Reviewerly/exHarmony huggingface dataset:
Application of recall methods in recommendation systems

In order to have a more comprehensive introduction and understanding of the re-search progress of recall strategies in recommender systems, this paper reviews the application of diverse recall methods in various recommender systems by different researchers. By searching and reading literature in major databases like Google Scholar, it is found that the recall method suitable for news recommendation system is also generally applicable in other recommendation systems. Therefore, this paper takes news recommendation system as an example to introduce traditional content-based recall and collaborative filtering-based methods. Hot-based recall and Embed-ding-based recall also developed in recent years. Furthermore, recall strategies (emo-tion-based recall and UIBB) that are specifically applicable to music and e-commerce recommendation systems are introduced. This paper briefly introduces these recall styles and collects researchers' evaluations and attitudes towards these recall styles, aiming to provide help for recommender system designers in optimizing recall methods.
Research on the algorithm of image feature detection and matching

In-depth research on feature detection technology affects people's modern life. Modern artificial intelligence can act as the eyes of human beings and efficiently filter out effective information from complex pictures. Corner detection has now evolved into a tool for efficient image scanning. People's increasingly stringent requirements for image processing continue to promote the birth of new technologies. Corner detection methods have been improved and perfected, and have experienced detectors such as Harris, FAST, Scalriant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), KAZE, AKAZE and Oriented FAST and Rotated BRIEF (ORB). In the face of many mainstream detectors, the main research purpose of this paper is to rely on Python and Computer vision to study the detection efficiency of various detectors in different environments. In this experiment, thirteen groups of pictures were selected, and after being flipped, complicated, and blurred respectively, they were detected by different detectors to obtain the results. Finally, by comparing the feature detection points, detection time and other factors, this study found that the ORB detector can be competent in most situations and is currently the fastest and stable feature point detection and extraction algorithm. On the other hand, the BRISK detector can handle highly blurred images more efficiently.
The Polyas Random Walk Theorem

Since there are a variety of uncertainties and probabilities in the actual world, mathematicians and other researchers have found numerous answers to these common problems. They have also developed theories, such as the probability theory, to help them organize their significant discoveries. A drunk person will eventually find their way home, but a drunk bird might never find it, as said by Shizuo Kakutani. A random walk, in its most basic definition, is just a path on a network or lattice where each step is decided at random using some probability distribution, such a coin or fair dice. A motion, such as going left or right on a number line or up or down on a plane, would correspond to each outcome of tossing a coin, heads or tails. Recurrent random walks are those that end up back at the beginning after a predetermined number of steps. Any non-recurrent random walk is transitory.
Computer vision models application in the current system on object detection tasks

The implementation of object detection algorithms would be helpful to the various fields of the current time. When object detection is applied to the surveillance camera system, it will be more efficient to locate crimes or find lost kids. This paper will investigate the performance of different object detection algorithms in a real-world scenario. With experimentation, CenterNet++ outperforms YOLO and MaskRCNN, two traditional and classic object detection algorithms, on the MS COCO dataset, which concludes that CenterNet++ can ensure both accuracy and speed.
Study on chess game based on artificial intelligence

With the development of technology and the growth of humans needs, artificial intelligence appears to the sight of the public and wields apparent influence in this world piece by piece, especially under the scheme of automation. There are many applications of artificial intelligence, such as computer games, software design, remote controlling, online meeting and so forth. This research will base on chess and card games, and make corresponding research via pattern matching, Bayesian network and TD algorithm application, making certain analysis to mentality of human and intelligent system. A large quantity of findings has been discovered that many researchers at home and abroad are keenly interested in this field through searching for assortments of relative references, looking through previous research results and inciting part of them. Throughout the world, Alan Turing once discussed the relationship between the next stage and machine intelligence with the paper chess machine. And Arthur Samuel, one of the founders of artificial intelligence, initiated a powerful Checkers Program when he studied and worked in this field. A lot of evidence shows the fact that machine games are one of the most riveting and challenging researches in artificial intelligence. Lots of scholars make some researches for it through kinds of matches and then draw corresponding conclusions.
Application of virtual reality technology in the psychological field

Virtual Reality is now widely used in various fields, which includes entertainment, education, working and telecommuting. With the popularization of virtual reality technology, there are more and more fields that VR is able to apply to, such as medical care and corresponding therapies. In this article, a new idea of using virtual reality in psychological field will be provided and scrutinized. By literature review and quantitative analysis, virtual reality technology can actually contribute to psychological consulting by transform the counselling environment, combining with other technologies, together stated in this passage with other potential usage and ethical issues.
Performance analysis of using multimodal embedding and word embedding transferred to sentiment classification

