Reviewerly/exHarmony huggingface dataset:
Machine Learning in Geology: Challenges and Prospects

Machine Learning methods, along with high-performance computing, is creating chances for data intensive science in various domains such as Geology. Known for its precision and efficiency, Machine learning has renovated many fields of science thoroughly. However, when it comes to Geology, Machine Learning methods aren’t fully applied, lacking an overall frame to arrange those works with scattered aims and methods. This paper presents a comprehensive review of research focusing on application of Machine Learning methods in Geology studies. The works analyzed were categorized in three aspects of Geology studies: mineral prospecting and mapping, land-cover monitoring; geochemical anomalies detection; earthquake monitoring and geological sample identification. The selection and classification of the presented articles demonstrate how Geology benefits from Machine Learning methods.
Reflecting the Study of Foreign Language Learning Motivation in China from 2012 to 2021

This paper reviews the articles on foreign language learning motivation published in the core journals of Peking University and on CSSCI in the past decade from 2012 to 2021. The articles are collected from 31 journals, mainly from the theoretical research and empirical research on foreign language learning motivation in China, and from the development trend, research content, research object The research methods and tools are statistically analyzed. The results show that the number of studies on foreign language learning motivation in China has generally declined in recent years. Also, the number of empirical research is generally more than that of theoretical research. Then. the number of articles about the relationship between motivation and learning effectiveness is the largest in the research content. The overwhelming majority of the literatures studied by questionnaire alone or jointly; Among the research objects, undergraduates are the major groups that being researched as participants. Finally, this paper reviews the current situation of foreign language learning motivation research in China.
The Causes of the Cyber Violence Problem Taking Qzone as an Example

After entering the internet age, cyber violence has appeared as a new type of violence and has attracted the attention of many researchers because of its frequency and serious consequences. This paper takes the users of Qzone as the research subject and uses a questionnaire survey and textual analysis to investigate the causes of the formation of cyber violence. The researcher concludes that cyber violence can be attributed to the anonymity of the internet, high sensitivity and impulsiveness of users, and the inadequacy of the law. The anonymity of the internet reduces people's sense of shame and makes perpetrators feel protected while the high sensitivity of the users makes them more susceptible to verbal stimuli that can provoke a violent counterattack. Furthermore, China's laws on cyber violence are not yet complete and the division of responsibility is not yet clear. Many people believe that punishment can be avoided and they have no qualms about venting their malice online. This study will help people develop a concrete understanding of the causes of cyber violence and better countermeasures could be taken to reduce the occurrence of cyber violence in many aspects, involving government, platforms, existing users, and potential future users.
The Unique Aesthetic Experience of VR Technology

VR as an emerging medium holds potential as a new visual form that has applications in diverse fields including but not limited to educational, artistic, or entertainment. New elements of presence, immersion, and interactivity characterize a completely new aesthetic experience for its users, which will be explored in this paper. Aesthetic experience is defined to be the experience that differs from everyday life, in which psychological focuses are completely on the subject at hand. Different aspects of the aesthetic experience include: fascination, or the user’s attention analyzed through lenses of physical and psychological; appraisal, or the cognition of the narrative story and compositional elements within VR works; and creativity, or the user’s ability to interact and create within virtual worlds. Through research and cumulation, this paper will conclude and create a holistic view of the future of VR and the aesthetic experience.
Towards Extreme Weather Detection: Dark Channel Priori Enhanced Detection with New Labelled Data

With more and more cars entering our lives, the number of car accidents is also increasing. To solve this problem, driverless technology has entered our field of vision. An effective way to realize unmanned driving technology is the application of deep learning. At present, the practice of various large companies in this field is mainly to realize the integrated deep learning of image recognition, judgment, and response on automobiles. The model performance is satisfactory in normal condition. However, if the vehicles encounter some extreme weather, e.g., foggy, it is difficult to ensure the safety of deep learning model due to the insufficient accuracy of image recognition. To solve this severe problem, we advance three strategies. Firstly, considering high-capacity backbone will mitigate this situation, Yolov5 with stronger performance was used as the detector in the experiment. Secondly, image pre-process would be benefit to this foggy situation. Thus, we introduce dark channel priori defogging to our detection framework. Thirdly, since deep learning is data hungry, we labelled image collected in heavy fog weather condition and utilize them to train the network. Abundant experiment demonstrates the effectiveness of the proposed strategies. We gain a precision of 0.89734 after training the model 299 times and the safety can be guaranteed with the high accuracy of image recognition.
Research on "Impeding" in Pre-litigation conduct preservation provision of the Copyright Law

