3,393 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
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Discussion on drivers and proposition of approaches to support the transition of traditional electricity consumers to prosumers
In recent years, traditional power systems have undergone a significant transition, mainly
related to the massive penetration of Renewable Energy Sources (RES). More specifically, the
transformation of residential consumers into prosumers has been challenging to the traditional
operation of electricity markets. This transition brings new challenges and opportunities to
the power system, leading to new Business Model (BM). One widely discussed change is
related to a consumer-centric or prosumer-driven approach, promoting increased participation
of small consumers in power systems. The present thesis aims at discussing the recent BMs as
enablers of the increasing prosumers’ role in the energy market and power system worldwide,
deepening the discussion with a holistic view of the Brazilian context. To do so, it defines
the main features of prosumers and their general related regulation as well as possible market
designs within power systems. Moreover, the work intends to contribute to the knowledge,
identification and understanding of the main regulatory barriers and enablers for the development
of those BMs in the Brazilian energy market. In addition, it discusses enabling technologies to
properly create the conditions that sustain new prosumer-driven markets. Then, it presents a
comprehensive review of existing and innovative BMs and a discussion on their future roles in
modern power systems and, in the Brazilian regulatory framework seeking to guide the decisions
for the country to develop its political and regulatory environment in the future. Moreover, a
set of recommendations for promoting these BMs in the power system worldwide is provided
along with policy recommendations to promote prosumers aggregation in the Brazilian energy
sector. An important conclusion is that, even though economically possible, not all innovative
BMs can spread around the world due to regulatory issues. Seeking to further explore one of
the prosumer-driven approaches presented and the challenges imposed by this innovative BM,
a study of energy and reserve markets based on the Peer-to-Peer (P2P) structure is carried out.
This structure is very promising for the prosumers’ promotion but presents some challenges for
the network operation. A critical challenge is to ensure that network constraints are not violated
due to energy trades between peers and neither due to the use of reserve capacity. Therefore,
two methodologies are proposed. First, is proposed a three-step approach (P2PTDF), using
Topological Distribution Factors (TDF) to penalize peers responsible for violations that may
occur in the network constraints, ensuring a feasible solution. Second, it is proposed a new
integrated prosumers-DSO approach applied in P2P energy and reserve tradings that also ensures
the feasibility of both energy and reserve transactions under network constraints. The proposed
approach includes the estimation of reserve requirements based on the RES uncertain behavior
from historical generation data, which allows identifying RES patterns. The proposed models
are assessed through a case study that uses a 14-bus system, under the technical and economic
criteria. The results show that the approaches can ensure a feasible network operation.Nos últimos anos, os sistemas tradicionais de energia passaram por uma transição significativa, principalmente relacionada à penetração massiva de fontes de energia renováveis (do
inglês, Renewable energy sources-RES). Mais especificamente, a transformação de consumidores
residenciais em prosumidores tem desafiado a atual operação do mercado de energia elétrica.
Essa transição traz novos desafios e oportunidades para o sistema elétrico, levando a novos
modelos de negócios (do inglês, Business Models-BM). Uma mudança amplamente discutida
está relacionada a uma abordagem centrada no consumidor ou direcionada ao prossumidor,
promovendo maior participação de pequenos consumidores nos sistemas de energia. A presente
tese tem como objetivo discutir os recentes BMs como facilitadores do crescente papel dos
prosumidores no mercado de energia e no sistema elétrico mundial, aprofundando a discussão
com uma visão holística do contexto brasileiro. Para tanto, define as principais características
dos prosumidores e sua regulamentação geral relacionada, bem como possíveis designs de
mercado dentro dos sistemas de energia. Além disso, o trabalho pretende contribuir para o
conhecimento, identificação e compreensão das principais barreiras regulatórias e facilitadoras
para o desenvolvimento desses BMs no mercado brasileiro de energia. Assim como, discutir as
tecnologias importantes para criar adequadamente as condições que sustentam novos mercados
orientados ao consumidor final. Em seguida, apresenta uma revisão abrangente dos BMs existentes e inovadores e uma discussão sobre seus papéis futuros nos sistemas de energia modernos
e, no quadro regulatório brasileiro, buscando orientar as decisões para que o país desenvolva
seu ambiente político e regulatório no futuro. Além disso, um conjunto de recomendações
para promover esses BMs no sistema de energia em todo o mundo é fornecido juntamente com
recomendações de políticas para promover a agregação de prosumidores no setor de energia
brasileiro. Uma conclusão importante é que, mesmo sendo economicamente possível, nem todos
os BMs inovadores podem se espalhar pelo mundo devido a obstáculos regulatórias. Buscando
explorar ainda mais uma das abordagens orientadas ao prosumidor apresentadas e os desafios
impostos por este BM inovador, é realizado um estudo dos mercados de energia e de reserva com
base na estrutura ponto a ponto (do inglês, peer-to-peer-P2P). Esta estrutura é muito promissora
para a promoção dos prosumidores mas apresenta alguns desafios para o funcionamento da rede.
