As the Industry 4.0 shifts towards the adoption of autonomous mobile robots (AMRs) in warehouses, decentralized decision-making has become a key design principle. Multi-robot task allocation (MRTA) is a problem that i...
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This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% r...
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This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems.
Current methods in mapping the availability of WiFi networks, such as crowdsourcing platforms (e.g. Project BASS and CoverageMap) and dedicated wardriving, face limitations in terms of data recency, volume, and cost-e...
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ISBN:
(数字)9798331521219
ISBN:
(纸本)9798331521226
Current methods in mapping the availability of WiFi networks, such as crowdsourcing platforms (e.g. Project BASS and CoverageMap) and dedicated wardriving, face limitations in terms of data recency, volume, and cost-effectiveness. Due to the downsides of both these methods, opportunistic wardriving is proposed which utilizes public utility vehicles (PUVs). This study investigates the feasibility of PUVs as potential wardriving vehicles for opportunistic coverage mapping. This method is cost-efficient and removes the problem of data recency because of its continuous daily trips. The findings suggest that opportunistic wardriving with PUVs is viable for wardriving with its capability to detect a high volume of access points (APs). Multiple runs within the University of the Philippines Diliman campus showed the PUVs’ effectiveness in detecting a significant number of APs, with higher success rates at closer distances. Comparatively, warwalking detected fewer APs, but there was a significant overlap between the methods. The study also highlighted significant differences in detected AP types, with wardriving finding more mobile hotspots and miscellaneous devices than warwalking. Overall, the results underline the effectiveness of PUVs in providing extensive WiFi coverage data and further enhancements to the detection system could optimize the approach.
Today, most database-backed web applications depend on the database to handle deadlocks. At runtime, the database monitors the progress of transaction execution to detect deadlocks and abort affected transactions. How...
Today, most database-backed web applications depend on the database to handle deadlocks. At runtime, the database monitors the progress of transaction execution to detect deadlocks and abort affected transactions. However, this common detect-and-recover strategy is costly to performance as aborted transactions waste CPU *** avoid deadlock-induced performance degradation, developers aim to reorganize the application code to remove deadlocks. Unfortunately, doing so is difficult for web applications. Not only do their implementations include hundreds of thousands of LoCs, but they also use third-party object-relational mapping (ORM) frameworks which hide database access details. Consequently, it is hard for developers to accurately diagnose *** propose WeSEER, a deadlock diagnosis tool for web applications. To overcome the opacity of ORMs, WeSEER performs concolic execution on unit tests to extract a web application’s transactions as a sequence of template statements with symbolic inputs as well as path conditions that enable the sequence. WeSEER then analyzes the extracted transactions based on fine-grained lock modeling to identify potential deadlocks and report the code locations that cause them. We implement WeSEER for Java-based (OpenJDK) web applications, and use it to analyze two popular open-source e-commerce applications, Broadleaf and Shopizer. WeSEER has successfully identified 18 potential deadlocks in Broadleaf and Shopizer. Eliminating these identified deadlocks can result in up to 39.5× and 4.5× throughput improvement for Broadleaf and Shopizer, respectively.
Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customizatio...
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As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of...
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science gateways have been widely utilized by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and ...
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ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transforma...
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This article introduces Follow-Me AI, a concept designed to enhance user interactions with smart environments, optimize energy use, and provide better control over data captured by these environments. Through AI agent...
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This paper presents a unified approach for repre-senting multiple domains alongside production in cyber-physical production systems (CPPSs) through domain-specific languages (DSLs). The approach is illustrated using m...
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ISBN:
(数字)9798350361230
ISBN:
(纸本)9798350361247
This paper presents a unified approach for repre-senting multiple domains alongside production in cyber-physical production systems (CPPSs) through domain-specific languages (DSLs). The approach is illustrated using material flows (MFs) as an example. The paper identifies requirements for DSLs in CPPSs and MFs, including concurrency, synchronization, constraints, and heterogeneity support. Subsequently, a plugin system is in-troduced for the existing Production Flow Description Language (PFDL), which allows users to incorporate additional domain-specific functionality in the form of plugins. To demonstrate this approach, we present the MF plugin, resulting in the combined PFDLMF. This showcases the reusability of such an approach and the easy integration of new CPPS domains. We envision the convergence of the PFDL with plugins towards a unified flow description language for CPPSs. A practical CPPS use-case demonstrates the expressiveness of the PFDLMF in modeling and executing complex MFs in an agent-based architecture.
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