The final chapter of this text provides extended questions designed to test students understanding and knowledge of evidence law as a whole. Each question combines multiple elements of the Uniform Evidence Act (1995) to ensure students understand how sections work together.

This chapter focuses on the exclusionary powers of the trial judge. Two forms of exclusion are examined: discretionary, where the trial judge has a choice whether to exclude the evidence; and mandatory, where the trial judge is required to do so. The discretions under the Act play a more significant role than the discretions at common law in determining the admissibility of evidence. This may be because the Act adopts the logical relevance test. However, despite evidence being admissible and relevant, the trial judge has the discretion to exclude the evidence. Further, many exclusionary rules of evidence at common law (discussed in the earlier chapters) are relaxed and the Act adopts a more flexible approach to the admissibility of evidence.

This chapter first considers the discretionary and mandatory exclusions regulated by pt 3.11 (ss 135–9) of the Act. The concepts of ‘probative value’ and ‘unfair prejudice’ govern the exercise of the discretions. The chapter then considers how the provisions on corroboration and unreliability under pts 4.4 and 4.5 (ss 164–165B) have altered the common law and how their operation may affect the weight of evidence.

This chapter discusses the provisions of the Act that address character evidence. The term ‘character evidence’ is not defined in the legislation, so some recourse to the common law is required. However, pt 3.8 of the Act provides a simple mechanism allowing evidence of character to be adduced in criminal proceedings, as follows. (1) Exclusionary rules that would prevent a defendant from adducing evidence of good character (the hearsay, opinion, tendency and credibility rules) do not apply. (2) If the defendant adduces evidence of good character (whether by giving evidence or through the testimony of another witness) then the prosecution, or another defendant, can respond with evidence of bad character (because the same exclusionary rules also do not apply).

This chapter also deals with the interaction of character and credibility evidence, and concludes with a discussion of evidentiary and procedural rules relating to character evidence about complainants and victims, addressed mainly in legislation outside the Act.

This chapter is about the admissibility of evidence in court as opposed to the adducing of evidence in court. This difference is significant. Even if the potential admission of evidence satisfies procedural requirements, the court may exclude it on the basis that it falls within one or more of the exclusionary rules of evidence, and not within an exception to those rules.

The fundamental rule in evidence law is that evidence that is relevant is admissible, unless it is excluded by one of the rules of exclusion. Where the evidence is irrelevant, it is inadmissible, and there are no rules of inclusion. Therefore, relevance is the first hurdle in considering whether an item of evidence is to be admitted in court.

To be admissible, evidence must be relevant to a fact in issue. In other words, the item of evidence must be able to affect the assessment of the probability that the fact in issue exists. Facts in issue are determined by reference to the substantive law. The material or principal facts, often referred to as the ‘ultimate issues’, ‘essential allegations’ or ‘material allegations’, are what must be proven.

This chapter considers the rules affecting confessions and admissions in civil and criminal proceedings. Parties can make admissions because a previous representation can constitute an admission before there is even any case. As a matter of terminology, in criminal proceedings admissions involve the defendant acknowledging only a limited aspect of the case against them, whereas a confession involves a full acknowledgement of guilt. Despite these technical differences, the term ‘admissions’ is used in the Act to cover both circumstances and therefore in this chapter for consistency.

The first issue this chapter addresses is whether the evidence adduced is in fact an admission. This is followed by an explanation of the statutory rules and cases pertaining to mandatory electronic recording of admissions. The chapter then considers the voluntariness and reliability requirements under ss 84 and 85. The types of statements and conduct that may amount to evidence of an admission in civil and criminal proceedings are explored. Finally, the unfairness discretion under the common law and the role of s 90 are considered.

