This chapter takes a relatively broad approach to defences, covering a range of factors that might serve to exculpate a defendant who might otherwise appear to have committed an offence. The defences examined here are arranged into two, imperfectly realised, categories. The first group have been termed ‘mental state defences’ and the second ‘self-help defences’. The group titled ‘mental state defences’ are so categorised because they depend to a greater or lesser extent on the contention that the accused did not possess the requisite mens rea to commit the offence. In assessing whether an accused may be able to rely on a defence a number of subjective and objective elements have to be applied and analysed. It is important to understand that the considerations informing the development of each of the defences are often very different and sometimes controversial. The groupings are far from perfectly realised and the rationales and doctrines of each of the defences may manifest as many dissimilarities as they do similarities. It is hoped that the arrangement of the material in this chapter will aid understanding by drawing comparisons across different aspects of the criminal law.

The crimes of murder and manslaughter as well as any statutorily created offences involving the death of a person, such as dangerous driving causing death or assaults causing death are homicides in the sense of unlawful killings. Homicides may, however, be lawful insofar as being justified by, for example, self-defence or acting in the defence of another person; or being excused as a result of duress. They may be the consequence of an accident or an accused may not be criminally responsible because they suffer from a mental illness.This chapter will explore and analyse the crimes of murder, manslaughter and various statutory crimes involving particular types of conduct which cause the death of another person, including assaults, driving vehicles and administering drugs or other acts to hasten death. Murder is the most serious form of unlawful homicide and, with culpability rooted largely in the intentional nature of the killing, it attracts severe punishment up to a maximum of imprisonment for life. This penalty may be mandatory and may mean for the term of the offender’s natural life in some jurisdictions or in specific circumstances.

10.1 Support Vector Machines

Support vector machines (SVMs) are supervised machine learning (ML) models used to solve regression and classification problems. However, it is widely used for solving classification problems. The main goal of SVM is to segregate the n-dimensional space into labels or classes by defining a decision boundary or hyperplanes. In this chapter, we shall explore SVM for solving classification problems.

10.1.1 SVM Working Principle

SVM Working Principle | Parteek Bhatia, https://youtu.be/UhzBKrIKPyE

To understand the working principle of the SVM classifier, we will take a standard ML problem where we want a machine to distinguish between a peach and an apple based on their size and color.

Let us suppose the size of the fruit is represented on the X-axis and the color of the fruit is on the Y-axis. The distribution of the dataset of apple and peach is shown in Figure 10.1.

To classify it, we must provide the machine with some sample stock of fruits and label each of the fruits in the stock as an “apple” or “peach”. For example, we have a labeled dataset of some 100 fruits with corresponding labels, i.e., “apple” or “peach”. When this data is fed into a machine, it will analyze these fruits and train itself. Once the training is completed, if some new fruit comes into the stock, the machine will classify whether it is an “apple” or a “peach”.

Most of the traditional ML algorithms would learn by observing the perfect apples and perfect peaches in the stock, i.e., they will train themselves by observing the ideal apples of stock (apples which are very much like apples in terms of their size and color) and the perfect peaches of stock (peaches which are very much like peaches in terms of their size and color). These standard samples are likely to be found in the heart of stock. The heart of the stock is shown in Figure 10.2.

The present chapter discusses the linguistic representation of, and reference to, individuals. Individuals were introduced in Chapters 2 and 3 as particulars – entities individuated by time and space – alongside events. The overarching question guiding this chapter is how to study the domain of reference to individuals in particular languages. The mention of particular languages in this formulation targets language documentation, description, and typology. However, I believe that the methods and tools discussed in this chapter will also be of use to students of child language, psycholinguists, and researchers engaging in corpus-based studies. The discussion begins by examining the types of concepts that populate the nominal domain (Sections 8.1–8.3). It then pivots to surveying the role of reference to individuals in the grammars of languages (Sections 8.4 and 8.5) and crosslinguistic variation in the lexicalization of the domain (Section 8.6) and concludes with a review of tools and methods for the exploration of the nominal domain (Section 8.7).

1.1 Introduction

In today’s data-driven world, information flows through the digital landscape like an untapped river of potential. Within this vast data stream lies the key to unlocking a new era of discovery and innovation. Machine learning (ML), a revolutionary field, acts as the gateway to this wealth of opportunities. With its ability to uncover patterns, make predictive insights, and adapt to evolving information, ML has transformed industries, redefined technology, and opened the door to limitless possibilities. This book is your gateway to the fascinating realm of ML—a journey that empowers you to harness the power of data, enabling you to build intelligent systems, make informed decisions, and explore the boundless possibilities of the digital age.