Multimodal machine learning is one of artificial intelligence's most important research topics. Contrastive Language-Image Pretraining (CLIP) is one of the applications of multimodal machine Learning and is well applied to computer vision. However, there is a research gap in applying CLIP in natural language processing. Therefore, based on IMDB, this paper applies the multimodal features of CLIP and three other pre-trained word vectors, Glove, Word2vec, and BERT, to compare their effects on sentiment classification of natural language processing, to test the performance of CLIP multimodal feature tuning in natural language processing. The results show that the multimodal feature of CLIP does not produce a significant effect on sentiment classification, and other multimodal features gain better effects. The highest accuracy is produced by BERT, and the Word embedding of CLIP is the lowest of the four accuracies of word embedding. At the same time, glove and word2vec are relatively close. The reason may be that the pre-trained CLIP model learns SOTA image representations from pictures and their descriptions, which is unsuitable for sentiment classification tasks. The specific reason remains untested.
Performance of NeRF and its optimized models on generating images from 2D to 3D of animation or game character

Neural Radiance Fields (NeRF) has recently emerged as a promising approach for single-image 3D scene reconstruction. Despite this, most studies on NeRF have focused on real-world data or items with established camera projection connections, rather than snapshots of video game characters that may not have a distinct camera projection connection. Although some research has been conducted on the use of animatable mannequins, the majority of these studies have used photographs of real people as their data source. To date, there has been limited research into the use of NeRF for animation or game characters. In this paper, we compare and evaluate a range of NeRF models on a dataset of animated game characters. The models considered include original NeRF, Block-NeRF, Mip-NeRF, and Pixel-NeRF, each of which has its own design choices and trade-offs. Our results show that Mip-NeRF models are effective at generating high-quality images of game characters on our dataset, while Pixel-NeRF performs poorly on our dataset. Despite these findings, our research demonstrates the feasibility of using NeRF-based models to create realistic character models or animations for video games, with potential applications in game development and virtual reality.
Performance analysis of sentiment classification based on deep learning methods

When it comes to natural language processing, the textual differentiation task is one of the classical and important research problems. Recently, the deep learning model has increasingly become one of the main methods to solve text classification problems. Common deep learning text classification models are convolutional neural networks (CNN), recurrent neural networks (RNN), the BERT model. For comparing the manifestation of various deep learning models in textual differentiation tasks horizontally, the thesis tests the classification accuracy of different deep learning models under the same experimental configuration. The experimental results show that using pre-trained word vectors helps to improve the classification accuracy of deep learning models. In addition, the reasonable design of more complex and larger deep learning models is helpful in enhancing the study capability of the specific model on text data. The experimental results indicate that the text classification model using pre-trained word vectors could gain higher accuracy than the model without pre-trained word vectors. In addition, in the comparison experiment of feedforward neural network (FNN), CNN, RNN and BERT model, BERT model performs best, and the text classification accuracy reaches 0.9232. Compared with a 1-layer FNN, the accuracy rate is increased by about 16%.
Image super-resolution techniques using deep neural networks

Super-resolution (SR) based on deep convolutional neural networks is a rapidly developing field with many real-world applications. In this paper, we examine cutting-edge super-resolution neural networks in-depth using freshly released difficult datasets to test single-image SR. We present a taxonomy that divides existing techniques into six categories, including upsampling, residual, recursive, dense connection, attention-based, and loss function designs. This taxonomy is applicable to deep learning-based SR networks. The comprehensive analysis shows that in the past few years, the accuracy has increased steadily and rapidly, while the complexity of the model and the accessibility of large-scale information have also increased accordingly. It has been noted that the present techniques have greatly outperformed the past techniques that were indicated as benchmarks. On this basis, this paper will put forward some suggestions for future research.
Research on information extraction technology applied for knowledge graphs

Information extraction is an important part of natural language processing and is an important basis for building question and answer systems and knowledge graphs. A growing number of new technologies are being applied to information extraction with the development of deep learning techniques. As a first step, this paper introduces information extraction techniques and their main tasks, then describes the development history of information extraction techniques, and introduces the practice and application of different types of information extraction techniques in knowledge graph construction, including entity-extraction, relationship extraction and attribute extraction. Finally, some problems and research directions faced by information extraction techniques are discussed.
Review on common algorithms in path panning and improvements

