Reviewerly/exHarmony huggingface dataset:
An Efficient Method for Detecting Hate Speech in Tamil Tweets Using an Ensemble Approach

People have converged on a worldwide level because of advancements in communication technologies. They are critical in ensuring freedom of speech by allowing individuals to openly express their thoughts, behaviors, and opinions. Although this presents an excellent chance for racism, trolling, and exposure to a flood of nasty online content. As a result, social media’s exponential rise in hate speech is profoundly affecting society. In this study, we used machine learning and deep learning algorithms to identify hate speech, and we compared their performance to create an ensemble model. Researchers gathered and integrated two distinct datasets of hateful tweets in Tamil that were produced by Bharathi Raja Chakravarthi et al. Tweets in this dataset fall into two categories: non offensive and offensive. This dataset contains 10,129 tweets. In addition, the researchers used six selected machine and deep learning algorithms for this study namely. Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Bidirectional LSTM, Multi-layer Perceptron (MLP) and Multilingual BERT. When it comes to detecting hate speech, SVM (82%) and LR (82%) have the best Accuracy. Furthermore, researchers developed two ensemble algorithms to construct a most efficient model. First ensemble model was created by combining SVM, LR and NB and the second ensemble was developed using SVM and LR. Four algorithms including the two ensemble models were obtained same accuracy. Therefore, the researchers compared the F1 score and found that the ensemble model 02 outperformed other classifiers. The results of this study are significant because they can be used as a benchmark for future research on Tamil language hate speech using various machine learning algorithms to detect hate speech more effectively and accurately.
Comprehensive Literature Survey on Deep Learning Used in Image Memorability Prediction and Modification

As humans, we can remember certain visuals in great detail, and sometimes even after viewing them once. What is even more interesting is that humans tend to remember and forget the same things, suggesting that there might be some general internal characteristics of an image that make it easier for the brain to encode and discard certain types of information. Research suggests that some pictures tend to be memorized more than others. The ability of an image to be remembered by different viewers is one of its intrinsic properties. In visualization and photography, creating memorable images is a difficult task. Hence, to solve the problem, various techniques predict visual memorability and manipulate images’ memorability. We present a comprehensive literature survey to assess the deep learning techniques used to predict and modify memorability. In particular, we analyze the use of convolutional neural networks, recurrent neural networks, and generative adversarial networks for image memorability prediction and modification.
Digital Image Correlation Pattern for Concrete Characteristics—Optimal Speckle

Digital Image Correlation (DIC) is an innovative effective method for assessment of stress-strain state of the structural element. The Digital Image Correlation technique enables to study cracking and fracture mechanisms in constructions, in order to obtain the full field of deformations and stresses and to evaluate the load-bearing capacity, reliability and residual resource. Measurement of deformations according to the DIC method is performed with the use of a stochastic contrast speckle pattern, which has to be previously applied to the studied surface. Good pattern determines high confidence of experimental results and low noise level and has to meet the following requirements: high contrast, the consistent speckle sizes and degree of filling of the surface, isotropy and randomness. This paper presents results of experimental research of prism concrete samples with different parameters of speckle pattern. The comparative analysis of experimental results enabled to determine the most optimal speckle sizes, pattern randomness and coverage.
Comparison and Enlightenment Between IELTS and Different English Language Examations in China

The world-renowned international English test, the IELTS test, tests students comprehensive English language proficiency. Compared to some selective English language tests in China, the IELTS test has various aspects that are worth learning from it. In recent years, some English language tests in China have some shortcomings that still need to be improved to some extent, and the testing methods and concepts demonstrated in the IELTS test can make up for the shortcomings of Chinese English testing. This article aims to compare the IELTS with the Chinese English test in terms of exam organization and requirements, exam question types and content, and discuss the summary and inspiration for Chinese exams and education. The research results indicate that some testing concepts and methods in the IELTS exam are worth learning from in Chinese education. English language tests in China can also refer to its advantages to reform and optimize its own examination and education models, making the domestic examination system and education model more suitable for Chinese students. Practice has also proven that in some regions where the IELTS exam has been implemented, the education system has also been positively affected, and the educational effectiveness of students has also been significantly improved.
Sentiment Analysis of Public Opinion Towards Reverse Diabetic Videos

