539 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
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
Identification of Key Performance Variables in Prone and Supine Underwater Dolphin Kick
In freestyle, backstroke, and butterfly races, swimmers may travel up to 15 m underwater following the dive entry and after the wall push-off in turns. The underwater dolphin kick (UDK), a cyclical movement comprising oscillations of the segments of the lower limb, is customarily used in this underwater phase. It was unknown whether kinematics and coordination patterns differed between UDK performed prone and supine. This thesis contributes to the current body of knowledge on the key performance variables of prone and supine UDK technique, thereby providing practical outcomes for coaches and practitioners to assess and improve UDK. Four studies were conducted to determine: 1) how start and turn performance of Great Britain’s (GB) swimmers compare with the rest of the world, 2) if a velocity-meter can be used interchangeably with video-based measurement of UDK speed, 3) if key kinematic metrics differ between prone and supine UDK, and 4) if coordination patterns differ between prone and supine UDK. Relative to clean swimming speeds, GB had slower starts and turns than the rest of the world in some events, but were equal to or faster in other events. Compared to the video-based method, the velocity-meter over- and under-estimated maximum and minimum kick cycle speeds, respectively; mean speeds were similar. With the exception of one upper body metric, no significant differences were found between prone and supine UDK kinematics. Differences were found between sexes, with males demonstrating significantly larger kick amplitude, maximum toe speeds, and distance per kick. Males reached maximum knee separation earlier in the kick cycle, and minimum foot separation later than females. Hip extension velocity, knee flexion velocity, and ankle plantar-flexion velocity were key determinants of UDK performance. Faster kickers maintained horizontal centre of mass speed over the entire underwater phase better than the slower kickers. A temporally sequential movement pattern was found for the knee flexion phase, but not the knee extension phase. Furthermore, coordination patterns between the hip and knee, and knee and ankle, did not differ significantly between prone and supine kicking. This thesis demonstrated that, though individual differences in technique do exist, the kinematics and coordination patterns observed in prone and supine UDK do not differ significantly
A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. A dataset used is 960 slices of CT scan collected from Iraqi patients /Ibn Al-Nafis teaching hospital. The performance metrics are used in this study (accuracy, recall, precision, and F1 scores). The results indicate that the proposed approach generated a high-quality model for the collected dataset, with an overall accuracy of 98.95% and an overall recall of 97 %
Habits of Mind: Designing Courses for Student Success
Although content knowledge remains at the heart of college teaching and learning, forward-thinking instructors recognize that we must also provide 21st-century college students with transferable skills (sometimes called portable intellectual abilities) to prepare them for their futures (Vazquez, 2020; Ritchhart, 2015; Venezia & Jaeger, 2013; Hazard, 2012). To “grow their capacity as efficacious thinkers to navigate and thrive in the face of unprecedented change” (Costa et al., 2023), students must learn and improve important study skills and academic dispositions throughout their educational careers. If we do not focus on skills-building in college courses, students will not be prepared for the challenges that await them after they leave institutions of higher education. If students are not prepared for these postsecondary education challenges, then it is fair to say that college faculty have failed them
Remote sensing for cost-effective blue carbon accounting
Blue carbon ecosystems (BCE) include mangrove forests, tidal marshes, and seagrass meadows, all of which are currently under threat, putting their contribution to mitigating climate change at risk. Although certain challenges and trade-offs exist, remote sensing offers a promising avenue for transparent, replicable, and cost-effective accounting of many BCE at unprecedented temporal and spatial scales. The United Nations Framework Convention on Climate Change (UNFCCC) has issued guidelines for developing blue carbon inventories to incorporate into Nationally Determined Contributions (NDCs). Yet, there is little guidance on remote sensing techniques for monitoring, reporting, and verifying blue carbon assets. This review constructs a unified roadmap for applying remote sensing technologies to develop cost-effective carbon inventories for BCE – from local to global scales. We summarise and discuss (1) current standard guidelines for blue carbon inventories; (2) traditional and cutting-edge remote sensing technologies for mapping blue carbon habitats; (3) methods for translating habitat maps into carbon estimates; and (4) a decision tree to assist users in determining the most suitable approach depending on their areas of interest, budget, and required accuracy of blue carbon assessment. We designed this work to support UNFCCC-approved IPCC guidelines with specific recommendations on remote sensing techniques for GHG inventories. Overall, remote sensing technologies are robust and cost-effective tools for monitoring, reporting, and verifying blue carbon assets and projects. Increased appreciation of these techniques can promote a technological shift towards greater policy and industry uptake, enhancing the scalability of blue carbon as a Natural Climate Solution worldwide
Land Use and Land Cover Mapping in a Changing World
It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems
Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification
Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation
Cross-cultural realization of the speech act of requests: case study of Algerian Ph.D. students
The present study is concerned with probing how Algerian Ph.D. students formulate
requests to their supervisors at a UK higher education institution; and how their
supervisors respond to these. The data are derived from a case study focussing on a
group of fifteen Algerian PhD students and six Supervisors at Manchester Metropolitan
University (hereafter MMU). The thesis falls within third wave approach to politeness
research, which advocates the integration of aspects from classical and discursive
approaches (Bousfield, 2010; Leech, 2014; Haugh and Culpeper, 2018) into the analysis
of politeness phenomenon. The current research, therefore, seeks to explores the speech
event of requests as a fundamentally written interactional phenomenon. In other
words, it considers this type of communication as a phenomenon that needs both
interlocutors; those who produce requests and those who respond to these successively.
More specifically, the study aims to examine how these participants (Algerian Ph.D.
students) attempt, using strategically different politeness strategies (Brown and
Levinson, 1987), to achieve their interactional goals in an asymmetrical powerrelations
context. Further, and while looking at the supervisors’ response, the study also
investigates how the receivers perceive those requests from the Algerian Ph.D.
students. While studying the speech acts (Austin 1962) of requests and responses to
these, the study also explores the socio-cultural factors influencing the use of politeness
strategies use and responses to the requests.
To meet the aims of the research project, a mixed method approach was used to
elicit the performances and perceptions of the participants. On one hand, Interactionbased
Discourse Completion Tasks (Hereafter, I-DCTs) were designed for the purpose of
approaching a realistic performance of requests and responses to requests in email
communication. On the other hand, followed-up semi-structured interviews were
conducted with the participants to investigate and understand how the socio-cultural
factors map out and influence their use of politeness strategies. Moreover, the interviews
also aim at discovering how the supervisors perceive the students’ email requests.
While the methodological contribution in this research is an innovation in the study
of politeness and pragmatics as an adaptation of Discourse Completion Tasks is
implemented to take account of requests and responses to requests. The study is also a
contribution to knowledge through the insights provided regarding the use of politeness
strategies by an under researched Algerian population.
The results of the current study, generating from a total of 21 I-DCTs (15 from
students and 6 from supervisors) and a total of 21 semi-structured follow-up interviews
with participants, show that the participants use mostly negative politeness strategies.
The results also show that the Algerian Ph.D. students are perceived as polite by their
supervisors. The study’s contribution, therefore, adds to the existing knowledge on the
realization of the speech act of requests and politeness in an intercultural communication
context
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