3,487 research outputs found
Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in CuraƧao and Amsterdam, between 2019-2022
In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in CuraƧao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet
Structuring the Stateās Voice of Contention in Harmonious Society: How Party Newspapers Cover Social Protests in China
During the Chinese Communist Partyās (CCP) campaign of building a āharmonious societyā, how do the official newspapers cover the instances of social contention on the ground? Answering this question will shed light not only on how the party press works but also on how the state and the society interact in todayās China. This thesis conceptualises this phenomenon with a multi-faceted and multi-levelled notion of āstate-initiated contentious public sphereā to capture the complexity of mediated relations between the state and social contention in the party press. Adopting a relational approach, this thesis analyses 1758 news reports of āmass incidentā in the Peopleās Daily and the Guangming Daily between 2004 and 2020, employing cluster analysis, qualitative comparative analysis, and social network analysis. The thesis finds significant differences in the patterns of contentious coverage in the party press at the level of event and province and an uneven distribution of attention to social contention across incidents and regions. For āreported regionsā, the thesis distinguishes four types of coverage and presents how party press responds differently to social contention in different scenarios at the provincial level. For āidentified incidentsā, the thesis distinguishes a cumulative type of visibility based on the quantity of coverage from a relational visibility based on the structure emerging from coverage and explains how different news-making rationales determine whether instances receive similar amounts of coverage or occupy similar positions within coverage. Eventually, by demonstrating how the Chinese state strategically uses party press to respond to social contention and how social contention is journalistically placed in different positions in the stateās eyes, this thesis argues that what social contention leads to is the establishment of complex state-contention relations channelled through the party press
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (āAIā) 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 ā 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 CatĆ³lica 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
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steerāa gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
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