This course will give an overview of all the topics we shall be looking at in this course. We shall begin by describing the oil value chain – the exploration and development, how oil is produced, shipped, and marketed. Moving further, we will learn about the importance of oil in the industry, both as a fuel and as a raw material in various forms in the global economy. Then, we will go through a brief history of oil – how it all began, and the different'kinds' of oil discoverers. We will be introduced to the major players in the oil market – the top producers and the major consumers. We will then see how oil is formed, how it sits deep within the earth and how we discover and refine it. We will learn about the different types of oils, and the methods employed to extract them. This will be followed by a brief overview of the different means of transporting oil, and the risks and benefits associated with the different methods of oil transport. Lastly, we shall look into the different oil benchmarks that prevail globally.

Despite the high accuracy offered by state-of-the-art deep natural-language models (e.g. LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a black-box to the end-user. Hence, explainability is rapidly becoming a fundamental requirement of future-generation data-driven systems based on deep-learning approaches. Several attempts to fulfill the existing gap between accuracy and interpretability have been done. However, robust and specialized xAI (Explainable Artificial Intelligence) solutions tailored to deep natural-language models are still missing. We propose a new framework, named T-EBAnO, which provides innovative prediction-local and class-based model-global explanation strategies tailored to black-box deep natural-language models. Given a deep NLP model and the textual input data, T-EBAnO provides an objective, human-readable, domain-specific assessment of the reasons behind the automatic decision-making process. Specifically, the framework extracts sets of interpretable features mining the inner knowledge of the model. Then, it quantifies the influence of each feature during the prediction process by exploiting the novel normalized Perturbation Influence Relation index at the local level and the novel Global Absolute Influence and Global Relative Influence indexes at the global level. The effectiveness and the quality of the local and global explanations obtained with T-EBAnO are proved on (i) a sentiment analysis task performed by a fine-tuned BERT model, and (ii) a toxic comment classification task performed by an LSTM model.

The philosopher Mark Coeckelbergh has long been dealing with the development of intelligent machines and their effects on concepts of humanity, societal transformation and the ideology of the trans- and posthuman. His recent book AI Ethics (MIT Press, 2020) provides a survey of the most pressing moral questions opened up by these developments. Should we simply enjoy the new liberties generated by AI as future offers without any alternative? Where does selflessness end with respect to the machinic "other," and where should deliberations about a "trustworthy" AI start? Questions like these are tackled by Coeckelbergh in the following interview.

The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.

The company will also hold the Avnet 2021 Artificial Intelligence Cloud Conference on 29 June, 2021. Joined by developers, engineers, and decision makers in the AI field, the summit will feature cutting-edge technology trends in artificial intelligence and machine learning, and in-depth discussions on the development, future prospects and blueprints for AI to encourage and accelerate innovation. "MarketsandMarkets forecasts the global artificial intelligence market size to grow to over USD$300 billion by 2026, and the market in Asia Pacific is anticipated to grow at the highest CAGR during the forecast period," says KS Lim, senior director of supplier management at Avnet Asia. "As the world's leading technology distributor and solution provider, Avnet has a comprehensive ecosystem that provides customers with end-to-end artificial intelligence and machine learning solutions, reducing the cost and complexity of product development to enable application scenarios," he says. "We will continue to work hand in hand with our suppliers and partners to further contribute to the development and maturity of the entire AI ecosystem."

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include: heavy reliance on massive training data, limited generalizability and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

Another example is making inferences about facial attributes and Deep learning, particularly convolutional neural networks have in identify by representing facial landmarks as a graph [33]. The literature the recent past revolutionized many machine learning tasks. Examples on application of GNNs on images can be broadly classified in to include image classification, video processing, speech recognition, three groups (a) pixel-based graphs, (b) superpixel-based graphs and and natural language processing. These applications are usually (c) object-based graphs - sample illustrations of these three methods characterized by data drawn from the Euclidean space. Recently, are shown in Figure 1. In addition to providing a comprehensive review many studies on extending deep learning approaches for graph data of graph techniques for images' superpixels, this paper paper have emerged [1-22]. The motivation for these studies stems from also makes notable contribution by introducing new taxonomy based the emergence of applications in which data is drawn from noneuclidean on how graph represents an image as summarized in Table 1.

We present a new approach to interpreting IRR that is empirical and contextualized. It is based upon benchmarking IRR against baseline measures in a replication, one of which is a novel cross-replication reliability (xRR) measure based on Cohen's kappa. We call this approach the xRR framework. We opensource a replication dataset of 4 million human judgements of facial expressions and analyze it with the proposed framework. We argue this framework can be used to measure the quality of crowdsourced datasets.

Army researchers have developed a pioneering framework that provides a baseline for the development of collaborative multi-agent systems. The framework is detailed in the survey paper "Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training," which is featured in the SPIE Digital Library. Researchers said the work will support research in reinforcement learning approaches for developing collaborative multi-agent systems such as teams of robots that could work side-by-side with future soldiers. "We propose that the underlying information sharing mechanism plays a critical role in centralized learning for multi-agent systems, but there is limited study of this phenomena within the research community," said Army researcher and computer scientist Dr. Piyush K. Sharma of the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory. "We conducted this survey of the state-of-the-art in reinforcement learning algorithms and their information sharing paradigms as a basis for asking fundamental questions on centralized learning for multi-agent systems that would improve their ability to work together."

Identifying academic plagiarism is a pressing problem, among others, for research institutions, publishers, and funding organizations. Detection approaches proposed so far analyze lexical, syntactical, and semantic text similarity. These approaches find copied, moderately reworded, and literally translated text. However, reliably detecting disguised plagiarism, such as strong paraphrases, sense-for-sense translations, and the reuse of non-textual content and ideas, is an open research problem. The thesis addresses this problem by proposing plagiarism detection approaches that implement a different concept: analyzing non-textual content in academic documents, specifically citations, images, and mathematical content. To validate the effectiveness of the proposed detection approaches, the thesis presents five evaluations that use real cases of academic plagiarism and exploratory searches for unknown cases. The evaluation results show that non-textual content elements contain a high degree of semantic information, are language-independent, and largely immutable to the alterations that authors typically perform to conceal plagiarism. Analyzing non-textual content complements text-based detection approaches and increases the detection effectiveness, particularly for disguised forms of academic plagiarism. To demonstrate the benefit of combining non-textual and text-based detection methods, the thesis describes the first plagiarism detection system that integrates the analysis of citation-based, image-based, math-based, and text-based document similarity. The system's user interface employs visualizations that significantly reduce the effort and time users must invest in examining content similarity.