Reviewerly/exHarmony huggingface dataset:
Movie information AI question answering system based on knowledge graph

Knowledge is essential for understanding. The ongoing explosion of information highlights the need for electronic texts that allow machines to better understand human language. The explosion of information in various fields has greatly changed our lives. Among them, the information explosion of the film is particularly prominent. Nowadays, people's requirements for high quality of spiritual life are increasing, and people's demand for movies is also increasing. With the extensive coverage of the Internet, the amount of movie-related information is also increasing, so how to design and build a question-and-answer system about movies is more and more urgent and important. At the same time, the original intention of the knowledge graph is to express various entities, concepts that exist objectively in the real world, and various relationships between them. It can better understand the abstract components in the language, and better to confirm the information through the interaction of the ambiguous parts of the language. In addition, the upgrade of deep learning natural language processing technology allows us to better understand and analyse language. In view of the above advantages, the movie question answering system in this paper is constructed based on the knowledge graph and uses natural language processing technology.
Research on the evolution of neural networks models in NLP

Since their first emergence, neural networks have produced significant results and have been an essential tool for solving natural language processing (NLP) problems. This paper aims to provide insight into the development of neural network models and their application in various NLP problems. The efficiency of the different approaches would be analyzed and insight into the different models would be provided. Overall, neural networks have been proven to be essential for solving problems related to NLP, and it is believed that further advancements in newer models would see a surge in performances of questions that were currently considered hard.
Discussion on image recognition under different conditions of Resnet and Densenet

As two mainstream and excellent neural networks, ResNet and DenseNet have been the main research directions of many scholars and papers. This paper mainly focuses on these two neural networks through a theoretical analysis, summary discussions, and practical experiments. The author explains their previous advantages and disadvantages, and, as a potentially obsolete technology, whether ResNet will be better or more efficient than DenseNet in some cases. The final experimental results show that although ReseNet is a somewhat outdated neural network model, it still outperforms DenseNet in some aspects. In addition, this paper provides some further optimization schemes for reference through the analysis of the two neural network models. At the same time, the basic structure and algorithm principle of the two neural networks are further explained in the analysis to make it clearer for readers.
The development of transformer in vision

Transformer is a deep neural network that utilizes a self-attentive technique to process data in parallel. The area of vision was neural network based before Transformer, with great frameworks like faster-RNN, YOLO, etc. Transformer, which was developed to stop this phenomena, is discussed in this article together with its accomplishments in the field of vision during the past few years and its projections for the future in order to provide some references for more research.
Real-time vehicle detection and tracking based on the combination of YOLOv7 and ByteTrack

Real-time tracking of vehicles is important for monitoring whether the roads are congested or not. How to achieve and maintain a high frame rate is a key problem to be solved in practical applications. In recent years, SORT, BOT-SORT [1] and other state-of-the-art target tracking algorithms have achieved fruitful results in target tracking tasks. The author combines YOLOv7(You Only Look Once v7) with bytetrack and compares the frame rate with some popular algorithms like Deepsort and bytetrack in real-time tracking to demonstrate that YOLOv7-bytetrack is more suitable for real-time detection, tracking of vehicles.
Light-weight YOLO based object detection algorithm for unmanned aerial vehicle

Object detection algorithms based on deep learning usually have good results in terms of speed and accuracy on GPU- based computing platforms. However, as this kind of algorithm is not perfectly supported for CPU-based Unmanned aerial vehicle(UAV), the object detection algorithm usually used in UAV has the problem of slow detection speed, which will lead to traffic accidents, traffic congestion, and other problems. To solve this problem, we proposed an object detection algorithm based on YOLOv5. Firstly, aiming at the problem of light- weight model architecture, mobilenetv3 was added to YOLOv5 to replace the original backbone. Secondly, in order to maintain a high detection accuracy, omni-dimensional dynamic convolution was added in the feature fusion part of the network as a replacement for stander convolution. Through the architecture analysis, the proposed algorithm solves the problem in the UAV traffic monitoring system.
The application of probabilistic programming in statistics

