Reviewerly/exHarmony huggingface dataset:
Exploring the Causes and Countermeasures of Cyber Violence in the Internet Era

In this current Internet era, the Internet is very popular and almost everyone is using it. However, some problems have arisen from this, and cyber violence is one of them. Today, cyber violence is happening more and more. Therefore, this article will study the causes of cyber violence and the countermeasures for cyber violence. This study provides an insightful and comprehensive study of cyber violence and broadens the research on cyber violence. Finally, it is found that the causes of cyber violence are: First of all, The virtuality and anonymity of the Internet. Secondly, some information media and netizens disseminate information irrationally. Thirdly, the relevant laws and regulations are not perfect. Fourthly, the supervision of Internet platforms is not in place. Fifthly, Internet users are getting younger. The countermeasures for cyber violence are: Firstly, improve the regulatory policies and laws for cyber violence. Secondly, the Internet platform should improve the regulatory strategy. Thirdly, improve the network public opinion supervision and platform reporting system. Fourthly, strengthen the early warning of public opinion and the guiding role of mainstream media. Fifthly, improve the rule of law and moral cultivation of Internet users.
Copyrights Infringement Risks in AI-generated Cover Songs: An Analysis Based on Current Legislation

In the age of Big Data, the accelerated iteration of AI models characterized by ChatGPT has fueled both excitement and fear. With the diversified application of generative AI models, last year witnessed a surge in AI cover songs. Compared with the discussions around the content generated by AI, the prior training process receives less attention. This article aims at analyzing the copyright law issues related to AI cover songs. By examining current legislation in different countries, the author believes that the training of AI cover models risks violating both property rights and moral rights stipulated in copyrights laws. Moreover, it fails to fit in the fair-use defense in most states. Considering the emotional values preserved by AI cover songs as well as the spirit of copyright laws, this article argues that basically, the training of AI cover models should be solely for non-commercial use. An unregulated approach toward AI cover songs can otherwise hinder innovations and disproportionately harm the interest of minority groups.
Multidimensional Analysis of Continuation Writings by Chinese Learners of English Across Different Proficiency Levels

This study used multidimensional analysis to investigate whether English learners writing features vary significantly across their proficiency levels in the continuation writing task. Writings were collected from 30 Chinese high-school English learners, who were divided into three proficiency groups based on their writing scores. Their writing texts were analyzed by using Multidimensional Analysis Tagger (MAT 1.3). The results showed that three groups varied significantly in the dimensions of formality and abstractness of their writings. These two dimensions are related to the proportion of nouns and conjunctions in writings. Writings by higher proficiency students were more formal and abstract because of higher proportions of nouns and conjunctions than those by intermediate and low proficiency students. The results indicate that teachers can improve students writing performance by introducing some formal words and conjunctions to students.
Text Summarisation Using BERT

Artificial intelligence has significantly increased during the last ten years. AI-related text summarisation, which identifies the pertinent sentences from a text, is a significant research topic. Text summarisation may obtain concise and accurate information while maintaining the text’s original structure. The method for creating a succinct and accurate extraction summary for the provided text material is presented in this project. This paper provides a comprehensive literature review to gauge the complexities in the existing solutions, followed by the proposed solution using BERT for finding embeddings. As a classification challenge, extractive summarisation is done by predicting a class for each sentence in a document (i.e. determining if a sentence should or should not be a part of the generated summary). The sentences are assigned a rank, also known as their prediction score. The summary is then provided by ordering the best-scoring sentences according to certain relevant factors (e.g. order of appearance in the document, grammatical correctness, etc.). The news articles from CNN/Daily mail are used to verify the accuracy.
KSMOTEEN: A Cluster Based Hybrid Sampling Model for Imbalance Class Data

Classification accuracy for imbalance class data is a primary issue in machine learning. Most classification algorithms result in insignificant accuracy when used over class imbalance data. Class imbalance data exist in many sensitive domains such as medicine, finance, etc., where infrequent events such as rare disease diagnoses and fraud transactions are required to be identified. In these domains, correct classification is essential. The paper presents a hybrid sampling model called KSMOTEEN to address class imbalance data. The model uses a clustering approach, the K-means clustering algorithm, and combines the SMOTEEN technique. The experimental result shows, the KSMOTEEN outperforms some existing sampling methods, thus improving the performance of classifiers for class imbalance data.
Mask Wearing Detection System for Epidemic Control Based on STM32

