Reviewerly/exHarmony huggingface dataset:
Precise Human Removal and Inpainting Using Mask RCNN and LaMa

Sometimes people are not supposed to be in a photo for various purposes, but this is usually unavoidable. Therefore, in the postprocessing of the image, it can be solved by removing people from the picture without affecting the coherence and naturalness of the object and background in the photo. We propose a human removal method based on image instance segmentation and image inpainting. Firstly, we send an image into the image instance segmentation algorithm to obtain a mask covering the unwanted parts of the picture. Then we do dilatation on this mask to expand the mask region. Finally, the inpainting algorithm will take the image and the processed mask and produce an inpainted image with no human and feel natural.
Artificial Intelligence-based Musical Instrument Accompaniment System

The string learning process requires students to be able to accurately grasp the accuracy of notes and rhythms in the process of playing, and its evaluation is usually done by instructors, but long one-on-one instruction is difficult to achieve in actual teaching. In this project, through the research of audio hardware systems and digital signal processing software technology, I design a string performance recognition robot system combining hardware and software, to realize automatic accompanying practice. The hardware of this system consists of a Raspberry Pi card-type computer, recording equipment, and cueing equipment. The software system processes the audio signal collected by the microphone through artificial intelligence technology and digital signal processing technology to realize the recognition of notes, intensity, and other elements according to the frequency characteristics of string instruments. The accuracy of the user's performance is judged by comparing the recognition result with the content of the score, and the user is guided by real-time hints through the display system. The experimental results show that the system can recognize 99% of the string audio with good recognition stability, so it can give accurate and timely performance guidance to the player.
Analysis of the Factors Affecting the Price Fluctuation of Bitcoin

According to the monetary theory, this paper believes that the demand for Bitcoin mainly includes two aspects: transaction demand and investment demand. This paper further discusses the impact of different demands on the price of Bitcoin based on two aspects of demand. Transaction demand and investment demand together affect the supply and demand relationship of the Bitcoin market. The empirical results show that the volatility of Bitcoin price is higher than that of international currencies and stocks as investment tools. This article emphasizes that the price of Bitcoin is primarily affected by supply and demand.
A combined analysis system for network products based on aspect extraction and sentiment classification

With the continuous development of online shopping, more and more online shopping review data has been accumulated. These reviews contain consumers' attitudes and opinions on the products, which are of great value for mining. This research introduces a set of methods to mine review information by using aspect extraction and sentiment analysis. By analyzing the aspects involved in the reviews and the sentiment polarity of the reviewers, the potential opinions on the products can be summarized and presented to the merchants so as to understand consumers' preferences and make reasonable improvements to the products. This research combines and adopts several techniques, such as aspect extraction, paragraph vector and sentiment classification, to build a complete process which can handle review text and get the results of review aspects and sentiment polarity. By taking an Amazon review dataset as an example and applying this process, the relevant aspects and sentiment polarities contained in the reviews are obtained, which reveals that the idea of this research is feasible.
ResNet50 Based Classification on Mnist and Cifar10

Convolutional neural network (CNN) is gradually trending in the past few years due to its learning capacity and great generalization power. CNNs use a different version of regularization, which makes it on lower extreme. In this task, we studied the performance of Residual neural networks (ResNet50) on Mnist and Cifar10 datasets. We built the model mainly based on ResNet50 with cross-entropy loss. Then we trained Mnist and Cifar10 datasets on it separately and evaluated its prediction accuracy. The prediction accuracy of this model on the test set of Mnist and Cifar10 is 35.87% and 95.35% respectively.
A Survey and Test of the Face Detection Model Based on Yolov5

Major surveys and papers illustrate how deep learning model performance in face detection and output many excellent tools and model to solve the problem. It boosts many people trying to use them on specific issues; they need to train the model and get the hyperparameters. However, many people are not familiar with the structure of the model, and there are plenty of parameters that make experiments more complicated; the report tries to collect and conclude some methods of experiments. This report provides a survey of the recent research in deep-learning-based face detection and the corresponding form of design of the investigation. At first, this work will do a literature review about the deep learning model, separated into three kinds (CNN R-CNN SSD). They all symbolize their series and illustrate why the work recommends the Yolo series as a pipeline. Then, the work tries the most straightforward method, OVAT, which analyzes the results, then has a discussion, and tries to give an idea for future work.
Abstractive summarization of COVID-19 with transfer text-to-text transformer

