Reviewerly/exHarmony huggingface dataset:
Design and implementation of STM32 based push box game

Over the past decade, China's gaming industry has continued to show stepwise growth. There are over 370,000 gaming-related enterprises in China, and over 20,000 gaming-related enterprises were established last year, but there is still a gap between the level of gaming development in Europe, America and Japan. The importance of portability for game carriers needs no further explanation, people can enjoy the joy of games anytime and anywhere. The project is to design an STM32 based Sokoban game that is portable, educational, and can be played anywhere and anytime by players. This paper introduces the technology and methodology of developing a STM32-based Sokoban game. Sokoban is a classic game that can be enjoyed by young and old alike, which is both brain exercise and physical and mental pleasure. The system uses the STM32L476RG board as the hardware platform, based on the Keil software development system and the C language, to complete the game design and implementation. Players can control the game through remote sensing and display. The game is accompanied by background music and game sound effects.
The combination and application of virtual reality and artificial intelligence

In recent years, virtual reality technology has gradually entered the public's vision. The rapid development of virtual reality technology has caused a great impact on people's cognition. Artificial intelligence (AI) is also a cutting-edge modern technology that can greatly affect human life. The combination of virtual reality (VR) and artificial intelligence is also seen as a new research direction. Based on this situation. By comparing with traditional virtual reality technology, this paper analyses the necessity and development situation of the combination of AI and VR, and expounds the feasibility and importance of AI+VR. At the same time, it explains how artificial intelligence can enhance the fidelity and interactivity of virtual reality technology to a certain extent. In addition, it also studies how AI can help VR in solving the current difficulties faced by VR technology. In addition, it also summarizes the application status of AI+VR, and looks forward to the impact of artificial intelligence and virtual reality technology on our life.
Quantization aware training for efficient reinforcement learning network

In recent years, reinforcement learning has been applied to many areas, especially deep reinforcement learning. Deep learning-based methods use a large computing method known as Quantization Aware Training (QAT). For this project, QAT will be used to investigate the performance of Deep Q-Networks. This method of QAT can reduce memory usage of the network, but may affect the training performance of the network with some games.
Question &amp; answering system based on retrieval

The principle of the question and answering system is nowadays one big branch of natural language processing. When a user enters a query, the system should find the question that is most similar to the entered question stored in the datasets. Then directly return the answer corresponding to the found question. Two models are implemented to better calculate the similarity between entered questions and questions in the corpus: TF-IDF and Word2Vec. After one hundred experiments to test the accuracy of these two models, the result shows that the Word2Vec has 81% accuracy and TF-IDF has only 49% accuracy. It is concluded that Word2Vec has a better accurate performance than TF-IDF. There is also an optimized storage method by using the inverted list for the corpus to improve the models efficiency.
Research on big data privacy protection technology

The extensive application of big data technology makes data burst with unprecedented value and vitality. However, due to the large amount of data, many data sources, and complex data access relationships, the current development of privacy protection technology is seriously lagging behind the development of big data technology, which restricts the application and promotion of big data. At present, it is urgent to sort out the development status of big data privacy protection technology, so as to provide a reference for the research and breakthrough of key issues of big data privacy protection. This paper analyzes k-anonymity and differential privacy protection, and points out that a protection method that can reduce the cost of high-dimensional data processing and ignore the background knowledge requirements of attackers is urgently needed for privacy protection in big data scenarios. At the same time, this paper also puts forward suggestions for the application and future development of these technologies.
A never ending warfare: Fighting cheaters in online video game

As the gaming industry gradually expands, more and more people begin taking notice of the industry. Though with blossom, problems emerge. Many people dont want to take the time to master a video game, so some will then resort to cheating. This is terrible news for developers as it ruins their reputation and player base. This paper will lay the groundwork for anyone interested in the industry on what has already been done to fight cheaters, as little research was conducted. This paper, will introduce the modern online connection architecture, categorize the most common cheats, and most importantly, introduce modern-day anti-cheat methods. The anti-cheat methods will be analyzed on their effectiveness on different online connection architecture and the type of cheat it works against. Lastly, the paper will also introduce the idea of kernel-level and its impact on anti-cheat.
Analysis on issues and challenges of IoT and the solution based on blockchain technology

