This conference paper discusses the influence of computer vision in self-driving car implementation. The automotive industry has experienced a significant transformation by integrating artificial intelligence, such as computer vision. The global market for AI in automotive has grown remarkably since many organizations in the electric vehicle industry have engaged in computer vision due to its benefits. Object detection in computer vision contributes to autonomous driving systems, assisting self-driving vehicles in classification of the road objects accurately. This paper has provided an overview of the importance of computer vision to self-driving cars, such as user experience, reduction in cost and vehicle safety. However, some challenges, such as adverse weather conditions, have also been experienced. A case study analysis has been done to have practical implications of the self-driving car implementation guided by computer vision for detection.

As college students increasingly navigate their academic pursuits and social lives through the digital realm, safeguarding personal privacy on the internet becomes a pressing concern. For example, deep forgery technology has further increased personal privacy concerns. This technique can produce fake videos, pictures, and sounds that are highly realistic, making it a tool for the misuse of personal and sound data. Several artificial intelligences (AI) systems require large amounts of data for training and improvement, which can involve collecting and storing sensitive personal information. If this data is not adequately protected, and there is no clear privacy policy, it may lead to the misuse or disclosure of personal information. In response to the rapid development of artificial intelligence and the impact of deep forgery technology on privacy and security, this research first tested the students' understanding of deep forgery technology, then introduced the dangers of this technology, and finally gave them a reasonable and effective solution. To promote active learning, activities are integrated to simulate real-world scenarios that college students might encounter. These include creating strong passwords, identifying phishing attempts, and understanding the implications of oversharing on social media platforms. Additionally, students are encouraged to examine case studies documenting privacy breaches and their impact on individuals and society. This essay aims to present an effective learning approach for college students to enhance their understanding of personal privacy protection online and provide an effective way of solving personal problems.

In recent years, network information security and stability problems have emerged one after another, and people's personal privacy and property security are being threatened. A large part of these problems are caused by two major characteristics of traditional DNS—insecurity and instability. Therefore, this article aims to explore a new internet naming system —the Blockchain Naming System—to address the current issues of internet information security and stability—especially the problems caused by the traditional DNS system. This article adopts scientific research methods such as literature survey, comparative research, experimental exploration, analogy research, and model building. By analyzing the current situation, studying the feasibility of the blockchain naming system, and comparing its advantages and disadvantages with the traditional DNS system, it is not difficult to conclude that this new blockchain naming system has huge potential. It can not only ensure stability and security but also contribute to other fields.

This survey aims to comprehensively review and analyze the progress in enhancing the stability of generative adversarial networks (GANs). It systematically explores the evolution of GAN architectures, loss functions, and regularization techniques, focusing on their impact on training stability. In addition, this article briefly discusses the main challenges encountered during GAN training, such as mode collapse and vanishing/exploding gradients, and synthesizes various strategies and methods developed to alleviate these problems. Finally, the survey highlights the ongoing need for innovative solutions to improve the stability of GAN training and ensure its effective and robust application in various fields.

Digital image information has the advantages of easy storage and communication, especially with the continuous emergence of powerful image processing software, editing and modifying digital images has become extremely convenient. Subsequently, issues such as low security and easy tampering of digital images have emerged, and the integrity and authenticity of images have been questioned. Some important applications, such as news images, court evidence, medical diagnoses, etc., are not allowed to have their content modified. Passive authentication methods are often only suitable for specific images or situations, don’t have the ability to locate the tamper areas. Active methods based on fragile watermarks often embed external information, making it inconvenient to perform blind authentication on the receiving end and resist malicious attacks that aim at bypassing tamper detection. In this paper, we propose to combine the advantages of passive authentication and active authentication. Firstly, an image is first divided into non-overlayed blocks, then generate check code for each pair of strongly coupled pixels within the same block. Fragile watermark technology is exploited to embed the check codes randomly based on a private key in the pixels of the image itself to achieve blind authentication for the receiver. Finally, we conduct the experiment in which a large number of images have been simulated for tampering and detected for authentication. The results show that compared with other similar methods, this paper not only has high detection accuracy, but also has high accuracy in locating the tampering location. In addition, the method proposed in this paper has other advantages in terms of computational cost and security.