This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly.
Active Learning in Physics: From 101, to Progress, and Perspective

Abstract Active learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, the potential integration of AL is explored with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.
Improved Collaboration and Learning Outcomes Theme VII Sub-Theme 3 Through the Discovery Learning Learning Model for Grade VI Students of SD Negeri 2 Samigaluh 2021/2022 Academic Year

This study aims to improve collaboration skills and learning outcomes of students in Theme VII Sub-Theme 3 using the discovery learning model for class VI students at SD Negeri 2 Samigaluh in the 2021/2022 academic year. This type of research is a Classroom Action Research (CAR) for 2 cycles, each cycle consisting of 2 meetings. The subjects in this study were class VI students at SD Negeri 2 Samigaluh for the 2021/2022 academic year, with a total of 11 students consisting of 5 boys and 6 girls. Data collection techniques in this study were based on observation results. The results of this study indicate an increase in students' collaboration skills, namely in the first cycle the average value was 18.18% while in the second cycle the average value was 86.37%. The results of the research data on student learning outcomes also showed an increase, this is known from the average value in cycle I of 68.18%, while in cycle II an average value of 95.45%. This shows that the application of discovery learning models can improve collaboration and learning outcomes of class VI students at SD Negeri 2 Samigaluh on theme VII sub-theme 3.
Choice: AI Assisted Intelligent Goal Tracking

It is often the case that people set up elaborate goals but are not able to fully implement them. A “goal” is defined as the progress or familiarity with a topic in which the user wants to excel. There are applications which allow people to make and track goals and their progress in a particular domain, but are heavily dependent on the user’s input. Only the user knows whether they have completed their goal or not and the application is just reflecting what the user already knows. With Choice, a system would be introduced which not only lets people set their goals, but also monitors whether they are indeed sticking to them. The basis of the application is history distribution of a user. The application uses anonymous history tracking and Machine Learning to provide insights into search patterns, thus enabling better analysis of search topics for users. It is specifically targeted toward university students and can be coupled with a customizable chat-bot to further provide relevant and helpful information to the students.
Classification of Sentiment Analysis Based on Machine Learning in Drug Recommendation Application

Since the corona virus was discovered, there has been an increase in the difficulty of gaining access to genuine clinical resources. This includes a scarcity of experts and healthcare workers and an absence of appropriate equipment and drugs. The whole medical community is in a state of crisis, which has led to the deaths of a significant number of people. Because the drug was not readily available, people began self-medicating without first consulting with their doctors, making their health situation much worse. Recently, machine learning has shown to be helpful in a wide variety of applications, and there has been an uptick in the amount of new work done for automation. The study’s goal is to showcase a medicine recommender system (RS) that can drastically cut down on the amount of labor now being done by specialists. In this research, we build a drug recommendation system by analyzing patient feedback for tone. We utilize the machine learning-based XGBoost classifier, count vectorization for feature extraction, and ADASYN for data balancing. This system can assist in recommending the best drug for a specific disease by using a variety of implementation processes. The predicted sentiments were established based on their precision, recall, accuracy, F1-score, and area under the curve (AUC). The findings suggest that 95% accuracy may be achieved using the classification algorithm XGBoost with count vectorization compared to other models. The results of our experiments demonstrate that our system can provide highly accurate, efficient, and scalable drug recommendations.
Android-Based Project-Based Learning Integrated Gravimetric Analysis for Chemical Expertise Analysis of Vocational School: Development of Teaching Materials

This study aimed to produce gravimetric analysis teaching materials and determine the validity test of gravimetric analysis teaching materials. The development of teaching materials is carried out using the ADDIE development model (Analysis-Design-Development-Implementation-Evaluation). The results of the development of teaching materials are in the form of applications with the APK extension that can be run on Android smartphones. The teaching material validation test obtained the percentage of assessment which included aspects of the content of teaching materials by 84.3% (very feasible), media aspects by 83.9% (very feasible), construct aspects of teaching materials by 81.7% (very feasible), aspects the language used is 79.2% (decent), and the aspect of conformity with the PjBL is 80.9% (very decent). Of the five aspects of the gravimetric analysis validation test assessment, the teaching materials developed were very feasible, with a total percentage of 82.87%. While the readability test got a percentage of 87.3%, so it was categorized as very feasible to use
Deep Learning-Based Language Identification in Code-Mixed Text