Um desafio crítico é garantir que as restrições da rede não sejam violadas devido a negociações
de energia entre pares e nem devido ao uso da capacidade de reserva. Portanto, duas metodologias são propostas. Primeiramente, é proposta uma abordagem em três passos (P2PTDF),
utilizando Fatores de Distribuição Topológica (do inglês, Topological Distribution Factors-TDF
) para penalizar os peers responsáveis por violações que possam ocorrer nas restrições da rede,
garantindo uma solução viável. Em segundo lugar, é proposta uma nova abordagem integrada
de prosumidores-DSO aplicada em transações P2P de energia e reserva que também garante a
viabilidade de transações de energia e reserva sob restrições de rede. A abordagem proposta
inclui a estimativa dos requisitos de reserva com base no comportamento incerto da RES a partir
de dados históricos de geração, o que permite identificar padrões de RES. Os modelos propostos
são avaliados através de um estudo de caso que utiliza um sistema de 14 barras, sob os critérios
técnico e econômico. Os resultados mostram que as abordagens podem garantir uma operação
de rede viável abrangendo energia e mercados de reserva
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
Less is More: Restricted Representations for Better Interpretability and Generalizability
Deep neural networks are prevalent in supervised learning for large amounts of tasks such as image classification, machine translation and even scientific discovery.
Their success is often at the sacrifice of interpretability and generalizability. The increasing complexity of models and involvement of the pre-training process make the inexplicability more imminent. The outstanding performance when labeled data are abundant while prone to overfit when labeled data are limited demonstrates the difficulty of deep neural networks' generalizability to different datasets.
This thesis aims to improve interpretability and generalizability by restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in a low-data regime.
We consider two strategies of restricting representations: (1) adding bottleneck, and (2) introducing compression. Given input x, suppose we want to learn y with the latent representation z (i.e. x→z→y), adding bottleneck means adding function R such that L(R(z)) < L(z) and introducing compression means adding function R so that L(R(y)) < L(y) where L refers to the number of bits. In other words, the restriction is added either in the middle of the pipeline or at the end of it.
We first introduce how adding information bottleneck can help attribution analysis and apply it to investigate BERT's behavior on text classification in Chapter 3.
We then extend this attribution method to analyze passage reranking in Chapter 4, where we conduct a detailed analysis to understand cross-layer and cross-passage behavior.
Adding bottleneck can not only provide insight to understand deep neural networks but can also be used to increase generalizability.
In Chapter 5, we demonstrate the equivalence between adding bottleneck and doing neural compression. We then leverage this finding with a framework called Non-Parametric learning by Compression with Latent Variables (NPC-LV), and show how optimizing neural compressors can be used in the non-parametric image classification with few labeled data.
To further investigate how compression alone helps non-parametric learning without latent variables (NPC), we carry out experiments with a universal compressor gzip on text classification in Chapter 6.
In Chapter 7, we elucidate methods of adopting the perspective of doing compression but without the actual process of compression using T5.
Using experimental results in passage reranking, we show that our method is highly effective in a low-data regime when only one thousand query-passage pairs are available.
In addition to the weakly supervised scenario, we also extend our method to large language models like GPT under almost no supervision --- in one-shot and zero-shot settings. The experiments show that without extra parameters or in-context learning, GPT can be used for semantic similarity, text classification, and text ranking and outperform strong baselines, which is presented in Chapter 8.