Article 56 of the current Copyright Law provides for the preservation of pre-litigation acts. Compared with Article 50 of the Copyright Law before the amendment, the main modification lies in the increase of the applicable circumstances of "Impeding the realization of its rights". This amendment extends the scope of application of pre-litigation copyright protection to the acts that impeding copyright owners from realizing their rights. However, the current law, relevant regulations and judicial interpretations do not provide for the meaning of "Impeding", and there is little discussion in the academic community. The vagueness of the term may create difficulties in its application. Therefore, the author analyses the meaning of the term "Impeding", taking into account the content and characteristics of the Civil Code and the Copyright Law. In the author's view, "Impeding the realization of its rights" shall mean that the actor has committed an act that endangers the copyright owner or the relevant right holder's enjoyment of both full ownership and the limited carrier of the copyrighted work. The judicial interpretation or relevant regulations should be made to further explain the provisions of the act of preservation.
Improve the stability of the rolling bridge by changing the shape of the internal mechanism

The London-designed Rolling Bridge inspired this study by Thomas Heatherwick to add stability to this bridge. This study aims to analyze what structures can increase the bridge's overall stability and determine the basic parameters. Finally, it revealed the structural type of this structure. This study uses force analysis, Pythagorean theorem and calculation of degrees of freedom to determine that the new structure is more stable than the original structure. In the study, the total length and number of segments were assumed to be constant to study the effect on geometry and static height during deployment. The study concluded that when the bridge's internal structure is triangular, the bridge is more stable than the original design in the folded state—the internal structure changes from triangular to trapezoidal when the bridge is extended to a horizontal position. When the bridge changes from the horizontal position to the folded state, the internal structure changes from trapezoidal to triangular. Meanwhile, hydraulic columns are mainly used to lift and bend bridges. Each structure has five nodes and five segments. Despite the increasing number and complexity of computations, the variable triangular structure is more stable than the fixed trapezoidal structure.
Genetic RRT: Asymptotically Optimal Sampling-based Path Planning via Optimization of Genetic Algorithm

As one of the most frequently used stochastic planners, Rapidly-exploring random trees (RRT) is commonly applied to the path-planning problem in the continuous state space. For single-query problems, RRTs demonstrate nice properties in computation speed and universality. However, the probabilistically complete results of the planning problems hinder their further use. To date, some heuristic-biased sampling searches like hRRT and Informed RRT* addressed the optimality problem of RRTs in the path-planning. However, using the heuristic subsets to bias sampling improved the performance of the algorithms while eliminating the probability of finding other equal optimal solutions simultaneously. To minimize the cost of solutions in the path-planning problems, this paper presents a novel method to focus on the optimization of paths planned by RRTs in terms of the genetic algorithm. The demonstrated planning technique’s advantages can be seen with a new algorithm, Genetic RRT, which retains the multi-results nature and the same effectiveness toward high-dimension path-planning problems as RRTs while significantly increasing the probability of finding an asymptotically optimal path via optimization of genetic algorithm, as the number of iterations increases.
Summary of Research on Facial Expression Recognition

Facial expression recognition allows computers to understand expressions according to human thinking. It is an important component of human-computer interaction. However, with the rapid development of deep learning, the research of deep learning technology in the field of facial expressions has also become a research hotspot, so is deep learning. It is necessary to analyze the application of technology in facial expression recognition and the results obtained. Firstly, several commonly used expression data sets are summarized; then the expression recognition methods based on deep learning are classified from the aspects of feature extraction and feature classification, and several network improvements in expression recognition based on deep learning are analyzed from the aspect of network improvement. Method; Finally, the challenges and future development in the field of facial expression recognition are described.
A brief analysis of the method of VR games to enhance immersion---- taking the game "Mind Concretization Company" as an example

Virtual reality technology has been used in many fields recently, including video game. Its unique features make it an ideal platform for immersive games. However, can’t achieve complicate operation and frequent interruption are two major problems that block PC or host game players to try on VR games. To find new solutions, we design the game “Mental concretization service center”, to increase the sense of immersion and attract more players. In contrast to other pure puzzle-solving games or exploring games, our project focus more on the plot, aiming to create an emotional bond between players and protagonist and guide players to reach their own ends. For each level, the scenes are accord with character’s personality and will change its styles to match the plot or contribute to puzzles. Moreover, we used multiple senses such as environmental sound to inspire players figuring out the puzzle, forcing them pay more attention which thus led to immersion. With the help of game engine, we built our first version and tested it in a small scale. The result was encouraging, showing that our methods had some preliminary effects on improving immersion.
Crowdfunding using Blockchain Technology

