1,720,962 research outputs found
Design of an AI-driven Architecture with Cobots for Digital Transformation to Enhance Quality Control in the Food Industry
No full textIn recent years, the rapid evolution of smart technologies has spurred enterprises to undergo digital transformations, revolutionizing their business processes and operations. This shift, known as Digital Transformation, has permeated diverse sectors, particularly impacting production systems. Notably, Artificial Intelligence (AI) and robotic automation have emerged as pivotal drivers in this transformation, promising enhanced efficiency and innovation in industrial digitization. This paper presents a novel architecture designed to facilitate digital transformation within enterprises, harnessing the capabilities of advanced collaborative robots (cobots) and cutting-edge image segmentation techniques. Focused on a practical scenario within a food production environment, our proposed architecture aims to seamlessly integrate a cobot and a camera in an automatic system for efficient cardboard disposal. Specifically, our attention is drawn to the challenge of differentiating sections of food packaging suitable for disposal from those contaminated with stains or organic residues, a task with significant implications for waste management efficiency. By leveraging a cloud-based architecture and deploying AI algorithms for image segmentation, localization, and robot guidance, our study showcases the tangible benefits and practical applicability of these methodologies in real-world settings. This research not only highlights the potential of AI-driven solutions in addressing specific industrial challenges but also underscores the broader impact of digital transformation on optimizing operational processes and driving innovation across sectors
Dynamic Pruning for Parsimonious CNN Inference on Embedded Systems
Full text linkAs a consequence of the current edge-processing trend, Convolutional Neural Networks (CNNs) deployment has spread to a rich landscape of devices, highlighting the need to reduce the algorithm’s complexity and exploit hardware-aided computing, as two prospective ways to improve performance on resource-constrained embedded systems. In this work, we refer to a compression method reducing a CNN computational workload based on the complexity of the data to be processed, by pruning unnecessary connections at runtime. To evaluate its efficiency when applied on edge processing platforms, we consider a keyword spotting (KWS) task executing on SensorTile, a low-power microcontroller platform by ST, and an image recognition task running on NEURAghe, an FPGA-based inference accelerator. In the first case, we obtained a 51% average reduction of the computing workload, resulting in up to 44% inference speedup, and 15% energy-saving, while in the latter, a 36% speedup is achieved, thanks to a 44% workload reduction. © 2022, Springer Nature Switzerland AG
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
Target-Aware Neural Architecture Search and Deployment for Keyword Spotting
Full text linkKeyword spotting (KWS) utilities have become increasingly popular on a wide range of mobile and home devices, representing a prolific application field for Convolutional Neural Networks (CNNs), which are commonly exploited to perform keyword classification. Addressing the challenges of targeting such resource-constrained platforms, requires a careful definition of the CNN architecture and the overall system implementation. These reasons have led to a growing need for design and optimization flows, able to intrinsically take into account the system's performance when ported on the target platform. In this work, we present a design methodology based on Neural Architecture Search, exploited to combine the exploration of the optimal network topology, the audio pre-processing scheme, and the data quantization policy. The proposed design flow includes target-awareness in the exploration loop, comparing the different design alternatives according to a model-based pre-evaluation of metrics like execution latency, memory footprint, and energy consumption, evaluated considering the application's execution on the target processing platform. We have tested our design flow to obtain target-specific CNNs for a resource-constrained commercial platform, the ST SensorTile. Considering two different application scenarios, enabling the comparison with the state-of-the-art of efficient CNN-based models for KWS, we have obtained up to a 1.8% accuracy improvement and a 40% footprint reduction in the most favorable case
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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