Reviewerly/exHarmony huggingface dataset:
Research on Interior Decoration Display Design System Based on Computer Artificial Intelligence Technology

This paper uses X3D components of the outside prototype technology, scripting language support technology and user interface support technology to complete an X3D/VRML based on the online interior decoration design system. Three-dimensional MAX software was used to conduct three-dimensional modeling of the house plane, obtain the three-dimensional modeling document of FBX, and input it into Unity3D. Some basic functionality was designed in Unity to make it more interactive. The system also provides a three-dimensional network interface. Converting floors, wallpaper, and furniture with a mouse can be done easily and quickly. In this way, users can quickly feel the three-dimensional decoration effect. This method can be used to design for customers' requirements in interior decoration.
A Teacher Behavior Recognition Model Based on Multi-Stream Graph Convolution Network

In recent years, various innovative applications of intelligent education driven by the new generation of AI technology have become research interest in the fields of education science and technology. Recognition of classroom teaching behavior based on image and video analyses has attracted much attention of researchers in recent years. Compared with the research on classroom student behavior, the research on classroom teacher behavior recognition and analysis is still in its infancy. Recognition of teacher behavior are associated with issues such as the lack of teacher behavior data sets in real classroom scenarios, difficulty in migration of existing behavior identification algorithms, and low accuracy of teachers' classroom behavior identification. Accordingly, this paper constructs a data set of teacher behavior in the real classroom environment, including video and bone information data of five types of teacher behavior, and proposes a teacher behavior recognition model based on multi-stream graph convolution network. This model effectively fuses joint, bone and motion information; adds graph attention module on the basis of graph convolution; learns and optimizes the connection between nodes; and finally realizes effective recognition of teacher behavior in classroom videos. The proposed model was compared with different models on self-built datasets for several groups of experiments. Our results show that the proposed model performs well in teacher behavior recognition tasks in real classroom scenarios.
BlockAgro: Towards Blockchain assisted IoST Framework for Agricultural Sector

Blockchain and Internet of Spatial Things (IoST) integration in smart agricultural systems employing Geographical Information System (GIS) provides agricultural systems with decentralized, trustworthy, secure, dependable, scalable, traceable, and transparent services. Most previous research uses IoT to automate agricultural systems without addressing security, scalability, single-point failure, access control, trust, and transparency concerns. With an expanded marketplace, distributors, producers, buyers, sellers, and other agricultural users, the previously proposed cloud-based and centralized agricultural systems faced limits in data collection, sharing, management, processing, storing, availability, security, auditability, and stability. To address the challenges that exist in IoST agricultural systems, we propose BlockAgro, a Blockchain-assisted IoST framework for the agricultural sector, with a three-tier architecture, namely an IoST device layer, a fog layer, and a cloud layer, to address the issues in IoT agricultural systems. Additionally, it provides data analysis, visualization, processing, filtration, and temporary storage of gathered IoST data at the nearest fog nodes to reduce cloud infrastructure overhead. Moreover, the experimental findings of the proposed framework ensure the access control mechanism for IoST devices, efficiency measurement metrics, and data availability in a decentralized agricultural platform.
Co-Creating Virtual Reality as Open Educational Resources: An Inquiry-Based Thinking Approach

In recent years, the use of internet and communication technologies in the workplace for collaboration has increased. Working as members of international teams is not only common, but necessary. This paper examines the TalkTech 2022 project, a global collaboration between universities in the United States and Romania, in which students participated as members of international teams to explore and share local cultural artefacts through the co-creation of virtual reality scenarios. The authors present the TalkTech project through the lens of a five-point inquiry model, an inquiry-based approach based on constructivist learning which empowers students to develop digital competencies and build knowledge through active participation and co-creation of digital artefacts and open educational resources. The authors conclude that the TalkTech project is a catalyst for developing critical thinking and digital competencies, which will serve students well in their future careers.
Speech Recognition of Isolated Odia Digits Using Convolutional Neural Network

Automatic Speech Recognition (ASR) is the process of recognition of voice by a computer or device. ASR has different applications such as Speaker Identification, voice based IVR system, automatic transcription system, etc. This paper proposes a Convolutional neural network (CNN) model for ASR of isolated digits spoken in the Odia language. Forty Mel Frequency Cepstral Coefficient (MFCC) features have been extracted for each isolated digit. The proposed acoustic model is implemented using CNN. The dataset is collected by recording the utterance of the digits (0-9). The model is tested with different dropout and learning rates. The proposed model gives better accuracy than other classification algorithms like DNN, HMM, SVN, and Random Forest. We have achieved an accuracy of 95.71%.
Design and Implementation of Intelligent Monitoring System