With the development of science and technology, the Internet of Things (IoT) has become an area of great influence and potential. However, the deficient data security and privacy problems of the current IoT systems seriously limit its application. This is due to the fact that the information exchange and data authentication in IoT must be done through the central server. In order to solve the fraud possibility of device, false authentication, unreliable data sharing, eliminating the concept of a central server, the block chain (BC) technology was adopted as part of the Internet of things used to improve the above problems. This paper elaborates the component interaction in the Internet of things which bring security and privacy problems, and introduces the existing block chain technology, the application of Internet of things. Various challenges of IoT and IoT with BC as well as future research directions are summarized.
Performance analysis and comparison of representative chatbots based on deep learning

In today's society, chat robots have entered people's vision. They can mainly carry out corresponding human-computer interaction, and are also research hotspots in the field of science and technology. Chat robots can build models to understand the input content, and then output relatively natural answers. In recent years, their birth has also produced a certain range of applications. However, the most representative chat robots are mainly based on retrieval and generation. Their patterns are different, so their behavior is also different. At the same time, in order to enable users to choose better chat robots, this paper compares the performance of retrieval chat robots based on SMN model, retrieval chat robots based on DAM model and seq2seq generation chat robots respectively. Their performance was analyzed and evaluated, mainly from the appropriateness and diversity of their responses and the difficulty of the training model.
Localization of robots under two-dimensional indoor conditions

In the 21st century, driven by the vigorous development of science and technology, intelligent services are developing under a unprecedented speed. With the progress of new science and new technology, robots have made a qualitative leap in artificial intelligence, sensing hardware equipment and other aspects. The emergence of mobile robot has replaced many ineffective mechanical labor and liberated a large number of labor. However, in reality, the environment of the moving robot is relatively complex, with many dynamic obstacles and environmental terrain bending, so a reasonable and efficient path planning can greatly save the traveling time of the mobile robot. At the same time, if the mobile robot accurately find the position of its own body and the position of obstacles, it can reach the target safely. Therefore, it is a useful research topic to study the positioning and mapping methods and the path planning method in the navigation technology of moving robot. This article mainly discuss some popular methods of localization under two-dimensional and demonstrate the weakness.
Overview of definition, evaluation, and algorithms of serendipity in recommender systems

Over time, recommendation systems are playing an important role in an increasingly wide range of areas, such as paper retrieval sites that can recommend papers or books to users, and shopping sites that can recommend products to users. With the development of recommendation systems, there are many different metrics to measure a good recommendation system, including serendipity. This paper summarizes the definition of serendipity, a review of the metrics for measuring serendipity, and several major serendipity-oriented algorithms and presents conjectures for future research on serendipity. Through the research of some papers, for how to delimit and evaluate recommender systems, experts have mostly focused on the unexpected, and most of them use and optimize collaborative filtering algorithms to achieve and improve serendipity.
The development of secure multi-party computation

In an increasingly technological world, some significant issues that have developed in the past few years are privacy and data protection. This is generalized in the broad field of cryptography. People often use concepts from cryptography to find ways to access a distributed set of data without exposing or altering it, protecting them from outside parties obtaining access to their data for malicious purposes. This is often achieved through a specific method called Secure Multi-Party Computation. Throughout the past few decades, improvements in Secure MPC have skyrocketed, becoming an essential part of society. Whether in finance, statistics, or just typical data collection and protection, Secure MPC plays an important role. This paper will discuss the history behind Secure MPC as well as some of its modern-day applications. Furthermore, this paper will analyze the advantages and disadvantages of Secure MPC, evaluating its potential for growth in the coming century.
Sentiment analysis for social media using SVM classifier of machine learning

The communitys perspectives and comments are a valuable resource for businesses and other organizations. In the past, businesses used inefficient procedures. Now that social media is the new trend, it enables an unprecedented level of analysis and evaluation. This enables unprecedented analysis and evaluation of various factors. This enables unprecedented analysis and evaluation of a wide range of topics and components in different contexts and settings. Throughout business history, these strategies have been expected. This field of study is called sentiment analysis. SVM was used to analyze sentiment for this research project. One of these duties required an SVM(SVM). Support vector machines, or SVM, is a popular supervised machine learning algorithm for determining text polarity. SVM abbreviates support vector machines. Precision, recall, and F-measure are used to evaluate SVM using two datasets of pre-classified tweets. Tables and graphs are used to communicate research findings. This research classifies tweets about US-Airlines and performs sentiment analysis with an accuracy of 91.8 percent, precision of 91.3 percent, and recall of 82.3 percent, as well as the F1 of 86.9 percent.
Basic things about reinforcement learning