Language identification (LID) research is a significant area of study in speech processing. The construction of a language identification system is highly relevant in the Indian context, where almost every state has its language, and each language has many dialects. Social networking is becoming increasingly important in today’s social media platforms for people to convey their opinions and perspectives. As a result, it might be challenging to distinguish between specific languages in a multilingual nation like India. Data was gathered from publicly accessible Facebook postings and tagged with a code-mixed data tag created for this study. This study uses deep learning techniques to identify languages at the word level, where the encoded content may be in Hindi, Assamese, or English. Convolutional neural networks (CNNs) and long short-term memory (LSTM), two deep neural techniques, are compared to feature-based learning for this task. According to the finding, CNN has the best language identification performance with an accuracy of 89.46%.
Comparative Analysis of Decision Tree and k-NN to Solve WSD Problem in Kashmiri

A word is treated as ‘ambiguous’ if it has more than one interpretation. Deciphering the meaning of an ambiguous word as per its context is known as word sense disambiguation (WSD). WSD, an open problem in natural language processing (NLP), has significant implication on various NLP applications, hence needs proper attention. Many efficient and robust approaches exist to tackle WSD issue and have been explored extensively for different languages. In this research work, the first attempt is made to analyze this problem in Kashmiri language. For this, we used two popular machine learning algorithms, k-NN and decision tree, and contrasted their performance. For experimentation, a novel sense tagged corpus is created for fifty commonly used and highly ambiguous Kashmiri words. The instances for the selected ambiguous words are extracted from a raw Kashmiri corpus of 500 K tokens. To tag the selected ambiguous terms with the appropriate sense, Kashmiri WordNet is used. Decision tree and k-NN-based classifiers are trained based on contextual features extracted from the sense tagged corpus. Both the algorithms are tested on all fifty target words. Accuracy, precision, recall and F1-measure are calculated for both the algorithms and compared. Although both the algorithms performed well, the decision tree-based classifier showed lower performance than the k-NN-based classifier in many cases.
Ensemble Learning for Enhanced Prediction of Online Shoppers’ Intention on Oversampling-Based Reconstructed Data

As customer traffic has been increasing over the years on online shopping websites, it is indispensable for sellers to assess online customers’ purchase intentions, which can potentially be predicted by analyzing the historical activities of the customers. This study analyzes the highly imbalanced empirical data of online shoppers’ intentions to foretell whether a visitor to an online shopping website will make a purchase. The synthetic minority oversampling technique has been implemented to reconstruct the dataset to alleviate the class imbalance in the original dataset. The effectiveness of oversampling has been identified by comparing the predictive performance of four different classifiers Partial decision tree (PART), decision tree (DT), Naïve Bayes (NB), and logistic regression (LR) on the reconstructed data with the performance on the original dataset. It has been observed that each classifier performs better on the reconstructed dataset. Ensemble learners have been implemented with varying base classifiers on the reconstructed dataset to identify the best predictive model. Bagging, boosting, and max-voting ensemble learners have been implemented with the base classifiers PART, DT, NB, and LR. The best performance has been observed by the prediction using bagging with PART as the base classifier with an accuracy of 92.62%. Hence, it has been identified as the best model for predicting the purchase intention of a customer in terms of accuracy. However, the highest precision and recall values of 0.923 have been given by the max-voting classifier with DT, PART, and LR as the base learners. It has also been concluded that the proposed methodology outperforms the existing models for shoppers’ intention section tasks.
Improving Learning Outcomes and Learning Motivation in Mathematics Through Demonstration Methods with Number Card Media Assistance for Class I Students of SD Negeri 2 Samigaluh in Academic Year 2021/2022

This study aims to improve the mathematics learning outcomes of class I students at SDN 2 Samigaluh for the 2021/2022 academic year. This type of research is Classroom Action Research which was carried out for 2 cycles with 2 meetings in each cycle. The results of the data in this study indicate that the learning outcomes of students have increased, namely, in cycle I, a percentage of 85.72% was obtained, and in cycle II a percentage of 100.00% was obtained. As for the results of the data on students' learning motivation, it showed a significant increase where in the first cycle the 1st meeting obtained a percentage of 52.57% and the 2nd meeting obtained a percentage of 72.94%. Whereas in cycle II, the 1st meeting obtained a percentage value of 85.51% and the 2nd meeting obtained a value of 90.11%. Thus, the use of the demonstration method using number card media aids can improve learning outcomes and learning motivation of class I students at SDN 2 Samigaluh in the mathematics subject for the 2021/2022 academic year.
Traffic Sign Detection and Recognition Using Ensemble Object Detection Models

