15,334 research outputs found

    The Yoob Case Study

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    Gil, B., Albuquerque, V., Dias, M. S., Abranches, R., & Ogando, M. (2023). Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study. In A. L. Martins, J. C. Ferreira, A. Kocian, & U. Tokkozhina (Eds.), Intelligent Transport Systems: 6th EAI International Conference, INTSYS 2022 Lisbon, Portugal, December 15–16, 2022 Proceedings (pp. 23-39). (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Vol. 486). Springer Nature. https://doi.org/10.1007/978-3-031-30855-0_2 --- This work is partially funded by national funds through FCT - Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020.The adoption of more environmentally friendly and sustainable fleets for last-mile parcel delivery within large urban centers, such as e-cargo bikes, has gained the interest of the community. The logistics infrastructure network, had to adapt to the requirements of this new type of fleet, and micro-hubs and nano-hubs emerged. In this paper we tackle spatiotemporal characterization of e-cargo bike fleet behavior, by conducting a data centered case study where we explore data from Yoob, a last mile delivery e-cargo bike logistics startup that operates in the Lisbon area and outskirts. We also address the identification of potential expansion locations to the establishment of new hubs. Our data was collected during a 4-month period (January to April 2022). By adopting state-of-the-art data science and machine learning techniques, and following the CRIPS-DM data mining method, our innovative approach discovered five clusters that are able to characterize the Yoob fleet, with variations in distances traveled, times, transported volumes and speeds. In the perspective of expanding Yoob’s e-cargo bike network, three new locations in Lisbon were signaled for potential new hub installation. To the authors knowledge this is the first study of this kind carried in Portugal, bringing new insights in the field of last-mile logistics.authorsversionpublishe

    Networked Time Series Prediction with Incomplete Data

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    A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%

    The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management

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    Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure

    FedForgery: Generalized Face Forgery Detection with Residual Federated Learning

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    With the continuous development of deep learning in the field of image generation models, a large number of vivid forged faces have been generated and spread on the Internet. These high-authenticity artifacts could grow into a threat to society security. Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training but ignore the personal privacy and security issues when personal data couldn't be centralizedly shared in real-world scenarios. Additionally, different distributions caused by diverse artifact types would further bring adverse influences on the forgery detection task. To solve the mentioned problems, the paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery). The designed variational autoencoder aims to learn robust discriminative residual feature maps to detect forgery faces (with diverse or even unknown artifact types). Furthermore, the general federated learning strategy is introduced to construct distributed detection model trained collaboratively with multiple local decentralized devices, which could further boost the representation generalization. Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery. The designed novel generalized face forgery detection protocols and source code would be publicly available.Comment: The code is available at https://github.com/GANG370/FedForgery. The paper has been accepted in the IEEE Transactions on Information Forensics & Securit

    Using knowledge graphs to infer gene expression in plants

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    IntroductionClimate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through in silico experimentation.MethodsWe developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in Arabidopsis thaliana and Populus trichocarpa plants exposed to drought conditions.ResultsA graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways.DiscussionThis suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph

    Urbanised forested landscape: Urbanisation, timber extraction and forest care on the Vișeu Valley, northern Romania

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    By looking at urbanisation processes from the vantage point of the forest, and the ways in which it both constitutes our living space while having been separated from the bounded space of the urban in modern history, the thesis asks: How can we (re)imagine urbanisation beyond the limits of the urban? How can a feminine line of thinking engage with the forest beyond the capitalist-colonial paradigm and its extractive project? and How can we “think with care” (Puig de la Bellacasa 2017) towards the forest as an inhabitant of our common world, instead of perpetuating the image of the forest as a space outside the delimited boundaries of the city? Through a case study research, introducing the Vișeu Valley in northern Romania as both a site engaged in the circulation of the global timber flow, a part of what Brenner and Schmid (2014) name “planetary urbanisation”, where the extractive logging operations beginning in the late XVIIIth century have constructed it as an extractive landscape, and a more than human landscape inhabited by a multitude of beings (animal, plant, and human) the thesis argues towards the importance of forest care and indigenous knowledge in landscape management understood as a trans-generational transmission of knowledge, that is interdependent with the persistence of the landscape as such. Having a trans-scalar approach, the thesis investigates the ways in which the extractive projects of the capitalist-colonial paradigm have and still are shaping forested landscapes across the globe in order to situate the case as part of a planetary forest landscape and the contemporary debates it is engaged in. By engaging with emerging paradigms within the fields of plant communication, forestry, legal scholarship and landscape urbanism that present trees and forests as intelligent beings, and look at urbanisation as a way of inhabiting the landscape in both indigenous and modern cultures, the thesis argues towards viewing forested landscapes as more than human living spaces. Thinking urbanisation through the case of the Vișeu Valley’s urbanised forested landscape, the thesis aligns with alternate ways of viewing urbanisation as co-habitation with more than human beings, particularly those emerging from interdisciplinary research in the Amazon river basin (Tavares 2017, Heckenberger 2012) and, in light of emerging discourses on the rights of nature, proposes an expanded concept of planetary citizenship, to include non-human personhood

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    On the Temporal-spatial Analysis of Estimating Urban Traffic Patterns Via GPS Trace Data of Car-hailing Vehicles

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    Car-hailing services have become a prominent data source for urban traffic studies. Extracting useful information from car-hailing trace data is essential for effective traffic management, while discrepancies between car-hailing vehicles and urban traffic should be considered. This paper proposes a generic framework for estimating and analyzing urban traffic patterns using car-hailing trace data. The framework consists of three layers: the data layer, the interactive software layer, and the processing method layer. By pre-processing car-hailing GPS trace data with operations such as data cutting, map matching, and trace correction, the framework generates tensor matrices that estimate traffic patterns for car-hailing vehicle flow and average road speed. An analysis block based on these matrices examines the relationships and differences between car-hailing vehicles and urban traffic patterns, which have been overlooked in previous research. Experimental results demonstrate the effectiveness of the proposed framework in examining temporal-spatial patterns of car-hailing vehicles and urban traffic. For temporal analysis, urban road traffic displays a bimodal characteristic while car-hailing flow exhibits a 'multi-peak' pattern, fluctuating significantly during holidays and thus generating a hierarchical structure. For spatial analysis, the heat maps generated from the matrices exhibit certain discrepancies, but the spatial distribution of hotspots and vehicle aggregation areas remains similar

    A conceptual framework for developing dashboards for big mobility data

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    Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets

    The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review

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    With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs
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