Artificial Intelligence has been a very popular topic at present, machine learning is also one of the main algorithms in AI, which consisted of Supervised learning, Unsupervised learning and Reinforcement learning, and Supervised learning and Unsupervised learning have been relatively mature. Reinforcement learning technology has a long history, it wasn't until the late '80s and early' 90s that reinforcement learning became widely used in artificial intelligence, machine learning. Generally, Reinforcement learning is a process of trial and error, agent will choose to make an action according to the feedback from the environment, this step will repeat a lot of times until it find the best policy, mapping it to reality, it can help human to fulfill some missions which are nearly impossible before. However, there is still some potential problems in Reinforcement learning. This essay compares some basic algorithms related to RL to help reader to have a basic understanding of RL and propose some exsiting defects about it.
Probalistic model of spam filter system

Numerous tasks have been carried out using probabilistic reasoning, including picture recognition, computer diagnosis, stock price prediction, movie recommendation, and cyber intrusion detection. But until recently, the breadth of probabilistic programming was constrained (partly because of the low processing power), and the majority of inference methods had to be created manually for every job. Nevertheless, in 2015, 3D representations of human faces were created from 2D photographs of such faces using a 50-line probabilistic computer vision software. The inference approach of the software, which was developed in Julia using the Picture package, was based on inverted graphics. "What used to require thousands of lines of code" might now be accomplished in just 50. Probabilistic computing is a method to create systems that help us make decisions in the face of uncertainty. In this paper we propose a spam-filtering system. The novelty of our spam-filtering system is that we utilize probabilistic programming to improve spam-filtering reasoning systems. We implement several factors from email to establish a model to get the output. Our preliminary results show that we successfully accomplish a spam-filtering system. More problem of classification of spam filter can be described in a probabilistic modelling language in our future work.
The characters and advantages of different algorithms in multi-robot path planning

Multi-agent path finding (MAPF) issue has recently gained popularity, with the goal of planning for several agents who can all follow instructions at the same time without colliding. It is a common difficulty when attempting to identify pathways to several agents while ensuring that each agent reaches its destination without colliding and confusion. Real-world MAPF applications such as warehouse management, food, and beverage distribution, and aviation management are paid a number of attention from people in recent years. This article discusses centralized and decentralized techniques in MAPF challenges. Several traditional algorithms will be quoted and introduced through the help of some past papers and experiments. Finally, it was determined that decentralized strategies generally provide more powerful performance, whereas centralized strategies are generally capable of computing optimal solution sequences with minimal coupling and solving complex problems, ranging from relatively few robot problems with high coupling to many robot problems with low coupling.
Algorithm structure for real-time general game playing agents

General Game Playing is an examination of AI agents that are designed without any explicit knowledge of a particular game. Instead, they are required to play a variety of games that they have never seen before by merely reading the description of the rules at runtime, without any intervention from a human being. Previous successful agents have concentrated their efforts on turn-based games, employing either generic heuristics or the Monte Carlo/UCT simulation technique. Real-time games have dominated the market in recent years, and as a result, agents that have more reflexibility have emerged as an important area of research. In this paper, the author evaluateswhether or not it is possible to implement the already-existing algorithms into a real-time playing GGP agent, and we present a potential algorithm structure that, on the basis of this evaluation, could be able to solve the challenge.
Research and analysis of virtual reality in different fields

Virtual reality technology is an emerging technology has been used in many fields. Because of its unique multi-perception, interactivity and autonomy, can enable users to obtain an immersive experience, enrich the sense of experience, VR is promoting the development and creation of multiple industrial fields with high speed. This article partly summarizes the application of VR technology in medical, educational and cultural fields. This paper analyzes the application characteristics and status quo of VR in the medical field, and discusses the progress and advantages of VR in education. It also summarizes the related technology of VR in culture. With the successful research and development of 5G technology, VR technology will also be improved, so VR technology will also be more widely used in various fields and promote the development of various fields.
Studies advanced in chatbots based on deep learning