ML has emerged as the dominant approach for solving problems in the modern world, and its wide-ranging applications have made it an integral part of our lives. Right from search engines to social networking sites, everything is powered by ML algorithms. Your favorite search engine uses ML algorithms to get you the appropriate search results. Smart home assistants like Alexa and Siri use ML to serve us better. The influence of ML in our day-to-day activities is so much that we cannot even realize it. Online shopping sites like Amazon, Flipkart, and Myntra use ML to recommend products. Facebook is using ML to display our feed. Netflix and YouTube are using ML to recommend videos based on our interests.

Data is growing exponentially with the Internet and smartphones, and ML has just made this data more usable and meaningful. Social media, entertainment, travel, mining, medicine, bioinformatics, or any field you could name uses ML in some form.

To understand the role of ML in the modern world, let us first discuss the applications of ML.

16.1 Introduction to Artificial Neural Network

Neural networks and deep learning are the buzzwords in modern-day computer science. And, if you think that these are the latest entrants in this field, you probably have a misconception. Neural networks have been around for quite some time, and they have only started picking up now, putting up a huge positive impact on computer science.

Artificial neural network (ANN) was invented in the 1960s and 1970s. It became a part of common tech talks, and people started thinking that this machine learning (ML) technique would solve all the complex problems that were challenging the researchers during that time. But sooner, the hopes and expectations died off over the next decade.

The decline could not be attributed to some loopholes in neural networks, but the major reason for the decline was the “technology” itself. The technology back then was not up to the right standard to facilitate neural networks as they needed a lot of data for training and huge computation resources for building the model. During that time, both data and computing power were scarce. Hence, the resulting neural network remained only on paper rather than taking centerstage of the machine to solve some real-world problems.

Later on, at the beginning of the 21st century, we saw a lot of improvements in storage techniques resulting in reduced cost per gigabyte of storage. Humanity witnessed a huge rise in big data due to the Internet boom and smartphones.

13.1 Implementation of k-means Clustering and Hierarchical Clustering

In the previous chapter, we discussed various clustering algorithms. We learned that clustering algorithms are broadly classified into partitioning methods, hierarchical methods, and density-based methods. The k-means clustering algorithm follows partitioning method; agglomerative and divisive algorithms follow the hierarchical method, while DBSCAN is based on density-based clustering methods.

In this chapter, we will implement each of these algorithms by considering various case studies by following a step-by-step approach. You are advised to perform all these steps on your own on the mentioned databases stated in this chapter.

The k-means algorithm is considered a partitioning method and an unsupervised machine learning (ML) algorithm used to identify clusters of data items in a dataset. It is one of the most prominent ML algorithms, and its implementation in Python is quite straightforward. This chapter will consider three case studies, i.e., customers shopping in the mall dataset, the U.S. arrests dataset, and a popular Iris dataset. We will understand the significance of k-means clustering techniques to implement it in Python through these case studies. Along with the clustering of data items, we will also discuss the ways to find out the optimal number of clusters. To compare the results of the k-means algorithm, we will also implement hierarchical clustering for these problems.

We will kick-start the implementation of the k-means algorithm in Spyder IDE using the following steps.

• Step 1: Importing the libraries and the dataset—The dataset for the respective case study would be downloaded, and then the required libraries would be imported.

• Step 2: Finding the optimal number of clusters—We will find the optimal number of clusters by the elbow method for the given dataset.

• Step 3: Fitting k-means to the dataset—A k-means model will be prepared by training the model over the acquired dataset.

• Step 4: Visualizing the clusters—The clusters formed by the k-means model would then be visualized in the form of scatter plots.

The overarching question of the book is What is the scientific process of empirical semantic research? Each chapter addresses a more specific question that must be answered in order to answer this overarching question. The first chapter looks into the nature of meaning (Sections 1.1 – 1.3) and the requirements scientific theories of meaning must meet (Section 1.4). In Section 1.2, coordination emerges as the central problem of communication – the “synchronization” of embodied minds for joint action. This is made possible by the conventionalization of coordination devices, or in other words, signs. I discuss the types of signs that occur in language (anticipating a more comprehensive review in Section 2.1) and introduce the two dimensions of meaning present when speakers coordinate on the world around them: reference to the world and cognitive representation of it (Section 1.3).