1,981 research outputs found
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy
Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches
Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people.
Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy.
The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech.
In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection
Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks
Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workersâ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subjectâs physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (âAIâ) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics â and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatĂłlica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Face Emotion Recognition Based on Machine Learning: A Review
Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions
Renewable Energy and Energy Storage Systems
The use of fossil fuels has contributed to climate change and global warming, which has led to a growing need for renewable and ecologically friendly alternatives to these. It is accepted that renewable energy sources are the ideal option to substitute fossil fuels in the near future. Significant progress has been made to produce renewable energy sources with acceptable prices at a commercial scale, such as solar, wind, and biomass energies. This success has been due to technological advances that can use renewable energy sources effectively at lower prices. More work is needed to maximize the capacity of renewable energy sources with a focus on their dispatchability, where the function of storage is considered crucial. Furthermore, hybrid renewable energy systems are needed with good energy management to balance the various renewable energy sourcesâ production/consumption/storage. This work covers the progress done in the main renewable energy sources at a commercial scale, including solar, wind, biomass, and hybrid renewable energy sources. Moreover, energy management between the various renewable energy sources and storage systems is discussed. Finally, this work discusses the recent progress in green hydrogen production and fuel cells that could pave the way for commercial usage of renewable energy in a wide range of applications
La traduzione specializzata allâopera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The âLanguage Toolkit â Le lingue straniere al servizio dellâinternazionalizzazione dellâimpresaâ project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
- âŠ