Prostate Diseases pose significant health risks, and the author has developed an integrated machine learning model using a medical indicator dataset of prostate patients. The article introduces seven different machine learning algorithms for classification tasks. The approach involved detailed exploratory data analysis, descriptive statistics, feature engineering, and data visualization. Additionally, data preprocessing was performed by addressing missing values and eliminating non-numeric characters. During the model training process, cross-validation techniques are employed to determine the optimal model parameters, ensuring the accuracy of the training. Furthermore, the training performance of the seven models is assessed through histograms and ROC curves. Based on their performance, three models are selected for ensemble modeling, aiming to further enhance training accuracy and improve precision. Conclusively, the findings indicate that the likelihood of prostate diseases correlates significantly with the medical indicator generated through feature engineering, specifically PSA (free)/PSA (Total), aligning with clinical guidelines for diagnosing prostate diseases. Furthermore, individual baseline data indicators such as body weight have a crucial impact on the likelihood of prostate disease, with obesity serving as a significant risk factor. Among the individual models, the k-Nearest Neighbors (KNN) model achieved the highest accuracy, while the ensemble model further improved accuracy. In summary, the work effectively alerts individuals to the potential occurrence of prostate cancer and hyperplasia by evaluating medical indicators. Ultimately, this initiative aims to raise awareness of maintaining good health and reducing the risk of prostate diseases.

The purpose of this study is to explore the use of speech recognition technology in addressing communication and language barriers between individuals from different geographical locations. Through a case study, this paper collects data from a variety of sources to demonstrate the effectiveness of speech recognition technology in enhancing cross-cultural communication. The findings show that speech recognition technology can greatly improve communication, reduce misunderstandings, and ultimately promote understanding and cooperation between individuals from different regions. This technology can help people overcome language barriers and achieve more fluent communication. In addition, speech recognition technology can improve people's productivity as it can quickly convert spoken language into written text.Speech recognition technology also has a wide range of applications in cross-cultural communication. It can be used in various fields such as business, tourism, education and healthcare. By using speech recognition technology, differences between cultures can be better understood and respected, leading to more harmonious social relationships.

Artificial Intelligence (AI), as a hot topic in today's science and technology, has penetrated into every aspect of our lives. The relationship between AI and mathematics and algorithms has become increasingly close. Mathematics, as a basic science, provides the theoretical basis for AI algorithms and models and is an essential part of the development of AI. The algorithm is an element that acts as a bridge between mathematics and AI and is a series of steps taken to solve a particular problem or reach a clear result [1]. The purpose of this paper is to explore the application and impact of mathematics and algorithms in the field of AI, and to explore how AI can bring about far-reaching changes in our society and human development.

The current study systematically examines the application of the Bayesian approach or Bayesian estimation methods in large-scale datasets, emphasizing their adaptability as well as predictive prowess across various domains. The study navigates the challenges inherent in the computational efficacy and scalability of these techniques to offer insights into their application in the fields of text analysis, image processing, recommendation systems, and social network analysis. The study uses experimental designs that highlight how well Bayesian methods perform in comparison to more conventional methods, emphasizing how much better they are able to handle uncertainty and incorporate prior knowledge. The future directions and possible enhancements of Bayesian techniques are also discussed, especially with regard to overcoming computational limitations by integrating machine learning and developing sophisticated algorithms. As a crucial tool for modern data analysis and predictive modelling, the study's conclusion upholds the critical role that Bayesian estimation plays in the world of big data.

Nowadays, robots can be considered as a symbol of advancement of internet and the multi-link robots represent interesting reference systems among various kinds of robots. 4-links robots can be obviously thought as a symbol of muti-robots and bionic robots owing to it takes inspiration of human’s body parts, foot, leg, thigh, and torso, consisting of four links [1]. The technology of 4-links robot is still in its infancy. At the same time, the invention of hybrid robots gets more recognition and widely applied. Thus, it is necessary to analyze and optimize 4-link robots, to contribute to the progress in bionic robots and provide convenience for human life systematically and further. This report studies stimulation method of muti-links robots, which combines spring-mass robots and 4- links planar robots as a kind of hybrid robots and aims to conclude the relationships between springs and length per step, mass distribution and increase energy efficiency.