With the development of the aging, the contradiction is becoming severe between young people to face life and to take care of the elderly .It is necessary to design a safety protection system to ensure the happiness of elderly people living at home in their later years. This article designs an intelligent monitoring system for elderly people, which can effectively monitor their behavioral status. The system is composed of STM32F103C8T6 chip, sensor module, button module, alarm module, GPRS module, display module, etc. It realizes the monitoring and processing of physiological parameters and behavioral status of elderly people at home. If there is an accident, the system will notify their children by sending SMS or calling. Simultaneously, which using an OLED display screen to display daily behavioral datas of elderly people at home, it could make it easier for children to better understand their physical condition and handle it in a timely. After testing, the system has achieved the monitoring of the living conditions of the elderly, the alarm of falls, and timely feedback, which may effectively promoting the harmonious development of the family and society.
Controlling the Accuracy and Efficiency of Collision Detection in 2d Games using Hitboxes

Collision detection is a process in game development that involves checking if two or more objects have intersected or collided with each other. It is a fundamental aspect of game mechanics that cannot be overlooked. Games invloves assets/sprites, which tend to be drawn digitally with the help of a computer program. This paper discusses controlling and detecting collisions in games that make use of PNG images as game assets. The conventional way to detect collision in a game is to check if the object is within the bounding box of another object or asset. However, such a method lacks realism and doesn’t work well with much complex shapes as the game might register a hit when another object collides with the transparent part of the object being checked for collision. In order to overcome these limitations, the proposed algorithm divides the entire image into smaller rectangles and stores its coordinates in an array. The array is then pruned by removing coordinates with no translucent or opaque elements. Collision is detected by checking if any of the points of the collision object is inside the image array.
Construction of Sensitive Image Datasets Based on Generative Methods

Due to the special nature of sensitive information, available sensitive data resources are relatively scarce to meet the needs of research in this field. The purpose of this paper is to construct a large sensitive image dataset by generative methods, with a view to supporting research exploration in related aspects. To achieve this goal, we adopt a new idea. First, we crawl sensitive and non-sensitive class datasets from web pages and filter them manually. Here, the sensitive classes mainly involve violence, terror and pornography; second, the selected data are used as the training set to fine-tune the pre-trained generative model; then, the fine-tuned generative model is used for sensitive/non-sensitive image generation, which expands the training dataset; finally, the generated training set is passed into the classification model to do the classification task for result verification. The experimental results show that the classification results of the expanded dataset using the generative method are higher than those of the original dataset, which proves the effectiveness of the generative method to construct the dataset.
The Influence of Isolation on Learning in an Immersive Laboratory Environment

Today's learning environments often involve multiple streams of information and entertainment during the learning process. Providing content via virtual reality (VR) can be used to create an isolated and engaging learning environment, in which immersion and interactivity are key factors for learning. Reducing the cognitive load in interactive VR environments by highlighting relevant material can attract the learner's attention. In this paper, we investigate the effect of isolation-based design on the learning experience in a VR-chemistry-lab environment. The lab environment includes a visualization of an interactive chemistry simulation to teach the concept of catalysis using a black box representation. Users can observe and influence the chemical reactions on different surfaces. We present an AB study with 39 participants to evaluate the users' emotions, motivation, learning experience, and outcome. The results indicate a positive effect on the learning outcomes and engage users in learning.
Credit Text Similarity Calculation Method Combined with Contrast Learning

The text matching task aims at mining the inherent semantic features from two texts and predicting the correlation or contradiction between texts. The credit text phases have obvious domain-specific features, and there are more adversarial texts, and most of them are of different lengths leading to missing information and unclear main ideas, which will reduce the accuracy of similarity calculation. To this end, this paper proposes a credit text similarity calculation method combined with contrast learning. First, we will recall the candidate set by multi-level text recall method to reduce the influence of non-critical factors and reduce the computational effort of text matching model from shallow semantics, deep semantics and entities. And then fine-tunes the pre-trained model by combining with contrast learning method to calculate the semantic text similarity. The comparison experimental results with BERT and SimCSE show that the text similarity calculation method proposed in this paper works better.
Revisiting Word Prediction: A GCN and ARIMA Framework for Wordle Mining