Based on the cooperation of artificial intelligence (AI) and unmanned driving technology, finding the best path from the starting node to the target node in the shortest time is a research hotspot. The required path planning algorithms can therefore be classified according to their different approaches to solving the problem. This paper focuses on Dijkstras algorithm, A* algorithm, Ant colony optimization, and genetic algorithm. It also discusses the present problems of these algorithms and some improvements made by researchers focusing on automated guided vehicle path planning.
Handwritten English character recognition using convolutional neural network

Converting paper material into electronic material is still a necessary work nowadays, however, recognition of handwritten characters still has limitations in their recognition rate, owing to the presence of various shapes, scales, and formats in different peoples handwritten characters. Machine learning has significant value in reducing human power. A Convolutional Neural Network model that is revised from LeNet-5, is used for handwritten letter recognition. This study uses the EMINST dataset to train the model, and the final recognition rate is about 93.44%.
Review of the development of gesture recognition technology in recent years

Currently, gesture recognition is the most rapidly advancing field in processing images and artificial technology. Gesture processing entails a procedure whereby gestures of the human body are recognized and used to coordinate PCs and other intelligent devices. This research will broadly outline human and PC coordination through gesture recognition technology over the past few years. Gesture recognition develops a simplified communication path between humans and PCs called Human-Computer Interaction (HCI). This study seeks to expose the significance of gesture recognition technology in recent years. In addition, this research will outlay the various methods in which gesture recognition technology can be applied in different perspectives, namely in education, transport, healthcare, and entertainment. This research will also elaborate on the pros and cons of every application process in each aspect. Lastly, this research will give an insight into the negative factors that hinder the development of gesture recognition technology. The research of gesture recognition mainly focuses on the visual aspect, and gesture provides an easy to understand recognition method. This makes gesture recognition products come into being and are widely used in the world. Now people do not need to touch anything. As long as they can wave their hands in the air, they can realize a series of complex operations that can be completed by touching the screen with the mouse and keyboard in the past.
Pretraining technology of adversarial generative networks based data selection

The effectiveness of adversarial generative networks (GANs) has been shown in many works. However, the convergence of the GANs should be granted mathematically with proper hyperparameters and some network structures. Considering the difficulties of constructing a large and clean dataset, GANs are required in many cases to extract enough information from a relatively small dataset. This could result in bad coverage performance of GANs, and one way to tackle this problem is pretraining. Since this method relies on importing external information from the pretraining dataset, this paper does a necessary discussion on the effect that the pretraining dataset has on the model and makes a simple assumption on the standard of choosing a pretraining dataset. Specifically, this paper focuses on the case where the target is to generate animation-style characters, and it has been proven that when providing a pretraining dataset with higher diversity, the performance of the GAN is better. The result of this paper should reveal the possibility that using a specially chosen pretraining dataset can improve the result of GAN.
Sentiment analysis based offensive language identification system for code-mixed data

Hate speech identification is the process of categorising textual information into hate and non- hate speech with the goal of identifying hate speech's targeted features. The objective of this research work is to take the Dataset from FIRE 2021 shared task code mixed data that includes YouTube comments and Twitter conversations and to detect whether the messages represents the offensive or non-offensive category. To detect the offensive language sentences, various deep learning models like Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network, Gated Recurrent Unit, and hybrid model like Convolutional Neural Network with Bidirectional Long Short-Term Memory methods were utilised in this research work. The performance of all the mentioned models is evaluated using precision, recall, F1-score, and accuracy. Out of all the models, both LSTM and GRU models perform better with the accuracy of 0.81, precision of 0.85 and recall of 0.95.
Analysis of different transformer variants

Efficient self-attention models are crucial in applications where long sequences are modeled, such as documents, images, and videos, are often composed of relatively large numbers of pixels or tokens. Therefore, the efficiency of dealing with long sequences is crucial for the broad adoption of Transformers. This paper talks about four transformer variants: Longformer, Transformer-XL, Big Bird, and Star transformer. This paper makes a comparative analysis of the four varieties theoretical methods, and obtains the advantages of the four methods and their applicable fields. Longformer has lower complexity and can be used for various document-level tasks. Transformer-XL improves the accuracy by addressing context fragmentation. BigBird replaces the Bert-like whole attention mechanism with Block Sparse Attention, which has achieved the SOTA effect on many tasks with long text sequences, such as long text summaries, long text questions, and answers. Star-Transformer reduces the time complexity to and performs well in the synthetic data set.
Handwritten digit recognition using machine learning