As a classic problem of Natural Language Processing, summarization provides convenience for studies, research, and daily life. The performance of generation summarization by Natural Language Processing techniques has attracted considerable attention. Meanwhile, COVID-19, a global explosion event, has led to the emergence of a large number of articles and research. The wide variety of articles makes it a perfect realization object for summarization generation tasks. This paper designed and implemented experiments by fine tuning T5 model to get an abstract summarization of COVID-19 literatures. A comparison of performance was shown to prove the reliability of the model.
Style-based Image Manipulation Using the StyleGAN2-Ada Architecture

Style-based image manipulation is to fuse the types of two arbitrary images, which is a popular task in computer vision. StyleGAN is a sophisticated architecture for generating images of high qualities. The framework allows the generator to operate on a latent space that is disentangled and allows us to do scale-specific manipulation on the semantic information of the generated images. In this paper, the author managed to fuse the styles of two given images on a controllable degree. The resultant images have natural appearances approximating real human portraits. Our method provides qualitative results for style-fusion of two given images, which achieves satisfy. Since StyleGAN offers an unraveled latent space representing disentangled semantics, the author hopes to use it on tasks like GAN inversion and manipulate images in a fine-grained control, which is the future work.
The Security Algorithm ESS-BR22-002 for enhancing the security of the data

The information age data is impressively more critical in open life, since people's prosperity data just finished up whether or not COVID'19 affected, and besides associated with all clinical issues data. These data used to inspected and expect the clinical issues data by Machine Learning Algorithm, and a while later expected data need more prominent security. Along these lines, we applied the ongoing procedure ChaCha method and that system focused in fig-uratively speaking "encryption execution" so security is less. In this paper, to apply the new ES-BR22-001 methodology and this strategy has 8 stages. The 1st stage is finding the K value. The 2nd stage is applying the K value, that is n is the order of matrix and Sk is start from intial values. The 3rd stage is find the Sk values. The 4th stage is to apply the Sk values in the sparse matrix. The 5th stage is sparse matrix values is converted into single line. The 6th stage is pair all the values. The 7th stage is all pair values will be apply in the matrix. The fi-nal stage is apply the Salsa operations in the matrix. The new ESS-BR22-002 method has provide security and performance are good while compared to ChaCha method.
Yoga Pose Estimation along with Human Posture Detection using Deep Learning Approach

The yoga is the ancient art for keeping the human body healthy and fit and acquiring mental peace. This position in yoga almost matches with human posture for comfortable physical activity. The different sequence of body joint action leads to a specific yoga asana that has its own benefit for health. If asana are done in improper way, it would create a bad impact not only in health but also in mental peace. The advancement in computer vision technology help the yoga practitioner identify if they are doing right asana by applying various machine learning and deep learning algorithms for pose estimation. This article has done the concise literature survey on different machine and deep learning algorithm available in computer vision technology. The survey portraits the different classification for human posture estimation and yoga pose estimation along with their contributions and specific difference in between them. Especially the CNN, RNN and LSTM algorithm evolution is briefly narrated in this concise survey for yoga pose estimation.
Sign Language Translation using Tensor Flow Model Zoo

Communication is very essential for all humans because it allows us to share or express our sentiments, emotions, and other thoughts. Humans communicate with one another via natural language (for example words), body language (hand gestures, facial motions, and so on) or writing etc. People who do not have any impairment can easily converse with each other in natural language. However people with impairments, such as deafness or blindness, suffer a communication hurdle. They converse with common people by adopting sign languages, which are difficult for normal people to grasp or understand. People with hearing and speech impairments often have a very difficult time conversing with other people without any translator or interpreter. As a matter of fact, this research is being undertaken that transforms sign language into text that is easily understood by the ordinary individual. This system will identify numbers, alphabets, and hand gestures as well. Our primary purpose is to remove the obstacles that exist between the deaf, dumb, and the rest of people.
EmpAMPS: A Secure Biometrics Framework for Mitigating the Menace of Ghost Workers in Developing Economies

Strong inclinations towards controlling and monitoring employees are becoming a more prevalent feature of working life. The success of any organization is directly proportional to the contributions of the workers either from executive level or operational level. The objective of managing workers attendance in working place is to improve profits and to minimize costs. Biometric authentication is an exciting technical improvement in recent times over other methods of human. A secure Employees Attendance Management framework (Em-pAMS) with remote monitoring that curtails the influx of ghost workers in public civil service integrated with a web-based, real-time monitoring tool that allows people in management positions to see when their employees check in and out, for informed decision-making during performance appraisal that avoids buddy-punching is presented. One major strength of the proposed system is that it provides a data security structure for ensuring the security of biometric templates (the fingerprints acquired from workers during the enrolment phase.
Secure Electronic Commerce Transactions Using Pythagorean Triple-based Cryptography and Audio Steganography