The amount of textual data has increased significantly over time, creating the potential for machine learning and natural language processing research. Nowadays, sentiment analysis of YouTube comments is a fascinating topic. Due to the inconsistent and poor quality of the data, not much has been done to pre-process the numerous user comments. In this study, we use machine learning approaches to do sentiment analysis on YouTube comments relating to hot subjects. Although several techniques for sentiment analysis have been created recently for the health sector, the field of reverse diabetes has not yet been much investigated based on YouTube video comment analysis. To categorise the dataset, the machine learning techniques like Naive Bayes, support vector machine, logistic regression, and random forest were utilised.
Analyzing the Feasibility of Bert Model for Toxicity Analysis of Text

Online comments can often be toxic, offensive, and harmful to individuals and communities. In recent years, there has been a growing need to automatically identify and mitigate these toxic comments. For this problem, NLP models are often used to identify such toxicity and harshness but each model has its own efficiency and performance limitations. In this paper, we propose the use of the bidirectional encoder representations from transformers (BERT) algorithm for toxicity classification of online comments. BERT is a state-of-the-art natural language processing model developed by Google in 2018 that has shown strong results on a variety of tasks. In this paper, we used the BERT algorithm for toxicity classification and evaluated its performance on a real world dataset and performed comparative analysis with conventional NLP models, logistic regression (TF-IDF) over which BERT showed an improvement of 6.9% in accuracy, 26.1% in f1-score, 21.5% in ROC score; logistic regression (BOW) over which BERT showed an improvement of 9.1% in accuracy, 70.6% in f1-score, 39.8% in ROC score; multinomialNB (BOW) over which BERT showed an improvement of 9.2% in accuracy, 25.9% in f1-score, 10.6% in ROC score.
A Breakthrough in the Traditional Combination of Psychology and Interaction Design ---- Wearable Devices

Wearable devices are currently mostly used in the medical field to monitor the physical condition of humans, but little study is known on the mental level. This article focuses on the implications of applying wearable devices to contemporary design and art, and how wearable devices can care for the spiritual aspect of the wearer. For achieving this, it is essential to invite the audience to participate in the design process, to consider and discuss an idealized design product together, and to move away from design to solve the needs of human life. The study received positive feedback from participants through case studies and a self-practice component. It can be concluded that wearable devices targeting the traditional healthcare industry have become the traditional way of healthcare, society and individuals should turn their attention to human consciousness, psychological interactions and how to deal with tricky intimate relationships.
A Study on Social Engagement of RPGs

As the game industry has developed, fresh and fashionable games continue to emerge. With the emergence of VR technology, VR games have changed entertainment for many people but also exposed many drawbacks of online games. One of the drawbacks is the lack of social connections with other players. This study mainly focuses on role-playing games (RPGs). The literature review and the survey summarize reasons for the lack of social attributes on online RPGs. The goal is to propose solutions based on the survey findings. The comparison between traditional online RPGs and VR RPGs indicates that VR RPGs have more room for improvement in terms of social connection. Four suggestions are listed and a simple analysis of their feasibilities are also discussed in the article. Moreover, this study finds two unique characteristics of VR games that may successfully encourage people to embrace new technology, even in a world full of uncertainty.
Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection

This paper presents a novel generic method to improve lesion detection by ensembling semantic segmentation and object detection models. The proposed approach allows to benefit from both voxel-based and box-based predictions, thus improving the ability to accurately detect lesions. The method consists of 3 main steps: (i) semantic segmentation and object detection models are trained separately; (ii) voxel-based and box-based predictions are matched spatially; (iii) corresponding lesion presence probabilities are combined into summary detection maps. We illustrate and validate the robustness of the proposed approach on three different oncology applications: liver and pancreas neoplasm detection in single-phase CT, and significant prostate cancer detection in multi-modal MRI. Performance is evaluated on publicly-available databases, and compared to two state-of-the art baseline methods. The proposed ensembling approach improves the average precision metric in all considered applications, with a 8% gain for prostate cancer.
The Application of ESA Teaching Model in Teaching English Writing in High School

