1,720,958 research outputs found
Double-Reservoir Deep Echo State Network Architecture for short-term Electricity Demand Forecasting
No full textThis study presents a novel, streamlined and scalable deep Echo State Network (ESN) architecture for demand forecasting in electricity markets. In contemporary electricity markets, where each new day it becomes more difficult to achieve smooth technical operations and not too much volatile electricity prices, the market stakeholders face the problem of forecasting energy demand with increasingly high accuracy and computational efficiency, because knowing demand in advance helps manage coming problems. Deep ESNs demonstrated themselves to be very fitting for the forecasting endeavor. We hence propose a deep ESN architecture for demand forecasting that has higher efficiency than currently used deep ESNs, while maintaining similar forecasting accuracy. Our design strategy is based on disentangling the ESN readout matrix from the individual reservoirs that make the ESN deep, just maintaining the readout weights from the output layer reservoir only. Furthermore, we use Particle Swarm optimization (PSO) to optimize the inter-reservoir connecting weights. We will prove, by numerical testing, that this allows our architecture to achieve an accuracy close to that of current deep ESNs, while being more scalable and being based on fewer parameters than the standard deep ESNs. Specifically, we evaluate this architecture by forecasting a daily average electricity demand time series from the Spanish electricity market. Our architecture, once optimized by PSO, is shown to improve over some common benchmarks and state-of-the-art methods
Decoding DOOH Viewability using YOLO for Privacy-Friendly Human Silhouette Identification on LiDAR Point Clouds
No full textTraditional methods for measuring digital out-of-home (DOOH) advertising effectiveness often rely on static data or camera footage, leading to limitations in accuracy and real-time insights. This research proposes a novel approach that leverages the combined power of LiDAR technology and YOLOv8, a state-of-the-art object detection model, to achieve precise and privacy-friendly human silhouette identification for DOOH performance measurement. By extracting 3D point cloud data from LiDAR sensors and employing YOLOv8's efficient object detection capabilities, the model accurately identifies and tracks pedestrians in the vicinity of DOOH displays. This information, combined with LiDAR's performance under varying weather and lighting conditions, offers a significant improvement over traditional methods, providing advertisers with valuable real-time data on audience engagement and campaign effectiveness. The comparison with the same model performance trained on a standard MC-COCO 2017 dataset presented comparable accuracy but faster inference times. Furthermore, the focus on LiDAR data ensures privacy by avoiding the use of facial recognition or other sensitive personal information. This research demonstrates the feasibility and potential of LiDAR-based human silhouette identification for DOOH performance measurement, paving the way for a more data-driven and effective advertising landscape
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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