Different people are expected to possess varying writing styles that are distinctive to their personalities. There are many aspects that make handwriting differ from person to person, which include spaces, inclination, height, basic patterns, connecting strokes, sizes and widths, markings, and ornaments, among others. However, the challenges come when the handwritten text must be converted into digital form to enhance information sharing and storage. To address this challenge, the recent past has seen the rise of reliance on machines over humans and the subsequent development of machine learning algorithms. Likewise, the recognition of handwritten text is part of the important field of research and development that appear to have a promising future. The handwritten characters recognition has gained considerable attention in the area of machine learning and pattern recognition, most recently. Several techniques have been proposed for recognizing handwritten text. In line with this, many studies have been conducted that explain techniques employed in converting textural substance from paper material to a form that is readable by a machine. Such a digital recognition system has the potential to create a paperless environment by processing existing paper documents and digitization in future. This paper presents two recent techniques based on neural networks, Convolutional Neural Networks (CNNs) and Waikato Environment for Knowledge Analysis (WEKA) and makes a comparative analysis. In this paper, we want to ensure the reliable and effective approaches to the recognition of handwritten digits. The results show that CNN is comparatively more accurate than WEKA, with an accuracy rate as high as 99.59 percent, with the lowest reported degree of accuracy standing at 88.3 percent. On the other hand, the highest report accuracy with regards to WEKA was 82.92 percent, and the lowest stood at 67.45 percent. Although there is a need for more research to be carried out, the information from the two algorithms demonstrates that the existing techniques are increasingly becoming more reliable, and CNN is leading in terms of accuracy.
Dynamic Weighted Gradient Reversal Network for Visible-infrared Person Re-identification

Due to intra-modality variations and cross-modality discrepancy, visible-infrared person re-identification (VI Re-ID) is an important and challenging task in intelligent video surveillance. The cross-modality discrepancy is mainly caused by the differences between visible images and infrared images, the inherent essence of which is heterogeneous. To alleviate this discrepancy, we propose a Dynamic Weighted Gradient Reversal Network (DGRNet) to enhance the learning of discriminative common representations by confusing the modality discrimination. In the proposed DGRNet, we design the gradient reversal model guiding adversarial training between identity classifier and modality discriminator to reduce the modality discrepancy of the same person in different modalities. Furthermore, we propose an optimization training method, that is, designing dynamic weight of gradient reversal to achieve optimal adversarial training, and dynamic weight has the ability to dynamically and adaptively evaluate the significance of target loss term, without involving hyper-parameter tuning. Extensive experiments were conducted on two public VI Re-ID datasets, SYSU-MM01 and RegDB. The experimental results show that the proposed DGRNet outperforms state-of-the-art methods and demonstrate the effectiveness of the DGRNet to learn more discriminative common representations for VI Re-ID.
Language Learning Styles And Strategies Of Preservice Teachers Its Relationship To Their Learning Engagement During Covid-19 Pandemic

Language teaching is a difficult task for teachers since it requires the application of proper strategies and methods to achieve efficient language learning. This study aims to evaluate how university pre-service teachers learned languages and how they engaged in language learning during the COVID-19 Pandemic. 100 University pre-service teachers for the second semester of the academic year 2020–2021 took part in this research. To comprehend the factors, the researcher performed analysis. Descriptive statistics were used to analyze the profile variables and interpret the acquired data, including frequency counts, student data, mean, and standard deviation. The respondents' significant mean scores when categorized according to their profile were examined using sample T-test and ANOVA. Through Google Forms, significant data were gathered and recorded. With a categorical mean of 4.32 (sd=0.05) and a descriptive interpretation of Very High, the findings indicate that Teacher-Modelling Items are the preferred learning method for the majority of students. With a categorical mean of 4.09 (sd=.58) and an emotional learning style, learners' learning engagement throughout the COVID-19 Pandemic can be demonstrated. In summary, instructors' use of examples helps students learn knowledge quickly. The learning of university-trained pre-service teachers is governed by emotion. It is advised that instructional tactics match up with students' learning plans. Additionally, in order to effectively meet the needs of the students, teachers must devise a variety of activities and appropriate teaching materials.