Today, streaming, Artificial Intelligence, and the Internet of Things (IoT) are being some of the main drivers to accelerate process automation in various companies. These technologies are often connected to critical ...
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Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due...
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Serverless computing, also known as Function as a Service, is a new paradigm that aims to separate the user of the platform from details about any infrastructure deployment. The problem lies in the fact that all the c...
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In software development, code autocomplete can be an essential tool in order to accelerate coding. However, many of these tools built into the IDEs are limited to suggesting only methods or arguments, often presenting...
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Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature o...
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Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts’ final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system’s effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance.
The extensive exploration of the Low Earth Orbit (LEO) has created a dangerous spacial environment, where space debris has threatened the feasibility of future operations. In this sense, Active Debris Removal (ADR) mi...
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Service identities are crucial for authentication and access control, ensuring that only authorized services access specific resources. The SPIFFE framework addresses workload identity management and authentication ef...
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ISBN:
(数字)9798331507589
ISBN:
(纸本)9798331507596
Service identities are crucial for authentication and access control, ensuring that only authorized services access specific resources. The SPIFFE framework addresses workload identity management and authentication effectively but needs support for solutions (e.g., extensible tokens) that fine-granular authorization mechanisms in distributed scenarios can use. In this context, we present the Lightweight SVID (LSVID), an identity document in JSON format that can be extended and used as a token. As an extensible token, Lightweight SVID (LSVID) enables features such as delegation, attenuation, and traceability, enhancing its flexibility and applicability. Our approach provides efficient handling of token extensions and validations, demonstrated through a proof-of-concept implemented in Go. Baseline results indicate that LSVID critical operations are efficient, with processing times in the microsecond range, offering significant functional advantages over the traditional JWT-SVIDs, one of two key security documents from SPIFFE.
A contemporaneous data center (DC) hosts multiple competitive network data flows from different applications, sharing the intermediate switches capacities. In this context, congestion control and avoidance on Transmis...
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ISBN:
(纸本)9781665435413
A contemporaneous data center (DC) hosts multiple competitive network data flows from different applications, sharing the intermediate switches capacities. In this context, congestion control and avoidance on Transmission Control Protocol (TCP) are critical tasks to ensure the quality of service for hosted applications. Specifically, Software Defined Networking (SDN) created an opportunity to avoid congestion once the centralized controller can gather ongoing and historical information from all network switches and flows. However, the data gathered is enormous, and fast-computing algorithms are crucial for decision-making. In this sense, this work proposes Reinforcement Learning- and SDN-aided Congestion Avoidance Tool (RSCAT), which uses data classification to determine if the network is congested and actor-critic reinforcement learning to find better TCP parameters. Our experimental analysis shows RSCAT could decrease the Flow Completion Time (FCT) of DCTCP and CUBIC variants in several cases without requiring any software update on DC end-points.
Vehicle-to-Everything (V2X) networks require low-latency communications utilizing a broad spectrum while operating under jamming. In this sense, low complexity antenna array-based broadband jamming mitigation schemes ...
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ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
Vehicle-to-Everything (V2X) networks require low-latency communications utilizing a broad spectrum while operating under jamming. In this sense, low complexity antenna array-based broadband jamming mitigation schemes are crucial in order to allow low latency communication and low-cost hardware. In this paper we propose low-complexity algorithms for signal recovery in broadband processing scenario applied to V2X. Numerical simulation of a jamming scenario demonstrate the proposed algorithm achieving the same performance in terms of signal-to-interference plus noise ratio (SINR) as the state-of-the-art while taking signiflcantly less time to compute.
The ornamental rock industry in Brazil is distinguished by its diverse assortment of rock types, presenting a unique challenge in classification due to its inherent subjectivity and reliance on expert judgment. to add...
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ISBN:
(数字)9798331540975
ISBN:
(纸本)9798331540982
The ornamental rock industry in Brazil is distinguished by its diverse assortment of rock types, presenting a unique challenge in classification due to its inherent subjectivity and reliance on expert judgment. to address this problem, the present study introduces a publicly accessible database encompassing 12 distinct classes of ornamental rocks, including granite, marble, and quartzite. This database comprises 1,798 images sourced directly from entities within the stone sector. We employ and compare the performance of seven neural network models for ornamental rock image classification: VGG16, VGG19, ResNet50, ResNet101, Xception, InceptionV3, and the Vision Transformer (ViT). Our empirical analysis reveals that the ViT model outperforms conventional architectures, achieving an accuracy rate of 98.36%.
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