Inspired by Chinese teachers' one-directional indoctrination English writing class and difficulties of Chinese high school students in English writing, the current study applied ESA (Engage Study Active) method to overcome the situation. The research subject in this paper is the teaching of English writing in high school, and this paper explores the effectiveness of applying the ESA teaching model toward English writing class. This study uses qualitative research to investigate whether the ESA teaching model of writing classes can stimulate students' interest in writing, enhance their writing skills, and improve their writing performances. According to the findings, implementing the ESA method can significantly improve students' engagement and writing performance. Students' motivations to write were increased, and writing activities in the classroom were improved. The study suggests that the ESA teaching model can be an effective approach to teaching English writing in Chinese high schools. To keep up with the changing needs of students, Chinese teachers' traditional approach must be modified. The findings of this study can help teachers and educators improve their teaching methods and improve students' learning experiences.
MARCS: A Novel Secure Hybrid Chaotic System

Chaotic systems have established an important place in the generation of random sequences for the protection of vital confidential data. In the paper, we propose a novel secure hybrid chaotic system ‘MARCS’ consisting of control parameters, modulo operations, and three one-dimensional chaotic maps logistic, sine, and exponential maps combined in a nonlinear manner to achieve high complexity and characteristics necessarily required for high information security applications. We analyze its performance by examining chaotic and randomness properties to evaluate its security strength. The simulation results reveal that the proposed hybrid chaotic system MARCS possesses strong chaotic and randomness properties. Thus, MARCS can be incorporated in the design of secure information systems for meeting the high information security requirements.
Basic digital competences of university students before and during the COVID-19 pandemic

The objective of this study was to compare the digital competences of university students before and during the COVID-19 pandemic, and to determine the impact of internet use on these competences, specifically those related to social communication, collaborative learning, as well as information searching and processing. The sample comprised 415 Spanish students who began university studies between 2019 and 2022. The instrument used was the COBADI. The results indicate an increase in internet use to talk with friends in social networks and work with classmates, such as in collaborative homework. In terms of digital competences during the pandemic, there was an increase in competences related to social communication, collaborative learning, and information searching and processing. The study also found that academic and recreational internet use predicted basic digital competences, as determined by a MANCOVA analysis. The role of the pandemic context in increasing basic digital competences in university students is discussed.
Application of Goffman's Theory of Dramaturgy and Baudrillard's Concepts of Simulacrum and Simulation in the Metaverse

This study investigates social interaction within the Metaverse from the perspective of Goffman's dramaturgical theory and Baudrillard's theory of hyperreality. The Metaverse, as a deeply immersive virtual 3-D world, presents extensive opportunities for sociological analysis of the presentation of self, identity construction, and the creation of "hyperreality" within the contemporary digital world. This study employs the virtual ethnographic methods of participant observation and semi-structured interviewing within four Metaverse contexts including massive multiplayer online role-playing games, immersive social media, Metaverse conferences, and digital human live broadcasts. Results show that participant activities are clustered into three main themes, namely the replication of the self and self-idealization, the extension of the self, and identity masking/unmasking. Through employing dramaturgical theory and the theory of hyperreality, the analysis of these three themes illuminates a deeper understanding of how Metaverse social interactions both replicate and diverge from sociological understandings of the self, identity, and "reality".
Research on the Application of Python in Chinese High School Education: Challenges and Solutions

Python programming courses have been gradually introduced into the high school education stage in China since 2013. This paper explores the application of Python in the Chinese education system, discusses the challenges faced, and presents corresponding solutions. Python facilitates students learning and problem-solving abilities while aligning with the demands of the era. Despite positive progress, challenges such as resource constraints and teacher training need to be addressed. Also, several problems encountered during the implementation of Python in practice are identified, including scheduling prioritization, insufficient qualifications of teachers, and challenges in remote areas. Solutions are proposed, such as incorporating Python into the curriculum evaluation and supervision, improving teacher qualifications through training and collaborations with experts, and providing hardware support in remote areas. Therefore, this paper presents the application status, challenges, and solutions of Python in Chinese high school education. It emphasizes the importance of promoting collaboration, practical abilities, and learning confidence among students. Future research can further explore the impact of Python on education and analyze specific contexts to enhance the localization and specialization of Python in China.
Language-Aware Soft Prompting: Text-to-Text Optimization for Few- and Zero-Shot Adaptation of V &amp;L Models

