### 计算机代写|机器学习代写machine learning代考|COMP30027

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|机器学习代写machine learning代考|Sentience and Cognition

Maturana and Varela treat sentience and cognition as inseparable elements of how autopoietic systems operate. Such systems are structurally coupled with their environment and the ability to maintain recursive interactions with their environment, defined as medium, is key to their survival. The system function remains constant, while the structure adapts to their environment to maintain self-reproduction, organization, and reorganization. Through continuous recurring interactions, autopoietic systems gain knowledge. This is a key element to Maturana and Varela’s autopoiesis theory-from the most basic organism to the most complex system, the process of cognition is a key property of living systems.

Perception and cognition occur through the operation of the nervous system, which is realized through the autopoiesis of the organism. As we have seen, autopoietic systems operate in a medium to which they are structurally coupled. Their survival is dependent on certain recurrent interactions continuing. For Maturana, this itself means that the organism has knowledge, even if only implicitly. The notion of cognition is extended to cover all the effective interactions that an organism has. A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself. and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are cognitive systems and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system (Maturana and Varela 1980, p. 13).

As defined above, sentience and cognition are key characteristics of intelligent living systems. Through the rapid advancements in technology, machines are developing biomimetic capabilities that increasingly simulate behaviors observed in humans and other natural organisms. These capabilities will converge as a form of collective intelligence (Fig. 2) combining both natural and artificial characteristics propagating a new stage in the evolution of the relationship and communication of humans, machines and nature. As a result of these relationships, new communication capabilities and hybrid languages will form to support diverse combinations of human-machine-nature sentience and cognition.

## 计算机代写|机器学习代写machine learning代考|Structural Coupling

As different components and systems become increasingly interconnected, new possibilities for training machine learning algorithms become available, giving rise to convergence driven by artificial intelligence. With advancements in technological innovation and increasingly technological convergence, coupled with the adoption of data-driven methodologies, systems are becoming increasingly smarter and autonomous. In the process of achieving convergence in systems, the underlying dynamics are the inherent forces in nature and evolution itself to develop parallel traits in systems and system behaviors. The diverse functions of systems and subsystems within smart cities, in principle, should move towards converging states given the same level of technological advancement and enablers, infrastructure/platforms and operational mechanisms. For example, if all smart cities’ functions are developed on top of big data analytics, the behavior response mechanisms should, over time, develop convergence characteristics, including realtime data response, data filtering and processing. Applied to the difference between human and machine information processing, the convergence characteristics of autonomous (AI) data analytics will eventually lessen the disparities between human and machine data processing. Within this integrated domain, human and machine intelligence will be co-dependent and co-creative, allowing the generation of convergent responses across all smart city functions. Equally, with other AIdriven functions, human and machine intelligence will converge with the formation of a new hybrid intelligence that we have defined as collective intelligence. In their book on Smart Cities and AI, Kirwan and Fu (2020) expand the definition of collective intelligence to include a human-machine-nature convergence, ultimately integrating human and machine intelligence as part of a broader unified operating system that is the extension of the natural environment. This stage of urban evolution will enable autopoiesis to occur as a comprehensive, unified ecosystem, where smart city functions are harmoniously aligned to their physical environment while further expanded to interface with broader interplanetary systems.

## 计算机代写|机器学习代写machine learning代考|Ambient Intelligence

In computing, ambient intelligence refers to a virtual environment that is sensitive and responsive to the presence of people. In smart cities, this refers to the development of ubiquitous sensor networks, or of a mesh of interconnected sensors, devices and technology that embed input, processing, or response ubiquitously through the environment Dainow (2017). Such interconnected environments will allow people to engage with their cities through seamless experiences rather than interacting with a disconnected set of individual components and discrete processes. These types of intelligent, interactive and adaptable environments are made possible through the convergence of domains and technologies, including IoT, Cloud Computing, machine learning, deep learning and artificial intelligence. What type of characteristics would ambient intelligence systems have? First, such systems will be ubiquitous and embedded, allowing users to engage, interact, and control their environments from any place and any device with a network connection. Second, these systems will be intelligent and clearly aware of the specific context, understanding exactly what is needed at any point in time, seeking ways to support users with information (warnings, updates, calculations, statistics, etc.), through ambient (light, temperature, humidity, etc.) and personalized recommendations. Such systems will be highly customizable to the individual user’s interests, needs, and goals. Third, these systems must be adaptive, reactive and responsive. The ability of such systems to operate in real-time is a critical condition for their successful implementation and ability to add value to their users. Finally, ambient systems must have anticipatory capabilities to allow them to predict a user’s future needs, emotional and physical states. Intelligent urban systems will require sophisticated, self-regulating, quasi autopoietic machine learning algorithms to power the next level of $\mathrm{AI}$ that will be required. Kirwan and Fu $(2020)$ introduce “ambient connectivity” to describe the ability of AI-driven systems to “sense” their environment in order to continuously optimize network bandwidth and frequencies for optimal energy use and efficiency.

## 计算机代写|机器学习代写machine learning代考|Sentience and Cognition

Maturana 和 Varela 将感知和认知视为自创生系统如何运作的不可分割的元素。此类系统在结构上与其环境耦合，并且与环境（定义为介质）保持递归交互的能力是其生存的关键。系统功能保持不变，而结构适应其环境以保持自我复制、组织和重组。通过不断重复的相互作用，自创生系统获得知识。这是 Maturana 和 Varela 的自创生理论的一个关键要素——从最基本的有机体到最复杂的系统，认知过程是生命系统的关键属性。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。