Chatbots have always been a hot research topic in the field of human-computer interaction research, which aims to build a conversational intelligent response model to simulate human dialogue. Thanks to the rapid development of natural language processing technology and the continuous accumulation of dialogue data, the research of chat robots have made remarkable progress, which has gradually been widely used in various fields such as e-commerce and smart home. According to different technical frameworks, existing chatbots are mainly divided into two types: retrieval chatbots and generative chatbots. As the primary means of implementing chatbots in the industry, retrieval chatbots have smooth responses and low computational resource consumption. In contrast, generative chatbots do not require a predefined knowledge base and can dynamically generate responses based on the dialogue content. In this paper, focusing on the above two types of frameworks, we introduce the latest research progress in the field of deep learning-based chatbots in detail, including the representative algorithms and corresponding pipelines. Second, we compare the performance of representative algorithms on different datasets. We also summarize the problems chatbot technology research faces and give an outlook on its future development trends.
A study of the lightweight algorithm for recognizing masks incorporating attention mechanism and G-GhostNet

As COVID-19 and humanity's protracted battle makes wearing masks a norm, intelligent recognition of mask-wearing and wearing criteria is investigated to lessen the labor of epidemic control and detection personnel. To achieve the purpose of not having to manually identify mask use in a busy, open setting, to be able to provide a real-time warning of the whole mask non-wearing phenomenon in mobile devices, and to address the issue of sluggish efficiency and poor precision of traditional target detection and tracking means in complex scenes, this work presents a lightweight mask identification method that combines G-GhostNet with an attention mechanism. Firstly, YoLov5's BackBone network is replaced with a more lightweight G-GhostNet with fewer convolution kernels to reduce the parameters and computation more significantly; secondly, an attention mechanism is introduced to G-GhostNets feature mixing process to increase discovered features weight values.Then, the dichotomous unmasked and masked datasets are prepared to enhance the model accuracy and speed by the loss function. The experiments compare the effects of Squeeze-and-Excitation Networks (SE), Convolutional Block Attention Module(CBAM), Efficient Channel Attention (ECA), and Coordinate Attention (CA), the four attention processes on the enhanced model with the model without the added attention mechanisms. The results show that adding the attention mechanisms all make the model recognize masks better, among which adding CA has the best recognition effect with a 2% improvement in accuracy but 1.3 times increase in Latency time; in contrast, adding SE, ECA balanced speed and accuracy.
Self-correction of automatic speech recognition

The rapid development of modern technology and society has raised the need for a large number of real-time translations, such as Automatic Speech Recognition (ASR) technology. One of the important factors determining the fluency of cooperation is that ASR translates the input speech into readable text so that both parties can understand each other's meaning. There is also a huge demand for and reliance on automatic speech recognition technology for modern applications. This article focuses on the word matching algorithm for backward search (WMA-BS), which requires two separate steps, one for the word matching algorithm and the other for the backward search step, for the first step, which can also be done by applying the word-level N-Gram language model. n-Gram model is a language model in which when n words appear in a text, we can use these n words to predict the structure of the text. we can use these n words to predict the structure of the text. In order to make the word-level N-gram language model suitable for the method, it is more useful for us to integrate it into shallow fusion. One of the most important parts to be addressed in the word matching algorithm is the boundary, or distance, between two words in English.
Research advanced in object detection based on deep learning

In the field of computer vision, identifying specific objects in images and predicting their location and class have long been hot research topics.Algorithms for early object detection rely on traditional handcrafted features, and the speed and precision of their detection cannot meet the needs of real-world applications. Rapid development in convolutional neural networks has accelerated the development of object identification systems based on deep learning.Existing deep learning-based object identification algorithms mostly use two-stage detection and single-stage detection, according to various detection frameworks. In this paper, around the above two types of frameworks, the latest research progress in the field of object detection is systematically introduced. Specifically, we first introduce representative object detection algorithms, including their design ideas, basic processes, and advantages and disadvantages. Second, using widely-used datasets, we objectively compare the effectiveness of several detection techniques. Finally, we summarize the unsolved problems in the detection of objects and talk about the route this topic will take going forward.
Detecting sarcastic expressions with deep neural networks

Following the ever increasing trend in social media such as Twitter, Facebook, and Instagram, automatic analysis of peoples conversations and languages have become a problem of great significance for businesses and governments in attempt to understand and analyze peoples habits, thoughts, and patterns towards different subjects of interests. Within the field of natural language processing, sarcasm detection has always been a difficult challenge for sentiment analysis. Recent years, there has been great interests shown by researchers towards sarcasm detection. Neural networks achieve huge success and advancements surrounding this topic, but reviews for this task are very limited and theres a lack of comprehensive review of the development of sarcasm detection so far. Thus, this paper aims to summarize and present the various methods directed towards sarcasm detection, the progress it has made, and examination of potential problems and availability for further improvements.