Crowdfunding is an online method of raising funds that was initially intended for individuals to contribute small amounts of money to support innovative projects. This fundraising method allows people to invest in entrepreneurial ventures through an intermediary platform. However, the current issue with traditional crowdfunding methods is that the third-party intermediaries do not guarantee the money invested by the backers, and the investors do not have control over their contributions. This paper proposes a blockchain-based crowdfunding solution to provide a private, secure, and decentralized platform for crowdfunding. The main aim of this paper is to enable investors to contribute to any project efficiently by creating smart contracts that give them control over their invested funds. Additionally, both project creators and investors can effectively manage and allocate funding for the project
Survey on Vision Based Hand Gesture Interface for Controlling Multimedia Player

As computers become additional pervasive in society, facilitating natural human–computer interaction (HCI) can have a positive impact on their use. Hence, there has been growing interest within the development of recent approaches and technologies for bridging the human-computer barrier. the last word aim is to bring HCI to a regime wherever interactions with computers are going to be as natural as associate degree interaction between humans, and to the current finish, incorporating gestures in HCI is a crucial analysis space. Gestures have long been thought-about as associate degree interaction technique that may doubtless deliver additional natural, creative, and intuitive strategies for human activity with our computers. Hand gesture recognition is one amongll|one amongst|one in every of} the systemsthat may notice the gesture of the hand in a period of time video. The gesture of hand is classed inside a definite space of interest. during this study, planning hand gesture recognition is one among the difficult jobs that involves 2 major issues. foremost is that the detection of the hand. Another drawback is to form an indication that's appropriate to be used one hand at a time. This project concentrates on however a system might notice, acknowledge and interpret hand gesture recognition through computer vision with the difficult factors that variability within the create, orientation, location, and scale. To perform well for developing this project, differing kinds of gestures like numbers and sign languages got to be created during this system. The image taken from the period of time video is analyzed via Haar-cascade Classifier to notice the gesture of hand before the image process is finished or in different words to notice the looks of hand in a very frame. during this project, the detection of hand are going to be done mistreatment the theories of Region of Interest (ROI) via Python programming. the reason of the results are going to be targeted on the simulation half since the distinction for the hardware implementation is that the ASCII text file to scan the period of time input video. the event of hand gesture recognition mistreatment Python, OpenCV, and YOLO V3 will be enforced by applying the theories of hand segmentation and also the hand detection system that uses the Haar-cascade classifier
News Recommendation System Based on Multiple Classifiers

In this project, we have designed, implemented and analyzed a news recommendation system. We used news articles dataset provided by BBC for the experiments. The system trains multiple classifiers on the training set, and use them to classify the news article chosen by the user. It then recommends m similar articles to the user. In this report, we discuss the methods we have implemented, results and their analysis. We have got best results with Naive Bayes algorithm, although we use combination of all the classifiers
CHERISH: Don’t Waste Donate

Eliminating resource waste is a key objective in the modern world. To do this, local communities can reuse resources like food that may be close to expiring in restaurants, stores, and food distribution facilities as well as any perishables that aren't consumed entirely within their intended shelf life. This is very important, especially in times of crisis like the COVID-19 pandemic. This study focuses on developing a fascinating initial webapp called CHERISH that offers a universal platform where users may visualise available resource resources in their neighbourhood and afterwards acquire access to resources, so addressing two key challenges, namely hunger and resource waste. This platform meets the broad category of AI for and is relevant to the UN SDGs (United Nations Sustainable Development Goals).
Online Exam Proctoring System

The popularity and coverage of various e-learning resources and different types of distance education has been increasing day by day. The capability to successfully monitor online tests has become important for the evolution for the next stage of education. At present, manual proctoring has become the most frequently used form of evaluation, where the student/test-taker is required to go to a specific examination centre, or by proctoring tests using a webcam. Though, such techniques are not cost effective and difficult. In this project, we aim to develop a system that accomplishes the goal of automated online proctoring. The proposed system hardware comprises of a single webcam, and a mic., with the goal of tracking the audio and visual surroundings of the location of test. The model comprises of six components that always measure the behaviour of the user : user authentication, text detection, audio detection, and tab switching detection. By combining the continuous estimation components, and using a temporal sliding window, we design higher-level features to determine whether the test taker is cheating at any time during the test. To test our proposed system, we collect multimedia data (audio and visual) from the test taker performing various types of cheating while taking online tests. Extensive experimental results demonstrate the accuracy, robustness, and efficiency of our online proctoring system.
An Efficient Multi-Class Privacy-Preserving-Based Encryption Framework for Large Distributed Databases