Word prediction algorithms play an important role in natural language text retrieval and pattern mining.This article explores the hidden patterns of the word mechanism by starting from the word items in Wordle. The paper presents a preliminary ARIMA model based on player guessing data from January 7th, 2022 to December 31st, 2022, aimed at predicting the range of reported results with a 95% confidence level on March 1st, 2023. To extract hidden information about word frequency and guessing difficulty, a GCN model was constructed to establish the distribution of attempts for word structures, where GCN network nodes represent letter frequency and position information, and edges contain weights for letter combinations. The model demonstrated a significant improvement over traditional machine learning algorithms.
Application of Encryption Technology in Information Security of the Internet of Things Perception Layer

Because the IoT (Internet of things) perception layer has a large number of nodes, limited computing and storage capabilities, and complex data types and network types, attackers can attack the loT perception layer with devices with good endurance, computing and storage capabilities, thus threatening the security of the sensing environment. In this paper, the application of encryption technology in information security of loT perception layer is studied. In order to make up for the huge overhead problem faced by Server in the existing research, this paper introduces fog nodes and constructs a fog-assisted crowdsourcing task assignment and swarm intelligence perception data aggregation model. Using fog nodes to assist the sensing platform to achieve large-scale task allocation and data aggregation. In order to protect their data privacy, participants choose to encrypt the data before submitting the perceived data to the fog node. The simulation results show that the reading efficiency of this method is greatly improved compared with other existing methods. The non-monotonic jump of the throughput rate of each method is because the setting of the initial frame is more suitable for reading the number of tags in this method. Simulation results show the effectiveness and efficiency of the proposed scheme.
Face Recognition Based on PCA Method

Face recognition is a kind of biometric recognition technology. Compared with other biometric recognition techniques such as iris recognition gait recognition and so on face recognition technology starts later but its application is more extensive. Although face recognition technology has been widely used in different fields and industries, it is still one of the more difficult topics because of the very similar facial structure between people, and the face shape and surface are affected by many factors. For the above reasons, face recognition based on principal component analysis (PCA) algorithm is studied in this paper. A face recognition system is designed by using MATLAB software, and the system is tested by ORL database. The recognition rate can reach 77%.
Fem-Scale: A Data-Driven Approach for Quantifying Degree of Individual Feminism Perspective

Abuse of women is widespread in underdeveloped nations like Bangladesh, where women are still afraid to assert their rights, and this trend persists. Even though significant changes are apparent, they have not entirely disappeared from society. On the other hand, there are some negative notions of feminism prevailing in our community. The lack of proper classification of the individual feminism perspective in relevant degrees is a significant concern while developing effective policies against this phenomenon. In this work, we develop a data-driven approach to quantify the degree of the individual feminist standpoint. We collected data from a selected crowd using survey questionnaires and analyzed the responses to segregate the participants into a Fem-Scale with five distinct classes: Sexist, Partial Sexist, Moderate, Feminist, and Radical feminist. We also validated the proposed Fem-Scale with real-life data generating an accuracy of 97.67% and highlighted the critical insights of the qualitative analysis of the collected data. To the best of the authors’ knowledge, this is the first attempt to introduce the degree of individual feminism standpoint. The proposed method can be beneficial in future research, including justifying the standpoint regarding feminism using public data from an individual’s social networking site (SNS).
Who Should Do It? Automatic Identification of Responsible Stakeholder in Writings During Training

In online Problem-Based Learning (PBL), being able to provide immediate feedback to learners is invaluable, yet difficult to achieve.We examine how well an off-theshelf Natural Language Processing (NLP) framework is able to detect the absence of an identified responsible stakeholder in ideas generated during security training.Part-of-Speech Tagging and Dependency Parsing are applied on contextualised written learner contributions, collected from a PBL environment and compare the results to an assessment performed by experts.Using grammatical analysis, we aim to detect the absence of an identified responsible stakeholder in collected contributions (n = 1174) from two security domains.Four heuristics are compared, resulting in a precision of (P P V = 0.929) on the best of these, sufficient to provide immediate feedback to learners.Our results suggest that for the purposes of scaffolding openended PBL exercises, off-the-shelf NLP frameworks can achieve good performance on responsible stakeholder identification.
Visual Analysis of Virtual World Research in Education from 2013 to 2022