This article focuses on three aspects of Probabilistic reasoning systems for applications, including Predict future events, Infer the cause of events, and learn from past events to better predict future events. In addition, the article explains that the data will become more accurate as the number of samples increases, and finally, the article describes the application of Turing-Completed Systems in statistics, a kind of probabilistic programming system. This article starts with a soccer competition as an example, then will explain the concepts in depth behind the example.
Approaches to complete coverage path planning using neural network and grid division

Since the goal of using complete coverage path planning is to generate a continuous and uninterrupted path that covers an area of interest while avoiding obstacles, its use could be extremely vital in today's field of robotics. Not only could it help our daily lives like lawnmowers, window cleaners, and painter robots, but it could also solve some dangerous or complex but vital problems for human beings; For example, mine detection, vacuum cleaning, and photogrammetry. This paper proposes two paths of successful approaches to Complete Coverage Path Planning: Neural Network and Grid division. After detailed data comparisons, both proved to have been efficient and successful, respectively. In addition, both plans' field applications would be placed at the end.
Generative adversarial network based image inpainting

Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are listed. After that, the evaluation metrics, and common datasets of deep learning-based image inpainting are listed. Finally, the existing image inpainting methods are summarized and summarized, and the ideas for future key research directions are presented and prospected.
Prediction and processing of natural language

With the rapid increase in the computing power of electronic computers and the dramatic decrease in the cost of manufacturing, researchers are refocusing on the challenging research field of natural language processing (NLP). Therefore, in a natural language training development as the main line, from the following several aspects work: first of all, on the basis of preliminary training technical update route, this paper introduces the traditional natural language training in advance technology and training neural network prediction technology, and analyse the related technical features, comparison, generalize the development and trends of natural language processing technology; Secondly, this paper introduces the improved natural language processing models based on BERT from two aspects, and summarizes these models from the aspects of pre-training mechanism, advantages and disadvantages, and performance. Furthermore, the main application fields of NLP are introduced, and the current challenges and corresponding solutions of NLP are described. Finally, this paper summarizes the work and predict the future development direction of NLP.
Peluang Pemanfaatan Big Data di Perpustakaan

Artificial Intelligence (AI) technology has the opportunity to be applied in libraries to improve services and meet user needs. AI can be used to speed up the process of finding information by users and making recommendations for collections of relevant library materials. This research will discuss how AI can be used in libraries, including the implementation of virtual reality, chatbots, voice recognition systems, robotics, and analysis systems. We will also cover documentation of implementing AI in other areas that have opportunities for implementation in libraries. The type of this research is qualitative using descriptive methods. Data sources were obtained from books, journals, activity reports, and websites that match the topic of the problem. This study found several examples of AI implementation in libraries, both those that have been done and those that have never been done and will be the future of libraries. The concept offered discusses how AI can work with librarians in providing better and more efficient services for users.
An improved hibbard interpolation algorithm based on edge gradient feature detection using Sobel operator

Color interpolation algorithms have important applications in color image processing, and many interpolation algorithms have been proposed one after another. The bilinear interpolation algorithm, Cok interpolation algorithm and gradient edge interpolation are three classical interpolation algorithms, however, they may cause problems such as zipper effect and Mohr's stripe effect. In this paper, this paper optimize the gradient edge algorithm, calculate the gradient by using Sobel operator and fit the direction of the edge in a weighted way, and propose a gradient edge weighted interpolation algorithm based on Sobel operator to improve the performance of the interpolation algorithm. Through the validation of color image recovery, the results show that the subjective visual effect and objective evaluation indexes such as structural similarity index, peak signal-to-noise ratio and minimum mean square error of the improved algorithm proposed in this paper are better than the traditional algorithm, which is of great significance to the improvement of the interpolation algorithm.
Wiener filter and wavelet transform of noisy image analysis based on MATLAB

In recent years, how to filter noise more effectively has been a topic of great interest, and improvements have been made based on various filtering algorithms in the hope of obtaining the most optimal filtering effect with the least amount of computation. However, there is a considerable research gap in how to achieve this goal. This article is based on this research. The motif of this study paper is the analysis of images containing Gaussian noise based on Wiener filter and wavelet transform, and an attempt to improve the filtering algorithm. The SNR and PSNR obtained by applying Wiener filtering analysis are 46.7864 and 76.0247, respectively, and the SNR and PSNR results of wavelet transform are 14.0851 and 49.7426. this article implements Wiener filtering based on Wiener method, and the results are almost consistent with the function of invoked system. It is found that the Wiener filter is more suitable for handling processing Gaussian noise. The SNR and PSNR values of both sets of experimental Wiener filter are larger than those of wavelet transform, then it indicates that Wiener filter is more beneficial to suppress Gaussian noise under the same condition.
Image semantic segmentation using deep learning technique

