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As training a high-performance deep neural network (DNN) model requires a large amount of data, powerful computing resources and expert knowledge, protecting well-trained DNN models from intellectual property (IP) infringement has raised serious concerns ...

Recent studies have proposed quantitative ultrasound (QUS) to extract the acoustic properties of tissues from pulse-echo data obtained through multiple transmissions. In this paper, we introduce a learning-based approach to identify thyroid nodule ...

As Deep Neural Networks (DNNs) evolve in complexity, so does their parameter size, resulting in prolonged training time. While various distributed training strategies have been proposed to speed up training, the efficiency of these strategies is ...

Recently, several approaches have been proposed for decomposing deep neural network (DNN) classifiers into binary classifier modules to facilitate modular development and repair of such models. These approaches concern only the problem of decomposing ...

Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses on a digital pen equipped with kinematic sensors, allowing users to write on any surface while ...

This paper conducts a corresponding study on the construction of a translation talent pool internally through the context of information retrieval technology. It first discusses the current state of development of information retrieval technology in ...

As training Deep Neural Network (DNN) is time-consuming, people resort to parallelization across multiple accelerators. A plethora of solutions adopt data/model parallelization, but they suffer from frequent weight synchronization overhead or resource ...

The rapid advancement of Artificial Intelligence (AI) necessitates significant enhancements in the energy efficiency of Graphics Processing Units (GPUs) for Deep Neural Network (DNN) workloads. Such a challenge is particularly critical for embedded GPUs,...

Various security threats are faced by the Internet of Things (IoT) as it enriches people’s daily lives. Intrusion detection is employed as an effective method to mitigate these threats, encompassing Botnet, DDoS, and Scan attacks. Due to the rapid ...

Dual-Mode Vehicles (DMVs) represent a new and sustainable solution to the problems of urban mobility, and energy consumption. This proposed work of a DMV can operate without a conventional battery. Instead, it uses a combination of energy management ...

Machine Learning and Deep Learning based Network Intrusion Detection Systems (NIDS) serve as the backbone to protect computer networks against various cyber security threats. However, their susceptibility to adversarial attacks is the biggest ...

The importance of extracting constraints from data is emphasized by its potential practical applications in solving real-world problems. While constraints are commonly used for modeling and problem-solving, methods for learning constraints from ...

A methodology for Human Activity Recognition using Deep Neural Networks (DNNs) is presented in this paper. The proposed approach introduces a model-driven input adaptation technique that involves the conversion of binary sensor data collected from ...

With the increasing availability of data and the development of powerful algorithms, deep neural networks have become an essential tool for all sectors. However, it can be challenging to automate the process of building and tuning them, due to the ...

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the ...

Contextual Importance and Utility (CIU) is a model-agnostic method for explaining outcomes of AI systems. CIU has succeeded in producing meaningful explanations where state-of-the-art methods fail, e.g. for detecting bleeding in ...

The enhancement of e-commerce conversion rates heavily relies on personalized product recommendations generated by recommendation systems (RS). Despite successful techniques, challenges like sparse data and cold-start issues hinder their effectiveness. ...

Emergency Department overcrowding is a well-known problem. The consequences are long waiting times for patients, reduced service quality, and the potential for increased mortality rates. Moreover, this problem is escalating with the aging of the ...

• Using Attention model for forecasting Emergency Department (ED) patient admissions.
• Comparing Attention model with LSTM-based networks for ED patients admissions.
• Exogenous variables: weather, calendar, air quality, allergens and ...

Machine Learning (ML) algorithms, specifically Artificial Neural Networks (ANNs), have proved their effectiveness in solving complex problems in many different applications and multiple fields. This paper focuses on optimizing the activation ...

This study presents the results of two perception experiments aimed at evaluating the effect that specific patterns of disfluencies have on people listening to synthetic speech. We consider the particular case of Cultural Heritage presentations ...

• Neural speech synthesis systems model speech phenomena in a natural-sounding way.
• Neural synthesis can work as a tool to investigate human speech behaviours.
• In specific contexts, speech disfluency phenomena foster listeners’ ...