The thesis proposes to tackle two big challenges in machine learning --- "interpretability" and "generalizability" through restricting representation. We provide both theoretical derivation and empirical results to show the effectiveness of using information-theoretic approaches. We not only design new algorithms but also provide numerous insights on why and how "compression" is so important in understanding deep neural networks and improving generalizability
Mapping the connections : An integrated approach to mapping Nature’s contributions to people in a Nordic biosphere reserve
Naturen og hennes økosystemer gir flere bidrag til mennesker som gagner vår velvære. Disse økosystemtjenestene er truet på grunn av omfattende menneskelige aktiviteter som har resultert i omfattende arealbruksendringer, raske klimaendringer og destruktiv overhøsting. Å anerkjenne og verdsette økosystemtjenester er en måte å gjøre rede for dem i politiske handlinger for å forvalte økosystemer bærekraftig for mennesker og natur. Imidlertid er det forskjellige måter som økosystemtjenester kan verdsettes på tvers av biofysiske, sosiokulturelle og monetære verdidomener, og disse verdiene samhandler innenfor og på tvers av domener. For å verdsette økosystemtjenester fullt ut er det behov for ikke bare å utvikle verdsettingsmetoder på tvers av alle tre domenene, men også måter å integrere på tvers av dem. Økosystemtjenester er ikke jevnt fordelt, og deres verdier er forskjellige i rom på grunn av ulike sosiale og økologiske faktorer. For å administrere økosystemtjenester må vi derfor også se hvordan og hvorfor verdiene deres varierer på tvers av landskap. og vi må gjøre rede for det dynamiske forholdet mellom økosystemtjenester på tvers av verdidomener og sosial-økologiske kontekster. I denne oppgaven presenterer jeg fire artikler som tar for seg noen av disse utfordringene med økosystemtjenester innenfor konteksten av et UNESCO-biosfærereservat på Vestlandet.
Først kartla vi sosiokulturelle verdier for økosystemtjenester ved hjelp av en undersøkelse av geografiske informasjonssystemer (PPGIS) for offentlig deltakelse. Vi undersøkte hvordan sosiokulturelle verdier for økosystemtjenesteverdier varierer på tvers av et biosfærereservat, hvilke verdier som vanligvis forekommer sammen i bunter, og hvilke sosial-økologiske egenskaper som bestemmer fordelingen av disse buntene. Folk kartla hovedsakelig steder for friluftsliv, biologisk mangfold, landbruksprodukter og kulturarv, hovedsakelig i områder med høyere menneskelig befolkning. Vi identifiserte fem bunter som representerer koblede biokulturelle verdier for landbruk og kulturarv, friluftsliv og biologisk mangfold, og vill mat og mental velvære. Generelt var tilgjengelighet den viktigste faktoren som avgjorde fordelingen av buntene.
For det andre integrerte vi biofysiske verdier med sosiokulturelle verdier og kartla økosystemtjenester i biosfærereservatet. Vi undersøkte fordelingen av disse integrerte økosystemtjenesteverdiene over biosfærereservatsonene og deres bunter over to romlige skalaer. Økosystemtjenestene samlet inn i tre distinkte sosial-økologiske systemarketyper som var like i distribusjon og relative økosystemtjenesteverdier på begge romlige skalaer. Buntene var også godt tilpasset relative økosystemtjenesteverdier i biosfærereservatsonene (kjerne, buffer og overgang), noe som indikerer at buntene fanger opp de sosialøkologiske systemene i sonene. Disse resultatene viser at det er viktig å vurdere sonenes sosialøkologiske kontekst for å gi tilstrekkelig kunnskap til å informere ledelsen.
For det tredje brukte vi en ny kombinasjon av PPGIS og sosiale nettverksdata for å kartlegge økosystemets samproduksjonsnettverk i biosfærereservatet. Vi identifiserte fire komponenter i økosystemets samproduksjonsnettverk som sosiokulturelle verdier, direkte ledelse, styring og forskning/kunnskapsproduksjon. Først kartla vi den relative oppmerksomheten ulike økosystemtjenester mottok fra disse samproduksjonskomponentene. Deretter kartla vi det sosiale nettverket for kommunikasjon om ulike økosystemtjenester blant samproduksjonskomponentene. Vi fant misforhold mellom ulike komponenter i samproduksjonsnettverket. Viktigere, vi identifiserte at kulturelle økosystemer ble høyt verdsatt, men får relativt mindre styring og særlig forskningsoppmerksomhet. Videre var de primære forvalterne av kulturelle økosystemtjenester også dårlig koblet i økosystemtjenestens samproduksjonssosiale nettverk. Resultatene viser viktigheten av å tenke på samproduksjon av økosystemtjenester som et relasjonelt nettverk og av å kartlegge hva som diskuteres av hvem.
