variety of emerging technologies including virtual realities, artificial intelligence, adaptive learning algorithms, live streaming and cloud platforms in the T&L process. Therefore, this study examines emerging technologies employed during the post-pandemic T&L process of two junior lecturers, from two universities in Malaysia. The two lecturers were selected based on purposive sampling due to their competency in utilising emerging technologies in the classroom. The data were collected via semi-structured interviews and analysed using thematic analysis. The findings indicated that both lecturers demonstrated their competency to integrate emerging technologies into their classrooms and they were able to enhance the interaction between instructor, students and content. The findings also disclosed some challenges faced by lecturers in employing emerging technologies in the classroom. This study has implications for the integration of emerging technologies as viable T&L tools and raised issues of concern that need to be addressed to better strategize the integration of emerging technologies in the T&L in universities during the post-pandemic era.
Three layered Multimodal Biometric Authentication on Smartphone using Keystroke Dynamics and Touch Dynamics

Data theft and other cell phone crimes are increasing day by day. Unauthorized access can be controlled through the strong authentication process. Authentication plays a vital role in smartphones as everyone stores all kind of data on smartphone due to comfort and accessibility feature it provides. Layered based multimodal biometric authentication is a great technique where it is compulsory for the users to go through all the layers to use the smartphone application. The proposed M3AS system is based on three layered multimodal biometric authentications. In this system the author used three techniques for all three layers of authentication i.e., Id-Password, Keystroke Dynamics, and Touch Dynamics. Before login the application user goes through with the first two-layer sand after login the application user’s identity will be checked all over the session using touch dynamics in third layer. To check the accuracy the author collected keystroke and touch features from 60 users at three different times. FAR, FRR and EER analysis was done to verify the proposed systems’ authenticity. FAR is 8.33%, FRR is 14.90% and EER is 11.61%. The proposed system achieved the accuracy 92%.
Scrutinization and Abstraction of Unstructured Electronic Health Records using Natural Language Processing and Deep Learning

The healthcare industry produces patients' medical records in great numbers on daily basis. In hospitals, these records are generated in both organized and unstructured formats. EHR commonly called Electronic Health Records are the digital copy of the medical summary of the patient, medical treatment, lab reports of the patients, and their medical history. Electronic health records hold an enormous amount of data which is found useful for research in the medical field but the limitation is that it takes time to process those data. To overcome this disadvantage, we are introducing the deep learning concept of Recurrent Neural Network (RNN) for rapid analysis in EHRs. And the other limitation is that due to the presence of medical entities in EHR, these are hard to process. To overcome this limitation NLP is used for the automatic processing of records. NLP has shown improvement in the performance of processing a large amount of data. The proposed work is about combining the knowledge of deep learning approaches with Natural Language Processing to handle the EHR datasets to enhance the already existing methods in an EHR system. In this paper, we study the patient's unstructured EHRs and propose a novel algorithm named Clinical-NERT. We predict the patient's medical conditions based on the symptoms they experience. We train our model with the degree of symptoms for the disease along with its severity. Here, deep learning techniques are used to provide a way to boost the performance of the system by enhancing the precision of the decision-making process regarding patients' disease prediction by processing the vibrantly rich information from the medical records.
Optimization Techniques for Car Price Prediction

The optimization is a key concept in machine learning, it can be used to find the best parameters for a machine learning algorithm, or to find the best way to solve a problem. Price determination for a secondhand car is typically a laborious and error-prone process if done manually. However, this method would be made simpler and more adaptable by applying machine learning techniques to the issue. This can be viewed as a prediction task using machine learning, where the model will be trained on previously collected data while also emphasizing the factors that impact price most. This study compares the effectiveness of various machine-learning algorithms and optimization techniques, and it suggests a novel machine-learning strategy paired with optimization algorithms for car price prediction. This kind of regression problem can be solved using a variety of machine-learning methods. These techniques can be used in conjunction with optimization algorithms to raise the model's overall prediction efficiency and accuracy. In this work, we tried seven optimization algorithms for model building and find the effectiveness of those algorithms. Finally using the Genetic algorithm and Random forest we achieved an R2 score of 0.91, a Mean absolute error of 01.49, a Mean squared error of 10.27, and a Root mean squared error of 03.20.
A Robust and Secure Image to Multi-Images Steganography

With the enhancement in technology sharing of information in different forms like text, images or videos is being adopted worldwide. But this sharing of information also led to cybercrime, to overcome this image steganography approach has been widely used in which messages in the form of text, image or video can be enclosed within another image which will be invisible to other users. This research paper proposed an enhanced method of image steganography by concealing the secret image within several cover images, each of which conceals a portion of the secret image that has already been encrypted with a key. By partially blending into these cover images, secret images can be transmitted through comparable or other open channels more reliably and securely.
Research on Human Activity Behavior Recognition Technology Based on LGBM Model

Human activity behavior recognition technology is widely used in medical health, fall detection and exercise training. This paper constructs a model to solve the problem of classifying human activity behavior recognition by miniature inertial sensors at different positions of the body. First, after data processing and analysis, we constructed the LGBM model. Later, we verified the ability to generalize the excellent results of the model to identify the input data by adjusting the training data. Finally, we study and address the problem of possible overfitting of the model.
Movie Recommendation System and Data Analysis

We are living in a digital era, nowadays everything starting from shopping to entertainment is easily accessible online to all the age groups, thanks to the user-friendly interface. This technological advancement was maximized during COVID-19 and continues to flourish till now. But this development made about 29.1 percent of the population that consists of children under the age of 18 vulnerable to movies, shows having illegal and adult content. Not only this, even for adults it can be a tiresome process to scroll through a long list of movies on sites just to find that one movie of your taste. In such cases, a recommendation system will help the user filter out the content easily. In this paper, we have done detailed data analysis over the IMDB movie dataset and created a recommendation system where users can look for movies on the basis of actor, director, genre, language etc. This system works using the concept of correlation and feature engineering.
Feature Importance on Text Analysis for a Novel Indonesian Movie Recommender System

With the increasing use of streaming services such as Netflix, Disney Plus, and Prime Video, a movie recommender system is an important tool for service providers and customers. However, there is no specific recommendation system for Indonesian films. Our research aims to improve the performance of the Indonesian film recommendation system with text analysis and feature importance. The first step is to get the Internet movie database (IMDb) Indonesian Movies dataset from Kaggle. We then conduct a text analysis that utilizes several stages, one of which is the bag of words (BoW) process. We apply feature importance to re-optimize the dataset. The following step compares three state-of-the-art classification models, SVM, naïve Bayes, and logistic regression, in predicting the Indonesian movie recommendation system. The metric we use to measure the performance of each model is the accuracy obtained by the cross-validation method. The test results show that out of 12 scenarios involving text analysis, feature Importance, and three prediction models for movie recommendation systems, the model with the best performance is naïve Bayes using text analysis and feature importance. The accuracy is 0.87 using K-fold cross-validation, with K utilized at 10.