With the deepening research on image understanding in many application fields, including auto drive system, unmanned aerial vehicle (UAV) landing point judgment, virtual reality wearable devices, etc., computer vision and machine learning researchers are paying more and more attention to image semantic segmentation (ISS). In this paper, according to the different region generation algorithms, the regional classification image semantic segmentation methods are classified into the candidate region method and the segmentation mask method, according to different learning methods, the image semantic segmentation methods based on super pixels are divided into fully supervised learning method and weakly supervised learning method. The typical algorithms in these various categories are summarized and compared. In addition, this paper also systematically expounds the role of DL technology in the field of ISS, and discusses the main challenges and future development prospects in this field.
Comparison of algorithms that use deep learning to classify spam

Although the application of network security protocols and cryptography provides a certain security guarantee for Internet surfing, it is difficult to cure the persistent security problems. Driven by the promotion of e-mail technology and benefits, bad businesses will also issue promotional emails indiscriminately to a large number of mailboxes, and even drive the underground industry of private mailbox information trading. The existing spam filters use black and whitelists, sensitive word matching and other technologies, but they can not effectively filter all forms of spam, and non-spam is often filtered, which brings more trouble to users. With the rise of artificial intelligence, machine learning algorithms have been applied to spam recognition, such as decision tree algorithm, Boosting algorithm, K nearest neighbour algorithm, SVM support vector machine algorithm, Bayesian principle related algorithms, etc. These methods based on traditional statistics can intelligently classify data sets with large differences and are often used together with expert systems with certain rules to classify spam. However, with the diversification of spam types, the old classification rules are relatively rigid, and new types of mail will be misjudged. In addition, statistics based natural language processing method is based on pre trained fixed dictionaries. For new words and polysemy words, it is impossible to give word vectors with accurate semantics, which brings difficulties to classification. This paper mainly studies the application of five machine learning algorithms in spam detection: improved naive Bayes algorithm, A Lite Bidirectional Encoder Representations from Transformer (ALBERT) dynamic word vector algorithm, Bidirectional Gating Recurrent Unit (BiGRU) algorithm, the Inverted Multi-Index with Weighted Naive Bayes (IMI-WNB) algorithm and clustering analysis algorithm.
Recognition of feline images based on deep learning

As the study of felines continues, image recognition technology is helping researchers identify species more and more. However, the research on cat image recognition based on deep learning is not deep enough in the past, which leads to the low efficiency of researchers and the inability to achieve the purpose of accurate recognition. According to researchers, there are more than 500 million pet cats in the world, of about 40 breeds. Predictably, the popularity of cats has led to an increasing amount of research on cats. On the basis of the existing background, this paper uses the deep learning network based on the PyTorch framework to study this topic and conducts in-depth exploration which finally has high efficiency and accuracy. It is hoped that this study can improve the research efficiency of related researchers.
An overview of image enhancement dehazing algorithms

This dissertation is an overview of Image dehazing algorithm that is utilized to process hazy images through certain technologies to remove the image haze occlusion and interference, improve the visual effect of image. For example, the contrast, color and detail and other aspects. The research method is literature review. The image enhancement algorithms mainly include histogram equilibrium, homomorphic filtering, wavelet transformation and Retinex method. The of these algorithms will be discussed detailly in the following sections of the article. The conclusion is that due to the error of the parameter information in the image with fog, the current defogging algorithm is still unable to achieve perfect results.
Convolutional neural network and vision transformer for image classification