The virtual world provides a novel context for teaching and learning where teachers and students can engage in activities such as lecturing, discussion, and collaboration virtually without the restriction of space. In this study, visual analysis software CiteSpace was used to analyze the literature related to the web of science database in the past 10 years (2013–2022), aiming to provide an overview of the virtual world research in the field of education, and reveal key research patterns and trending features. The research findings revealed authors, regions, journals, and references with significant contribution. Additionally, keyword co-occurrence, burst terms, and a timeline map were generated to identify key hotspots in this research field.
Sentiment Analysis Using Learning-based Approaches: A Comparative Study

Sentiment analysis, which involves analyzing text data and using language computation to extract valuable information, is a significant focus in Natural Language Processing (NLP). It is widely used in various applications such as product review analysis, customer feedback analysis, and social media monitoring. This research investigates the performance of different machine learning and deep learning models for sentiment analysis on a dataset of customer reviews from an e-commerce platform. A total of eight approaches have been presented in this study including LightGBM, SVM, KNN with bagging, MultinomialNB, DNN, LSTM, BERT, and RoBERTa. The performance for all the proposed models was compared using four evaluation metrics: accuracy, precision, recall and F1-score. The experimental results indicate that the SVM model has outperformed all the other methods with a testing accuracy of 73.98%. The F1-score, precision and recall are also the highest at 0.71, 0.72 and 0.70 respectively. This study contributes to the sentiment analysis literature by demonstrating the effectiveness of different models for sentiment analysis on customer reviews datasets.
An exploration of the impact of Feature quality versus Feature quantity on the performance of a machine learning model

About 0.62 trillion bytes of data are generated every hour globally. These figures have been increasing as a result of digitalization and social networks. Some data ecosystems capture, store, and manage this big DATA. The basis is to be able to analyze their information and extract their value. This fact is a gold mine for companies researching and using this data. This leads us to follow how essential and valuable data is in this growing age. For any machine learning model, the selection of data is necessary. In this paper, several experiments have been performed to check the importance of data quality vs. data quantity on model performance. This clearly indicates comparing the data’s richness regarding feature quality (e.g., features in images) and the amount of data for any machine learning model. Images are classified into two sets based on features, then removing redundant features from them, then training a machine learning model. Model getting trained with non-redundant data gives highest accuracy (>80%) in all cases versus the one with all features, proving the importance of feature variability and not just the feature count.
Joint Model for Extraction of Clinical Temporal Relationships at Document level through Deep Learning

Event temporal relationship is helpful in analyzing natural language as it can classify event improvement. Pre-trained language approaches have been applied in numerous recent studies, and the results have clearly improved performance. When multiple events are not included in the same sentence, these methods generally fail spectacularly due to the more dynamic context. In order we intend a Joint Model to enhance the effectiveness of temporal knowledge extraction within and Between Sentences (JTE). The temporal relation classification model is consolidated through an ancillary task to estimate the sequence of events of the sentences, creating a multi-task methodology. Extensive research indicates that, when especially in comparison to the baseline model, On the I2B2-2012 data set, our framework is capable of enhancing the effectiveness of cross sentence temporal information retrieval while accomplishing abrasive results.
Research on Machine Reading Comprehension Model Based on Bidirectional Attention

Machine Reading Comprehension (MRC) is a significant subfield of Natural Language Processing (NLP) and has become a hot research topic in recent years. It is an algorithm that allows a machine to understand natural language text and answer based on questions. With the rapid development of deep learning, MRC, especially attention mechanisms, has emerged as a primary technique in NLP.This paper focuses on the development and principles of attention mechanisms, along with introducing three MRC models based on bidirectional attention. Bidirectional attention helps the models extract crucial information, enhancing accuracy and enabling broader applications in various NLP tasks.
Design of automatic evaluation system of C language program based on fuzzy clustering algorithm

The purpose of developing the automatic evaluation system of C language program is to make computers replace teachers, so that students can learn domain knowledge independently. In this way, teachers can be liberated from tedious work, and students can learn domain knowledge freely. In this paper, an automatic evaluation system of C language program based on fuzzy clustering algorithm is established. This system is developed with B/S three-tier architecture, that is, the whole business application is divided into three layers: presentation layer, business logic layer and data access layer. Based on the above basic knowledge, this paper describes the analysis of learning behavior by FCM( Fuzzy c-means). According to FCM, the membership matrix and clustering center matrix are calculated, and finally the clustering results of learners with different learning enthusiasm levels are obtained. The research results show that the accuracy rate of the method used in this paper is 91.0582%. It can not only avoid giving full marks to opportunistic students, but also give a more reasonable score to students who don't have result files and output results. The score given is objective and accurate, which meets our actual needs.