427 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

    Get PDF
    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) 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 – 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 Católica 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

    Get PDF
    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

    AI: Limits and Prospects of Artificial Intelligence

    Get PDF
    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Speech Emotion Recognition Using Attention Model

    Get PDF
    Speech emotion recognition is an important research topic that can help to maintain and improve public health and contribute towards the ongoing progress of healthcare technology. There have been several advancements in the field of speech emotion recognition systems including the use of deep learning models and new acoustic and temporal features. This paper proposes a self-attention-based deep learning model that was created by combining a two-dimensional Convolutional Neural Network (CNN) and a long short-term memory (LSTM) network. This research builds on the existing literature to identify the best-performing features for this task with extensive experiments on different combinations of spectral and rhythmic information. Mel Frequency Cepstral Coefficients (MFCCs) emerged as the best performing features for this task. The experiments were performed on a customised dataset that was developed as a combination of RAVDESS, SAVEE, and TESS datasets. Eight states of emotions (happy, sad, angry, surprise, disgust, calm, fearful, and neutral) were detected. The proposed attention-based deep learning model achieved an average test accuracy rate of 90%, which is a substantial improvement over established models. Hence, this emotion detection model has the potential to improve automated mental health monitoring

    A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

    Get PDF
    Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis (SA) entails extracting and identifying user opinions from the text, and various lexicon-and machine learning-based methods have been developed over the years to accomplish this. However, deep learning (DL)-based approaches have recently become dominant due to their superior performance. This study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of DL-based approaches. In addition, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures and the resources available in the public domain to aid SA tasks. Furthermore, the survey discusses domain-specific practical applications of SA tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation

    A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

    Full text link
    Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.Comment: Accepted for EUROGRAPHICS 202

    Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

    Get PDF
    Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

    An Optimized and Privacy-Preserving System Architecture for Effective Voice Authentication over Wireless Network

    Get PDF
    The speaker authentication systems assist in determining the identity of speaker in audio through distinctive voice characteristics. Accurate speaker authentication over wireless network is becoming more challenging due to phishing assaults over the network. There have been constructed multiple kinds of speech authentication models to employ in multiple applications where voice authentication is a primary focus for user identity verification. However, explored voice authentication models have some limitations related to accuracy and phishing assaults in real-time over wireless network. In research, optimized and privacy-preserving system architecture for effective speaker authentication over a wireless network has been proposed to accurately identify the speaker voice in real-time and prevent phishing assaults over network in more accurate manner. The proposed system achieved very good performance metrics measured accuracy, precision, and recall and the F1 score of the proposed model were98.91%, 96.43%, 95.37%, and 97.99%, respectively. The measured training losses on the epoch 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 0.6, 0.3, 0.3, 0.3, and 0.2, respectively. Also, the measured testing losses on the epoch of 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.2, 2, 1.5, 1.4, 1.1, 0.8, 0.8, 0.7, 0.4, 0.1 and 0.1, respectively. Voice authentication over wireless networks is serious issue due to various phishing attacks and inaccuracy in voice identification. Therefore, this requires huge attention for further research in this field to develop less computationally complex speech authentication systems.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved
    • …
    corecore