In this paper we propose a linear-time certifying algorithm for the single-source shortest-path problem capable of verifying graphs with positive, negative, and zero arc weights. Previously proposed linear-time approa...
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The exploitation of sustainable distributed energy sources is associated with the energy resilience and power optimisation of power grids. This study divides the energy sector of urban areas into isolated and non-isol...
The exploitation of sustainable distributed energy sources is associated with the energy resilience and power optimisation of power grids. This study divides the energy sector of urban areas into isolated and non-isolated topologies and attempts to review the application of microgrids within the two. In addition, it investigates methods to optimise power quality with the integration of multi-renewable generation to the system and discusses on the feasibility towards islanded operating microgrids. The proposed work is a result of a careful evaluation of the current literature on the topic. Consequently, the outcome of the given study is anticipated to facilitate future work on Microgrid implementation functioning in islanded mode.
Twister2 is an open-source big data hosting environment designed to process both batch and streaming data at scale. Twister2 runs jobs in both high-performance computing (HPC) and big data clusters. It provides a cros...
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the Ant Colony Optimization (ACO) algorithm is a famous metaheuristic technique that has been successfully applied in various optimization issues. It mimics the foraging conduct of ants to locate the shortest path amo...
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Since the global pandemic has significantly impacted human life, technology has become a vital role in various sectors. The more technology used, the more we need the electricity supply. The stability of the electrici...
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Large Language Models (LLMs) are being employed in various domains to support different tasks. There are many challenges when working with LLMs, such as hallucination and domain knowledge gaps. Retrieval Augmented Gen...
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In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to rel...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.
The culinary business industry, such as restaurants, heavily relies on consumer experience. One aspect that can affect consumer experience is the service provided by the restaurant in supplying complete dining equipme...
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ISBN:
(数字)9798350391992
ISBN:
(纸本)9798350392005
The culinary business industry, such as restaurants, heavily relies on consumer experience. One aspect that can affect consumer experience is the service provided by the restaurant in supplying complete dining equipment. Misunderstandings may occur due to the lack of complete dining and drinking utensils in restaurants, leading to complaints from customers or guests. However, manually checking the completeness of dining equipment can be time-consuming and can easily lead to human error. Therefore, it is necessary to develop a system that capable to detect the completeness of dining equipment on restaurant tables. To address this, this research uses cameras and machine learning to build a system that can automatically recognize dining equipment on the dining table. The machine learning model used in this research is YOLOv5. In YOLOv5, two processes occur such as training and testing. The training process is carried out on an annotated dataset to produce a trained model. Then this model is tested in the testing process to evaluate its performance using a separate dataset different from the training dataset. The model run on Google Colab showed an accuracy rate of 90% during training and 93.75% during testing.
The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of...
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ISBN:
(数字)9789819776030
ISBN:
(纸本)9789819776023
The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha
Pretrained models have taken full advantage of monolingual corpora and achieved impressive results in training Unsupervised Neural Machine Translation (UNMT) models. However, when adapting UNMT models with in-domain m...
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Pretrained models have taken full advantage of monolingual corpora and achieved impressive results in training Unsupervised Neural Machine Translation (UNMT) models. However, when adapting UNMT models with in-domain monolingual corpora for domain-specific translation tasks, one of the languages may lack in-domain corpora, resulting in the unequal amount and proportion of in-domain monolingual corpora in each language. This problem situation is known as Domain Mismatch (DM). This study investigates the impact of DM in UNMT. We find that DM causes a translation quality disparity. That is, while in-domain monolingual corpora of a language can enhance the in-domain translation quality into that particular language, this enhancement cannot be generalized to the other language, and the translation quality into the other language remains deficient. To address this problem, we propose Domain-Aware Adaptation (DAA), which can be embedded in the vanilla UNMT model training process. By passing sentence-level domain information to the model during training and inference, DAA gives higher weight to in-domain data from open-domain corpora related to specific domains to alleviate domain mismatch. The experimental results on German-English and Romanian-English translation tasks specified in the IT, Koran, medical, and TED2020 domains demonstrate that DAA can efficiently exploit open-domain corpora to mitigate the quality disparity of translation caused by DM.
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