Automated cars are being developed by leading automakers and this technology is expected to revolutionize how people experience transportation, giving people more options and convenience. The implementation of traffic signal detection and recognition systems in automated vehicles can help reduce the number of fatalities due to traffic mishaps, improve road safety and efficiency, decrease traffic congestion, and help reduce air pollution. Once the traffic signs and lights are detected, the driver can then take the necessary actions to ensure a safe journey. We propose a method for traffic sign detection and recognition using ensemble techniques on four models, namely BEiT, Yolo V5, Faster-CNN, and sequential CNN. Current research focuses on traffic sign detection using an individual model like CNN. To further boost the accuracy of object detection, our proposed approach uses a combination of the average, AND, OR, and weighted-fusion strategies to combine the outputs of the different ensembles. The testing in this project utilizes the German Traffic Sign Recognition Benchmark (GTSRB), Belgium Traffic Sign image data, and Road Sign Detection datasets. In comparison with the individual models for object detection, the ensemble of these models was able to increase the model accuracy to 99.54% with validation accuracy of 99.74% and test accuracy of 99.34%.
A Lightweight Cipher for Balancing Security Trade-Off in Smart Healthcare Application

IoT is a dynamic, emerging sector characterized by using existing Internet infrastructure to connect devices and data centers worldwide. The internet of healthcare things (IoHT) is a subclass of IoT that includes high-tech medical equipment that plays a crucial role in data monitoring, processing, storage, and transfer. It has novel challenges in data security. Several cryptographic techniques have been devised to safeguard the system against data misuse, alteration, and node tempering. Such cryptographic algorithms are inadequate because of the device's diminutive size, limited computing power, memory, and power resources. A lightweight cryptographic scheme based on symmetric cryptosystems powered by the Feistel structure is required to secure such a system. A lightweight cryptosystem is proposed in this paper for the security of the e-healthcare system. The proposed algorithm encompasses a compact S-box function, less round and addition substitution, and an XOR operation to generate encrypted information from plaintext. Additionally, a secure authentication mechanism based on the previously mentioned method is suggested for secure communication in the healthcare system. The cryptanalysis process and result state that our proposed system resists several cryptanalytic attacks which include brute force, avalanche effect, linear cryptanalysis, and biclique attack when compared with standard AES algorithm.
An Analytical Survey of Image Encryption Techniques Used in Various Application Domains

Due to continuous innovations in machinery, digital images are extensively used in numerous domains like remote sensing, military, communication, medical, etc. These images may encompass subtle and private data. Thus, images are mandatory to be secured from illegal access. Several image security methodologies have been developed in previous years. Most of the techniques to secure the images use encryption. In this method, an encrypted image is generated from the plane image using an encryption algorithm and a secret key. Researchers are continuously devising new techniques in various domains for image encryption. To increase image security, various researchers have devised new techniques for image encryption. In this perspective, an analytical survey of the current image encryption approaches is presented in this paper. The investigation covers the application of encryption techniques in various application domains such as finance, defense, remote sensing, medical, etc. The image encryption techniques reviewed in this paper are categorized based on chaotic map, DNA coding, neural network, and spatial techniques. These techniques are compared to assess those on the basis of various security parameters. In addition, the performance of these techniques is also analyzed. The future directions to encourage researchers for devising new encryption techniques are also presented in this paper.
Performance Analysis of DCT Based Latent Space Image Data Augmentation Technique

Image data augmentation is the process of enlarging the dataset by creating synthetic images using the existing images to address the problem of scarcity of data and poor generalization. The existing methods for image data augmentation are mainly domain specific and focus on transformations in spatial domain. In this paper, we have developed a domain independent image data augmentation technique based on frequency domain transformations. In this research, we have utilized discrete cosine transform (DCT)-based latent space and proposed two techniques. In the first technique, we apply DCT on each of the color channel of the image and synthesize new image by considering only high-energy latent coefficients. The second technique is a hybrid technique where standard transformations are applied on the images synthesized from the DCT latent space. The experiments carried out on the Cifar10 dataset using VGG16 revealed that the proposed hybrid technique is more effective than the technique where the synthetic images are created solely from the DCT-based latent space. The analysis of training loss and validation loss shows significant improvement in validation loss, thus improving generalization of the VGG 16 model.