Til slutt integrerte vi økologiske feltundersøkelser og PPGIS for å utforske (mis)matchen i biofysiske og sosiokulturelle verdier for økosystemtjenester i sammenheng med landforlatelse og skogplanting. Biofysiske verdier for økosystemtjenester var mer like på tvers av vegetasjonstyper, mens sosiokulturelle verdier generelt var høyest i åpen vegetasjon og uplantede skogtyper. Økosystemtjenesten med størst forskjell i biofysiske og sosiokulturelle verdier global klimaregulering, mens biologisk mangfold og landbruksprodukter var like på tvers av verdidomenene. Sosiokulturelle verdier var ikke jevnt fordelt på studiedeltakerne. Det var to distinkte grupper som representerte eldre bønder bosatt i regionen med høye verdier for å levere økosystemtjenester på den ene siden, og yngre kvinner som ikke er innbyggere som verdsetter regulering og vedlikehold av økosystemtjenester. Denne studien viser viktigheten av å vurdere ulike både ulike verdidomener og faktorene som påvirker disse verdiene i beslutninger om endring av arealbruk.Nature and her ecosystems make multiple contributions to people that benefit our wellbeing. These ecosystem services are under threat due to extensive human activities that have resulted in widespread land-use change, rapid climate change and destructive overharvesting. Acknowledging and valuing ecosystem services is a way to account for them in policy actions to manage ecosystems sustainably for people and nature. However, there are different ways in which ecosystem services can be valued across biophysical, socio-cultural, and monetary value-domains and these values interact within and across domains. To fully value ecosystem services there is a need to not only develop valuation methods across all three domains, but also ways of integrating across them. Ecosystem services are not evenly distributed, and their values differ in space due to various social and ecological factors. Therefore, to manage ecosystem services we also need to know how and why their values vary across landscapes, and we need to account for the dynamic relationship between ecosystem services across the value-domains and social-ecological contexts. In this thesis I present four papers that addresses some of these challenges with ecosystem services within the context of a UNESCO Biosphere Reserve in western Norway.
First, we mapped socio-cultural values for ecosystem services using a public participation geographic information systems (PPGIS) survey. We explored how socio-cultural values for ecosystem service values vary across a biosphere reserve, which values commonly co-occur in bundles, and what social-ecological characteristics determine the distribution of those bundles. People mapped predominantly places for outdoor recreation, biodiversity, agricultural products, and cultural heritage predominantly in areas with higher human populations. We identified five bundles representing linked biocultural values for agriculture and cultural heritage, outdoor recreation and biodiversity, and wild food and mental wellbeing. In general accessibility was the most important factor that determined the distribution of the bundles.
Second, we integrated biophysical values with socio-cultural values and mapped ecosystem services in the biosphere reserve. We explored the distribution of these integrated ecosystem services values across the biosphere reserve zones and their bundles across two spatial scales. The ecosystem services bundled into three distinct social-ecological system archetypes that were similar in their distribution and relative ecosystem service values at both spatial scales. The bundles were also well matched to relative ecosystem services values of the Biosphere Reserve zones (core, buffer and transition) indicating that the bundles capture the social-ecological systems of the zones. These results show that it is important to consider the social-ecological context of the zones to provide sufficient knowledge to inform management.
Third, we used a novel combination of PPGIS and social network data to map the ecosystem co-production network in the biosphere reserve. We identified four components of the ecosystem co-production network as socio-cultural values, direct management, governance, and research/knowledge production. First, we mapped the relative attention different ecosystem services received from those co-production components. Then we mapped the social network of communication about different ecosystem services among the co-production components. We found mismatches between different components of the co-production network. Importantly, we identified that cultural ecosystems were highly valued but receive comparatively less governance and particularly research attention. Furthermore, the primary managers of cultural ecosystem services were also poorly connected in the ecosystem service co-production social-network. The results show the importance of thinking of ecosystem service co-production as a relational network and of mapping what is being discussed by whom.
Finally, we integrated ecological field surveys and PPGIS to explore the (mis)match in biophysical and socio-cultural values for ecosystem services in the context of land abandonment and afforestation. Biophysical values for ecosystem services were more similar across vegetation types while socio-cultural values were generally highest in open vegetation and unplanted forest types. The ecosystem service with the largest difference in biophysical and socio-cultural values global climate regulation, while biodiversity and agricultural products were similar across the value-domains. Socio-cultural values were not evenly spread across the study participants. There were two distinct groups representing older farmers resident in the region with high values for provisioning ecosystem services on the one hand, and non-resident younger females valuing regulating and maintenance ecosystem services. This study shows the importance of considering different value-domains and the factors that influence those values in land-use change decisions.Doktorgradsavhandlin
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields 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 modified Proportional Conflict 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 classifiers, 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, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
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 classification, 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 steer—a 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
Predicate Matrix: an interoperable lexical knowledge base for predicates
183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas
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