In this work, we expand the discussion of bias in Automatic Speech Recognition (ASR) through a large-scale audit. Using a large and global data set of speech, we perform an audit of some of the most popular English AS...
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Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such ...
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This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. Firs...
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Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage,...
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Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other.
Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk o...
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A prominent use case of consumer electronics-based Internet of Things (IoT) applications, focused on smart cities, is connected devices that enable cities to optimize their operations via access to high volumes of sen...
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A prominent use case of consumer electronics-based Internet of Things (IoT) applications, focused on smart cities, is connected devices that enable cities to optimize their operations via access to high volumes of sensitive data. Yet, these devices commonly utilize public channels for data access and sharing, requiring consistent communication protocols and an Intrusion Detection System (IDS) with the aid of AI. However, most of them involve high computation and communication costs. They are not fully reliable, either. Also, AI-based IDS solutions are viewed as black boxes because they cannot justify their decisions. To resolve these issues, we have proposed a framework based on explainable artificial intelligence (XAI) for securing consumer IoT applications in smart cities. At the beginning of the protocol execution, the participants exchange authenticated data through the blockchain-based AKA procedure. Meanwhile, we adopt the Python-based Shapley Additive Explanation (SHAP) framework to explain and interpret the core features guiding decision-making. The working model of this framework depicts its validation with recent benchmark methods. IEEE
In the rapidly evolving field of natural language processing (NLP), enhancing model performance in understanding and generating human language has become increasingly critical. As the need for better language models i...
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ISBN:
(数字)9798350376425
ISBN:
(纸本)9798350376432
In the rapidly evolving field of natural language processing (NLP), enhancing model performance in understanding and generating human language has become increasingly critical. As the need for better language models increases, enhancing the current systems’ shortcomings becomes crucial. There are difficulties in existing models precisely due to the fundamental problem of striking a balance between the level of detail achievable and the amount of computational resources that a model requires for accurate estimation in practical settings. Current models, present several problems including but not limited to overfitting, average performance on out-of-sample data and less applicability for subtle linguistic features. Such obstacles limit their capability to execute in real conditions, where high degree of accuracy and precision is paramount. To overcome these challenges, this study introduces a novel framework that employs the BERT model which was developed to capture bidirectional context and achieve greater depth of the semantic relations between words. The proposed approach is designed to improve the model’s effectiveness to a considerable extent through the use of mechanisms such as attention as well as transformer architecture included in BERT. The proposed BERT model demonstrates exceptional results, achieving an accuracy of 99.10% and a precision of 98.30%. These metrics reflect the model's superior ability to understand and generate accurate language representations, surpassing existing models in both effectiveness and efficiency. This advancement underscores the potential of BERT to address critical challenges in NLP, offering a promising solution for applications requiring high precision and robust language comprehension.
In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In part...
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Gestational Diabetes Mellitus (GDM) is a rising concern worldwide, particularly in low-resource countries such as Bangladesh, where access to healthcare facilities is limited and awareness of GDM management is inadequ...
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Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data,...
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