8,082 research outputs found
Interpreting wealth distribution via poverty map inference using multimodal data
Poverty maps are essential tools for governments and NGOs to track
socioeconomic changes and adequately allocate infrastructure and services in
places in need. Sensor and online crowd-sourced data combined with machine
learning methods have provided a recent breakthrough in poverty map inference.
However, these methods do not capture local wealth fluctuations, and are not
optimized to produce accountable results that guarantee accurate predictions to
all sub-populations. Here, we propose a pipeline of machine learning models to
infer the mean and standard deviation of wealth across multiple geographically
clustered populated places, and illustrate their performance in Sierra Leone
and Uganda. These models leverage seven independent and freely available
feature sources based on satellite images, and metadata collected via online
crowd-sourcing and social media. Our models show that combined metadata
features are the best predictors of wealth in rural areas, outperforming
image-based models, which are the best for predicting the highest wealth
quintiles. Our results recover the local mean and variation of wealth, and
correctly capture the positive yet non-monotonous correlation between them. We
further demonstrate the capabilities and limitations of model transfer across
countries and the effects of data recency and other biases. Our methodology
provides open tools to build towards more transparent and interpretable models
to help governments and NGOs to make informed decisions based on data
availability, urbanization level, and poverty thresholds.Comment: 12 pages. In Proceedings of the ACM Web Conference 2023 (WWW'23
PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors
Graph neural networks (GNNs) have recently emerged as a promising learning
paradigm in learning graph-structured data and have demonstrated wide success
across various domains such as recommendation systems, social networks, and
electronic design automation (EDA). Like other deep learning (DL) methods, GNNs
are being deployed in sophisticated modern hardware systems, as well as
dedicated accelerators. However, despite the popularity of GNNs and the recent
efforts of bringing GNNs to hardware, the fault tolerance and resilience of
GNNs have generally been overlooked. Inspired by the inherent algorithmic
resilience of DL methods, this paper conducts, for the first time, a
large-scale and empirical study of GNN resilience, aiming to understand the
relationship between hardware faults and GNN accuracy. By developing a
customized fault injection tool on top of PyTorch, we perform extensive fault
injection experiments on various GNN models and application datasets. We
observe that the error resilience of GNN models varies by orders of magnitude
with respect to different models and application datasets. Further, we explore
a low-cost error mitigation mechanism for GNN to enhance its resilience. This
GNN resilience study aims to open up new directions and opportunities for
future GNN accelerator design and architectural optimization
Conditional Feature Importance for Mixed Data
Despite the popularity of feature importance (FI) measures in interpretable
machine learning, the statistical adequacy of these methods is rarely
discussed. From a statistical perspective, a major distinction is between
analyzing a variable's importance before and after adjusting for covariates -
i.e., between and measures. Our work
draws attention to this rarely acknowledged, yet crucial distinction and
showcases its implications. Further, we reveal that for testing conditional FI,
only few methods are available and practitioners have hitherto been severely
restricted in method application due to mismatching data requirements. Most
real-world data exhibits complex feature dependencies and incorporates both
continuous and categorical data (mixed data). Both properties are oftentimes
neglected by conditional FI measures. To fill this gap, we propose to combine
the conditional predictive impact (CPI) framework with sequential knockoff
sampling. The CPI enables conditional FI measurement that controls for any
feature dependencies by sampling valid knockoffs - hence, generating synthetic
data with similar statistical properties - for the data to be analyzed.
Sequential knockoffs were deliberately designed to handle mixed data and thus
allow us to extend the CPI approach to such datasets. We demonstrate through
numerous simulations and a real-world example that our proposed workflow
controls type I error, achieves high power and is in line with results given by
other conditional FI measures, whereas marginal FI metrics result in misleading
interpretations. Our findings highlight the necessity of developing
statistically adequate, specialized methods for mixed data
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Economia colaborativa
A importância de se proceder à análise dos principais desafios jurÃdicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutÃvel, correspondendo à necessidade de se fomentar a segurança jurÃdica destas práticas, potenciadoras de crescimento económico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domÃnio, com o objetivo de identificar os problemas jurÃdicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.
O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202
Technical Dimensions of Programming Systems
Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art.
In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too.
We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them.
We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions.
Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems.
Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
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