The rapidly increasing utilization of Information Technology (IT) in electronic commerce and other aspects of human existence has led to a rise in worries about the privacy and safe-ty/security of those involved. Due to the alarmingly high frequency with which data or user privacy breaches occur today, conducting online transactions can represent a major threat to buyers (and even business owners') privacy. Existing solutions to these security issues are still vulnerable to classic phishing scams. As a result, the construction of a robust and effi-cient audio steganography system for secure electronic commerce transactions is presented in this work. The developed system first encrypts sensitive financial data using the Pythagorean Triple-based Cryptographic (PTC) algorithm, then compresses the resulting cipher-text using the Lempel Ziv Welch (LZW) and Huffman's Compression Algorithms, and finally embeds the compressed file (using one-dimensional Discrete Cosine Transform (DCT)) in a suitable audio-file cover to produce an output (stego file) that is indistinguishable from the cover file. This output can then be exchanged between communicating entities across open networks. The new system has a little or no alteration in its outputs, as evidenced by the Signal-to-Noise Ratio (SNR), in the experimental results gotten.
Insights into Cryptocurrency Market Prices Fluctuation Correlated to COVID-19 Pandemic spillovers: Whether Bitcoin Can Act as a Safe Haven

In this article, I examine the relationship between COVID-19 daily additional cases and Bitcoin daily closing prices by applying Quantile-on-Quantile Regression (QQR) approach. The results reveal that the changing intensity level of COVID-19 does have an impact on Bitcoin prices. Whats more, my research captures the dependence between the distribution of Bitcoin prices and the severity of the COVID-19 pandemic. Levels of COVID-19 cause a rise in Bitcoin prices. I give a fresh look at combining every day confirmed number of new infections of COVID-19 and behavioral economics with fluctuating daily Bitcoin prices. And as the confirmed case has stabilized, although it is still at a relatively high position, investors would develop speculative psychology and take abnormal and seemingly inexplicable actions, e.g., they prefer to invest in Cryptocurrency rather than hold US dollars cash. Bitcoin offers a relatively steady performance in enduring the pandemic shocks; thus, it can be considered a safe-haven asset.
Facial Expression Detection Using Local Binary Pattern And K-Nearest Neighbor Methods

Facial Expression Detection is the recognition of a pattern where the input is a digital image and the output is a label of a person's emotions that have been made into a class, which class has been stored in the database as training data to find the closest or similar. Pattern recognition with training data or similar classes is done using artificial intelligence with various methods. This study aims to test the Local Binary Pattern and k-Nearest Neighbors methods to be implemented in facial expression detection and create a system on a computer to be able to know human facial expressions are happy or sad. Local Binary Pattern is defined as the ratio of the binary value of the pixel at the center of the image to the 8 values of the surrounding pixels. K-Nearest Neighbors algorithm with supervised learning which aims to find new patterns in the data by connecting new data patterns with existing data patterns. Based on the results of manual testing on sad expressions, the accuracy is 90% and happy expressions are 80%. Furthermore, the K- Fold Cross Validation test, at 5-Fold Cross Validation at 61.66% and at 10-Fold Cross Validation at 75%.
From STEM to STEAM: The Connections and Fostering of Creativity in STEAM

Aiming to understand the connections between STEM and STEAM education, and why using STEAM can be more creative for students, this review analyzes literature on STEM and STEAM especially as applied to mathematics and music education. The research questions are (a) why creativity in STEM is important; (b) How to relate STEM with arts (STEAM); and (c) Why STEAM is important in foresting students creativity. STEM - Science, Mathematics, Engineering, and Technology - was launched as an initiative by the National Science Foundation (NSF) (Hasanah, 2020). STEM education has attracted much attention in recent years, but how to teach STEM in a way which fosters creativity is unclear. However, STEAM (Science, Technology, Engineering, Art, and Math) education provides a potential way to foster students creativity and innovation during teaching and learning STEM (Liao, 2016). This study explores the literature on limitations of teaching creativity in STEM and why it is important to relate arts (STEAM) in STEM education. The findings of this study show that STEM can be more effective when related with arts, but the practical strategies as articulated in the extant literature are still limited.
Examine the Widespread of Internet Slang EMO: The Role of Social Media in Dissemination of Negative Emotions