This paper introduces a novel hybrid filter-based ensemble multi-class classification model for distributed privacy-preserving applications. The conventional privacy-preserving multi-class learning models have limited capacity to enhance the true positive rate, mainly due to computational time and memory constraints, as well as the static nature of metrics for parameter optimization and multi-class perturbation processes. In this research, we develop the proposed model on large medical and market databases with the aim of enhancing multi-party data confidentiality through a security framework during the privacy-preserving process. Moreover, we also introduce a secure multi-party data perturbation process to improve computational efficiency and privacy-preserving performance. Experimental results were evaluated on different real-time privacy-preserving datasets, such as medical and market datasets, using different statistical metrics. The evaluation results demonstrate that the proposed multi-party-based multi-class privacy-preserving model performs statistically better than conventional approaches.
Classification model of glass relics based on decision tree algorithm

In order to assist in the compositional analysis and identification of glass artefacts, to clarify the correlations and differences between the individual chemical components, and to achieve the aim of analyzing the patterns of classification of glass artefacts, a classification model was developed in this paper. The data obtained from the chemical components are first processed accordingly, and then a multiple linear regression model is established to investigate the relationship between each chemical component variable and the variable of weathering, and a few chemical components that are significantly affected by weathering are selected as the basis, and a decision tree model based on a particle swarm algorithm is constructed to investigate the classification pattern and subclasses. The final result is that glass products can be classified into high potassium glass types and lead-barium glass types according to whether the chemical composition PbO content is greater than 6.078. The K-Means classification model was also used to classify three subclasses of high-potassium glass products and five subclasses of lead-barium glass products according to the content of the relevant chemical components.
Research on speech style transfer algorithm combined with image processing perspective

Speech, as the acoustic expression of language, is one of the most natural and effective means of human information communication. With the rapid development of the Internet and communication technology, the function of robot voice interaction is more and more popular among people. However, the robotic pronunciation cannot meet people's demand for personalized voice interaction. At the same time, style transfer technology, which is widely used in image and video processing, has been relatively mature. By studying the theoretical methods of generalized style transfer technology (including the style transfer of images and video signals), and comparing and analyzing various machine learning algorithms used by the current voice style transfer technology, this paper draws the following conclusions: First, various models generally have the problem of large demand for training data and difficulty in training. Second, an algorithm model shows the alienation effect in different usage scenarios. Finally, based on the above problems, suggestions for the development of voice style transfer are put forward.
Chinese Named Entity Recognition based on ERNIE

The traditional named entity recognition model based on neural network uses static word vector, which can’t represent the ambiguity of the word in the context. The ERNIE-BiLSTM-CRF model is proposed. The ERNIE pre-training model can output different word vectors for different contexts by using multiple layers of Transformer, obtaining dynamic word vectors that contain overall sequence information. Secondly, the word vectors are input into the BiLSTM layer, which can obtain sentence context information through forward and backward LSTM and obtain more sentence features, thereby improving the model's effectiveness. Finally, the sequence is labeled through the CRF layer to obtain the globally optimal labeling information and complete the named entity recognition task. The experimental results show that compared with the traditional model, the F1 score of this model has significantly improved.
Extracting Entities from Video and Tagging them - NLP Model

In order to give consumers access to all the crucial details or the excitement of the films, video summarising seeks to create a high-quality text-based summary of the videos. In order to summarise a video, audio files must first be converted to text files, and then the video files. This whole Natural Language Processing's transformer architecture is used in conjunction with the process. Despite the fact that there have been numerous studies on text summarising, we propose our model, an extractive-video summarizer, which is based on cutting-edge pre-trained ML models and open-source libraries. The following regime is utilised by the extractive video summarizer: Creating a transdisciplinary collection of movies, extracting audio from video files, and creating text from audio recordings are just a few examples. the text summarization using (V)Entity extraction and extractive summarizers. Hindi and English, two widely spoken languages in India, were the main subjects of our research. To sum up, our model performs remarkably well and produces appropriate tags for videos.