Abstract Soft prompt learning has emerged as a promising direction for adapting V &amp;L models to a downstream task using a few training examples. However, current methods significantly overfit the training data suffering from large accuracy degradation when tested on unseen classes from the same domain. In addition, all prior methods operate exclusively under the assumption that both vision and language data is present. To this end, we make the following 5 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we also propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) Moreover, we identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) Importantly, we show that LASP is inherently amenable to including, during training, virtual classes , i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Expanding for the first time the setting to language-only adaptation, (5) we present a novel zero-shot variant of LASP where no visual samples at all are available for the downstream task. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Finally, (c) we show that our zero-shot variant improves upon CLIP without requiring any extra data. Code will be made available.
The Features of ABAC Construction and Its Application in Chinese Second Language Teaching

This study aims to provide guidance on how to help students master ABAC construction better, in terms of syntactic features, semantic features, as well as pedagogical methods. Based on the literature review, we analyzed the internal structure of ABAC construction. We selected 38 examples of ABAC construction applicable to teaching Chinese as a second language combined with the textbook Chinese Roadmap used in junior high school classes in Hong Kong. These sentences were analyzed both syntactically and semantically to identify the commonalities of the ABAC constructions syntactic and semantic features, and investigated the patterns to obtain a systematic understanding of its characteristics. By integrating the characteristics of ABAC construction, we proposed teaching suggestions for teaching Chinese as a second language, including syntactic features, semantic rules, and teaching methods, hoping to deepen the learners systematic comprehension of ABAC construction and to provide a valuable reference for teaching Chinese construction as a second language.
Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

Abstract Photovoltaic (PV) power generation is widely utilized to satisfy the increasing energy demand due to its cleanness and inexhaustibility. Accurate PV power forecasting can improve the penetration of PV power in the grid. However, it is pretty challenging to predict PV power in short‐term under precious future meteorological information absence conditions. To address this problem, this study proposes the hybrid Contrastive Learning and Temporal Convolutional Network (CL‐TCN), and this forecasting approach consists of two parts, including model training and adaptive processes of forecasting models. In the model training stage, this forecasting method firstly trains 18 TCN models for 18 time points from 9:00 a.m. to 17:30 p.m. These TCN models are trained by only using historical PV power data samples, and each model is used to predict the next half‐hour power output. The adaptive process of models means that, in a practical forecasting stage, PV power samples from historical data are firstly evaluated and scored by a CL based data scoring mechanism to search for the most similar data samples to current measured samples. Then these similar samples are further applied to training a single above‐mentioned well‐trained TCN model to improve its performance in forecasting the next half‐hour PV power. The experimental results tested at the time resolution of 30 min demonstrate that the proposed approach has superior performance in forecasting accuracy not only in smooth PV power samples but also in fluctuating PV power samples. Moreover, the proposed CL based data scoring mechanism can filter useless data samples effectively accelerating the forecasting process.
Advancing Abdominal Organ and PDAC Segmentation Accuracy with Task-Specific Interactive Models

Deep learning-based segmentation algorithms have the potential to expedite the cumbersome clinical task of creating detailed target delineations for disease diagnosis and prognosis. However, these algorithms have yet to be widely adopted in clinical practice, partly because the resulting model segmentations often fall short of the necessary accuracy and robustness that clinical practice demands. This research aims to make AI work in the real world, where domain shift is anticipated and inter-observer variability is inherent to medical practice. While current research aims to design models that can address these challenges, we propose an alternative approach that involves minimal user (clinician) interaction in the segmentation process. By combining the pattern recognition abilities of neural networks with the domain knowledge of clinicians, segmentation predictions can deliver the desired clinical result with little effort on the part of clinicians. To test this approach, we implemented, fine-tuned and compared three state-of-the-art (SOTA) interactive AI (IAI) methods for segmenting six different abdominal organs and pancreatic ductal adenocarcinoma (PDAC), an extremely challenging structure to segment, in CT images. We demonstrate that the fine-tuned RITM (Reviving Iterative Training with Mask Guidance for Interactive Segmentation) method can achieve higher segmentation accuracy than non-interactive SOTA models with as few as three clicks, potentially reducing the time required for treatment planning. Overall, IAI may be an effective method for bridging the gap between what deep learning-based segmentation algorithms have to offer and the high standard that is required for patient care.