Karnik-Mendel method (KM) is widely known for type-reduction of interval type-2 fuzzy sets. The original and enhanced solutions of KM use various forms of iteration to converge onto the result, leading to different le...
详细信息
ISBN:
(纸本)9798350319552;9798350319545
Karnik-Mendel method (KM) is widely known for type-reduction of interval type-2 fuzzy sets. The original and enhanced solutions of KM use various forms of iteration to converge onto the result, leading to different levels of computational complexities. Based on the algorithmic advancements in quantum computing, this paper proposes a non-iterative quantum computing solution to KM. We map the KM problem to the problem of minimising an objective function in the form of a binary constrained quadratic model (CQM) which can then be solved in a single run using "quantum annealing". The algorithms used in each step, together with a numerical example and the results from real quantum computer are provided. Quantum technology permitting in the future, the proposed solution can potentially remove the known defuzzification bottleneck of designing type-2 fuzzy systems.
This research paper conducts a thorough and comparative analysis of various load balancing algorithms in cloud computing environments, aiming to provide valuable insights for cloud administrators and architects in the...
详细信息
As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distr...
详细信息
ISBN:
(纸本)9798350333398
As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural network among a cluster of nodes. However, distribution may lead to additional energy consumption and dependency among devices that suffer from unstable transmission rates. Unstable transmission rates harm real-time performance of IoT devices causing low latency, high energy usage, and potential failures. Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources. This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model's performance to enable distribution in case of resource shortage. Based on the results, a multi-objective optimization problem is formulated to minimize latency and maximize accuracy as per available resources. Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution while maintaining high accuracy.
The authors present a framework for computer science (CS) instructors to evaluate and strengthen junior and senior undergraduate students’ understanding of parallel and distributedcomputing (PDC) concepts through a ...
详细信息
The urgency in the adoption of the computing Continuum paradigm, which allows computing services to be deployed closer to users, requires a plethora of powerful, distributed devices that host such services. In this co...
详细信息
ISBN:
(纸本)9798350368529;9798350368512
The urgency in the adoption of the computing Continuum paradigm, which allows computing services to be deployed closer to users, requires a plethora of powerful, distributed devices that host such services. In this context, user-owned devices, such as phones or gaming devices, massively distributed by nature and experiencing a continuous growth in computing resources, are naturally fit to host services, and thus, their incorporation to the Continuum to provide the urgently needed infrastructural support is inevitable. In this future, two key challenges must be addressed: automating service orchestration across a massive number of devices, and ensuring device owners maintain agency over the circumstances and conditions under which services can be hosted on their devices and consumed by other users. As a first step towards this future, we present ATMOS, an automated service orchestration platform for integrating user-owned devices in the computing Continuum. ATMOS enforces user-defined policies, automatically adjusting the placement and replication of services across devices. The evaluation of ATMOS shows that it enforces all user policies, compared to state-of-the-art service orchestration systems, which violate up to 90.3% of user policies, with minimal impact on the experienced QoS.
distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves...
详细信息
ISBN:
(纸本)9798350351712;9798350351729
distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as storage systems, decentralized databases, content distribution networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. Preliminary results indicate LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributedsystems while maintaining high scalability and resilience to network churn.
In the ever-dynamic landscape of autonomy and unparalleled intelligence, the vehicles moving across any geospatial terrain create an Artificial Intelligence (AI)-powered vehicular network that can interact quickly to ...
详细信息
ISBN:
(纸本)9798350304060;9798350304053
In the ever-dynamic landscape of autonomy and unparalleled intelligence, the vehicles moving across any geospatial terrain create an Artificial Intelligence (AI)-powered vehicular network that can interact quickly to make autonomous decisions. However, the efficient working of autonomous vehicles (AVs) relies on seamless data integration from various resources, including Vehicle-to-vehicle (V2V), Vehicle-to-Infrastructure (V2I), and other communications in the AI-powered networks. In such scenarios, the velocity and pattern of vehicle mobility and motion require high effectiveness and efficiency in AI-enabled decision-making. Additionally, understanding the critical basis or feature data that triggers a particular decision from an AI model is also essential to enhance user acceptance and trust. This paper introduces an explainable edge computing-based solution to enhance the performance of AVs in an AI-powered vehicular network. The solution focuses on harnessing the power of local edge through Explainable Artificial Intelligence (XAI). This data is sourced from local edge computing nodes and is efficiently disseminated to the global edge. A comprehensive approach is designed for identifying and amalgamating important feature data from the local edge to the global edge. Through this cooperative approach, autonomous vehicular networks attain elevated efficiency, accuracy, and adaptability, making the driving experience of an AV more reliable and secure.
The proceedings contain 67 papers. The topics discussed include: understanding and improving temporal fairness on an electronic trading venue;certificateless cryptography-based rule management protocol for advanced mi...
ISBN:
(纸本)9781538632925
The proceedings contain 67 papers. The topics discussed include: understanding and improving temporal fairness on an electronic trading venue;certificateless cryptography-based rule management protocol for advanced mission delivery networks;faulty sensor data detection in wireless sensor networks using logistical regression;an adaptability-enhanced routing method for multiple gateway-based wireless sensor networks using secure dispersed data transfer;a progressive download method based on timer-driven requesting schemes using multiple TCP flows on multiple paths;policies guiding cohesive interactions among internet of things with communication clouds and social networks;and enhanced security of building automation systems through microkernel-based controller platforms.
Association rule mining (ARM) is a data mining approach used to identify interesting relationships and patterns in massive datasets. Its objective is to categories associations between various items or variables based...
详细信息
Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data. While learning image GANs on Fede...
详细信息
ISBN:
(纸本)9798350304817
Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data. While learning image GANs on Federated Learning (FL) and Multi-Discriminator (MD) systems has just been demonstrated, it is unknown if tabular GANs can be learned from decentralized data sources. Different from image GANs, state-of-the-art tabular GANs require prior knowledge on the data distribution of each (discrete and continuous) column to agree on a common encoding - risking privacy guarantees. In this paper, we propose GDTS, a distributed framework for GAN-based tabular synthesizer. GDTS provides different system architectures to match the two training paradigms termed GDTS_FL and GDTS_MD. Key to enable learning on distributed data is the proposed novel privacy-preserving multi-source feature encoding to capture the global data properties. In addition GDTS encompasses a weighting strategy based on table similarity to counter the detrimental effects of non-IID data and a validation pipeline to easily assess and compare the performance of different paradigms and hyper parameters. We evaluate the effectiveness of GDTS in terms of synthetic data quality, and overall training scalability. Experiments show that GDTS_FL achieves better statistical similarity and machine learning utility between generated and original data compared to GDTS_MD.
暂无评论