The focus of this book is the uniform Evidence Act (referred to throughout as ‘the Act’ or ‘the Acts’). The Act has not been introduced in Queensland, South Australia or Western Australia, where each state’s Evidence Act and the common law apply. However, the Act is still an important reference guide for those states due to the connection between the common law and the Act. Despite the differences between jurisdictions that have adopted the Act, there is a significant degree of uniformity. Accordingly, in this book, the provisions that are extracted to indicate the rules in relation to the Act come from the Commonwealth Act. Any important jurisdictional differences are separately identified.

This chapter considers the legislative history of evidence law and some fundamental introductory concepts that are used frequently in evidence law and the trial process. This chapter is an introductory overview; specific topics are dealt with in substance in subsequent chapters.

In this chapter, we focus on multilingualism and language contact, moving away from the strong focus on monolingualism characteristic of many traditional approaches to language history, and discussing various onsets, scenarios and outcomes of language contact. We introduce the concepts of borrowing and imposition as central constructs to understand contact-induced change in language, illustrating and critically examining these ideas in three case studies: the development of loanwords in Canadian French, Germanic substrate effects in the formation of American Englishes and mixed-language business writing in medieval Britain after the Norman Conquest. Building on these cases, we discuss which elements of the language can be transferred and explore possible pathways of social diffusion of borrowings, as well touching upon various traits and examples of code switching and similar multilingual practices in historical texts. Finally, we evaluate the constructs of pidgin and creole languages, discussing to what extent they can be seen as different in structural terms, or whether their distinctiveness arises primarily from the sociohistorical circumstances from which they arose.

This chapter explains credibility evidence under pt 3.7 of the Act and the common law principles governing the admission of credibility evidence. Central to this topic is what constitutes credibility evidence.

In general, credibility evidence is evidence that is directly relevant to the establishment of the credibility of a witness or another person for the ultimate purpose of establishing the facts in issue. As a consequence, credibility evidence is ‘collateral’ with respect to the establishment of the primary facts in issue in a proceeding. From the perspective of relevance, credibility evidence is admissible, even though it is collateral. From the perspective of admissibility, credibility evidence is initially excluded (‘primarily’) because it is collateral, but is then admitted (‘secondarily’) under specific exceptions.

The chapter thus discusses credibility evidence; exclusion of credibility evidence about a witness under the credibility rule; exceptions that permit admission of credibility evidence about a witness; and the admission of credibility evidence about persons other than witnesses.

This chapter deals with a range of matters relating to the facilitation of proof (mostly found in ch 4 of the Act) and ancillary matters (found in ch 5 of the Act). Although these provisions are somewhat technical, many are important in practice, as they allow decisions to be reached without evidence having to be taken on some issues. They also regulate the ways in which certain kinds of information, such as that contained in public documents and registers, may be used. Other aspects of proof, such as the standards of proof applying in civil and criminal proceedings, as well as judicial notice, are dealt with in Chapter 1 of this book. Warnings, although falling within ch 4 of the Act, are discussed together with discretions and limiting directions in Chapter 12 of this book.

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.

9.1 INTRODUCTION [LO1]

Stresses given by relatively simple equations in the strength of materials for structures or machine members are based on the assumed continuity of the elastic medium. However, the presence of discontinuity destroys the assumed regularity of stress distribution in a member and a sudden increase in stresses occurs in the neighborhood of the discontinuity. In developing machines, it is impossible to avoid abrupt changes in cross-sections, holes, notches, shoulders, etc. Abrupt changes in cross-section also occur at the roots of gear teeth and threads of bolts. Some examples are shown in Figure 9.1.

Any such discontinuity acts as a stress raiser. Ideally, discontinuity in materials such as non-metallic inclusions in metals, casting defects, residual stresses from welding may also act as stress raisers. In this chapter, however, we shall consider only the geometric discontinuity that arises from design considerations of structures or machine parts.

Many theoretical methods and experimental techniques have been developed to determine stress concentrations in different structural and mechanical systems. In order to understand the concept, we shall begin with a plate with a centrally located hole. The plate is subjected to uniformly distributed tensile loading at the ends, as shown in Figure 9.2.

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.