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Numerous explanation methods have been recently developed to interpret the decisions made by deep neural network (DNN) models. For image classifiers, these methods typically provide an attribution score to each pixel in the image to quantify its ...

Audio deepfake detection is emerging as a crucial field in digital media, as distinguishing real audio from deepfakes becomes increasingly challenging due to the advancement of deepfake technologies. These methods threaten information authenticity and ...

Convolutional Neural Networks (CNNs) have been the state of the art in many applications, including computer vision and multimedia forensics. Translation invariance is often included among the reasons for their success. However, the recent literature has ...

Missing sensor data in human activity recognition is an active field of research that is being targeted with generative models for synthetic data generation. In contrast to most previous approaches, we aim to generate data of a sensor exclusively from ...

We present an end-to-end camera-only drone tracking approach in a multi-agent framework. We show implementation and simulation of such a system and test the tracking components utilizing a CNN-LSTM model for range estimation tested on real data. A video ...

The rise of social media has posed a challenging problem of effectively identifying rumors. With the great success of contrastive learning in many fields, many contrastive learning models for rumor detection have been proposed. However, existing models ...

Classifying architectural structures is an important and challenging task that requires expertise. Convolutional Neural Networks (CNN), which are a type of deep learning (DL) approach, have shown successful results in computer vision applications ...

Traditional drug discovery methods are both time-consuming and expensive. Utilizing artificial intelligence to predict drug-target binding affinity (DTA) has become an essential approach for accelerating new drug discovery. While many deep ...

Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation. In this paper, we propose a ...

Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in Ultra-Reliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously ...

Design Rule Checking (DRC) is one of the most important metrices in physical design procedure to evaluate quality of a detail route. The prediction of DRC violation (DRV) in the early stage can reduce the iterations of design procedure and improve the ...

Mapping a deep neural network (DNN) layer onto domain-specific accelerators can require an intractable number of choices regarding loop factorization, ordering, and spatial unrolling. Determining the optimal mapping that achieves the best figures in ...

Since the General Data Protection Regulation (GDPR) came into force in May 2018, companies have worked on their data practices to comply with the requirements of GDPR. In particular, since the privacy policy is the essential communication channel for ...

The accuracy of recent deep learning based clinical decision support systems is promising. However, lack of model interpretability remains an obstacle to widespread adoption of artificial intelligence in healthcare. Using sleep as a case study, we ...

This paper proposes a novel mask-based output layer for multi-level hierarchical classification, addressing the limitations of existing methods which (i) often do not embed the taxonomy structure being used, (ii) use a complex backbone neural network ...

Recent technology advancements have driven the production of plenoptic devices that capture and display visual contents, not only as texture information (as in 2D images) but also as 3D texture-geometric information. These devices represent the visual ...

Non-invasive physiological sensors allow for the collection of user-specific data in realistic environments. In this paper, using physiological data, we investigate the effectiveness of Convolutional Neural Network (CNN) based feature embeddings and ...

Deep neural networks (dnn) techniques for aspect-based sentiment classification have been widely studied. The success of these methods depends largely on training data which are often inadequate because of the rigor involved in ...

In order to extend ambient assisted living technologies for semi-autonomous people from smart homes to smart cities, it is necessary to recognize vulnerable people in the city. Gait-based approaches have been used to perform soft biometrics recognition,...

Training from scratch is the most common way to build a Convolutional Neural Network (CNN) based model. What if we can build new CNN models by reusing parts from previously built CNN models? What if we can improve a CNN model by replacing (possibly ...