EMO is an emerging and popular internet slang that refers to a negative emotional state. Via content analysis and interview, various perception toward EMO was found in this work. The fundamental motivation to use EMO includes the crowd mentality on social media and its linguistic functions. The application of EMO varies by the social media affordances, such as editability, visibility, and identifiability, and is supported by individuals' personal preference for expression and psychological purpose of self-disclosure. Nevertheless, some users refuse to use EMO to express their emotions. The abuse of EMO is perceived as a "dilution" of emotions. Despite different perceptions of EMO, individuals share the consensus that EMO effectively facilitates interpersonal communication as an internet slang, but it is insufficient to dissipate one's negative emotions. However, the massive application of EMO reflected an increase in the public's acceptance and respect toward negativities. Verbalizing and externalizing emotion discharge the unpleasant unconscious feelings silently modifying the emotional well beings of teenagers.
The trend advantages and disadvantages of short video in news communication of China

In the current era, the rapid development of new media makes it more closely related to news. Compared with the era of traditional media, news on new media shows significant advantages in news collection and dissemination. New media, especially the short videos, has the characteristics of easily production, strong effectiveness and strong sense of user participation, which is very in line with the needs of news transmission. Short video news has developed rapidly in various new media platforms. However, at the same time, too fast development and low threshold of popularization have also brought many negativ^roblems. Moreover; with the gradual decline of traditional media, traditional media must catch up with the trend of the times and participate in newgpedia. At this time of convergence and integration of media, how to continue to make best use of the advantages of new media and avoid the disadvantages has become a problem worthy of exploration.
The Application of Project-Based Learning in Chinese College English Education

Project-based learning (PBL)originated in the 1950s and has been widely applied to the education field. After reviewing previous research, this paper found that in input classes: 1) The ability to retrieve and read academic papers, critical thinking, analytical abilities, team organization and coordinated ability of students have been shown to improve. 2) There are still existing weaknesses such as task building and requirements on basic English level. In output classes, the paper found that: 1) The reading skills of undergraduates enhanced and the ability to use vocabulary and grammar correctly gained great progress. 2) Learners lack of motivation is still a problem. Moreover, this paper also provided some possible research directions for future PBL studies, and they could address some solutions or could develop comparative studies of PBL application in different countries. In this paper, college English classes in China will be divided into input classes and output classes. The ability to retrieve and read academic papers, critical thinking, analytical abilities, team organization and coordinated ability of students have been shown to improve, but there are also existing weaknesses such as tasking building and requirements on basic English level in the PBL applied input English courses in Chinese colleges. Likewise, the application of PBL in output classes has shown that not only have the reading skills of undergraduates enhanced, but the ability to use vocabulary and grammar correctly has also gained great progress. However, learners lack of motivation is still a problem, according to which there are also solutions at the end of this paper trying to solve the existing problems found in the application of the PBL model in Chinese English courses.
Analysis on Existing Project-Based Learning Application in Various Education Levels and Technology- Integrated Project-Based Learning

Project-based learning is a new and high-quality teaching methodology that has been put into use in the past few decades. Through the use of literature review and case analysis, the paper evaluates how PBL and support learners study in all levels of education and how emerging education technology is integrated into PBL, aiming to elevate all stages of project-based learning.
The Variation of the Meaning of Emojis Taking WeChats stickers as an Example

The study of the semantics of emoji is, in general, considered from a static perspective. Even when emoji are studied from a dynamic perspective, generational differences are rarely taken into account. An analysis of five typical and controversial emoji in this paper reveals a significant age gap of the usage of emoji.
Enter the chatbot revolution

Theoretical physicist Matt Hodgson from the University of York talks to Hamish Johnston about the role that large language models, or chatbots, can play in physics.
Python-based Model Optimization Platform

With the increasing degree of informatization in today's society, the presentation of problems has become more complex, which puts forward higher requirements for people's ability to solve problems. Python is a popular language recently, and it is very popular among developers because of the many mature libraries that are encapsulated in it. People can use related libraries in Python and use open-source related libraries for algorithm research. The main purpose of this paper is to study the optimization platform of the model based on Python. This paper mainly analyzes the characteristics of the Python language and the structure of Python programming, and uses the relevant database of Python to realize the modeling work. The experiment shows that the accuracy of the decision tree model is 96.94 %, the accuracy of the KNN classification model is 89.05%.
A Multi-modal Discourse Analysis of WeChat Emoji Usage in Different Age Groups

As an auxiliary chatting method, WeChat emojis have played an important role in peoples language communication. As an emerging field, it has received extensive attention from linguistics in recent years. Based on the language meta-functions of Halliday, this paper will combine emoji with different age groups of people, and use multi-modal discourse analysis to deeply explore the ideational function, interpersonal function and textual function when different age groups use WeChat emoji and refine their internal reasons.