Visual Transformer (ViT) has been a hot topic for research for the past few years after it first emerged in the field. On image recognitions, due to the amount of information ViT could retrieve from the source image, in cases it can rival the traditionally prevailing Convolutional Neural Network (CNN). Then there emerged different models based on ViT, all being built having a specific field or a flaw not addressed by original ViT in mind. In this paper these models are being tested on the same dataset along with a standard CNN to see how they perform compare to each other, and the best performing ViT model was then changed to see how there would be some possible improvements.
Investigation of Primary School Teachers' Lifelong Learning Dispositions and Technological Competencies

It is thought that it is significant to investigate to what extent teachers have educational technology competencies, to what extent they can improve themselves and to what extent they have lifelong learning skills. In this context, lifelong learning dispositions and technological competencies of primary school teachers were examined in this study with a comparative and relational approach in terms of some variables. The study, in which quantitative research methods were employed, was conducted on 270 primary school teachers working in different cities in Kazakhstan. Lifelong learning dispositions and technological competence perception scales were used to collect the research data. According to the findings of the study, lifelong learning dispositions and technological competencies of primary school teachers were found to be at medium level. Lifelong learning dispositions and technological competencies of primary school teachers differ significantly according to gender and age variables. Finally, a significant and positive relationship was found between lifelong learning dispositions and technological competencies of primary school teachers.
Establishment of Soil Quantitative Detection Model Based on Sparrow Search Algorithm

In recent years, with the rapid economic growth in my country, environmental problems have become more severe, among which the problem of heavy metal pollution in soil is particularly prominent. Soil is the material basis for human survival. In order to ensure human health and achieve sustainable development, the control and treatment of soil heavy metal pollution is imminent. Based on the sparrow search algorithm, this paper establishes a soil quantitative detection model, and uses the algorithm’s population optimization performance to detect soil heavy metal content. And a detection model based on the improved algorithm was proposed, and the detection accuracy of the models under the two algorithms and the relative error of each heavy metal element were compared, and the effectiveness of the improved sparrow search algorithm in soil quantitative analysis was verified.
YAPAY ZEKA (AI) TEKNOLOJİLERİ FİNANSALLARI DOĞRU BİR ŞEKİLDE TAHMİN EDEBİLİR Mİ? CHATGPT KULLANILARAK BORSA İSTANBUL ÜZERİNDE BİR ARAŞTIRMA

This article analyzes to what extent ChatGPT can accurately estimate financial values by interpreting past financial statements and current important developments in the sector.Within the scope of the research, the financial data of 73 companies traded in the BIST100 index and the texts that summarize the sector-specific developments obtained from the Microsoft BING Chat application were used.In the first stage, ChatGPT was asked to interpret the financial values for the 2019-2021 period and make estimates for sales, net profit, and dividend distribution for 2022.In the second stage, text about sector-specific developments was entered into ChatGPT and it was asked to revise its estimate.Afterward, the estimations made by ChatGPT in these two stages were compared with the realizations and it was tried to understand how successful ChatGPT was in these estimations.According to the results of the research, ChatGPT was able to estimate the sales values with 18% deviation as median values in the first stage, when it used only the financials, and with 18.9% deviation in the second stage, when it used both financials and the current developments in the sector.The median deviation of the estimates for net profit in the first phase was 57.8%, while it was 64.9% in the second phase.The median deviations for dividends were 54.6% in the first stage and 51.8% in the second stage.According to the research findings, it has been observed that ChatGPT has experienced a serious development process, although its financial and news analysis capabilities are limited.
Better Understanding Diverse End User Website Usage Challenges with Browser-Based Augmented Reality Approaches

Software engineers are usually quite different from their end users, especially those with a variety of accessibility related usage challenges. These include, but are not limited to, sight, hearing, cognitive, mobility, hand control, age, language and many others. A popular approach to assist developers in understanding and designing for these diverse end user accessibility challenges are ‘Augmented Reality’ (AR) browser-based plug-ins. These attempt to mimic how a user with a particular challenge or set of challenges will perceive viewing and interacting with a target web site. We review work on developing such plug-ins, summarise some of the popular AR brower-based plug-ins designed to support accessibility design and evaluation, and report results of a developer survey we conducted on their requirements and usage of such tools. We then report a detailed heuristic evaluation of a popular example, Funkify, and discuss the performance of several of its simulators on commonly used web site exemplars. Finally we identify and report a range of future research needs in this area.