Malicious image tampering refers to intentionally manipulating images to make them harmful to the owners or users. It has become one of the most severe challenges to image authenticity. Conventional methods for detecting tampering by identifying ...

The growing complexity of contemporary software systems, with multiple sub-modules, demands innovative modeling approaches, especially in addressing intricate failures and masked data generation. This paper proposed a stochastic differential ...

This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We highlight emerging challenges and prospective research directions, aiming to provide valuable resources for ...

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processing, data mining, and recommender systems. This surge in interest prompts us ...

Wireless earbuds have been gaining increasing popularity and using them to make phone calls or issue voice commands requires the earbud microphones to pick up human speech. When the speaker is in a noisy environment, speech quality degrades significantly ...

The act of website fingerprinting, which involves monitoring traffic features to infer private user information, has attracted much attention in the research community recently. While previous studies primarily focused on classifying fingerprint ...

With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. However, data heterogeneity, e.g., non-independently identically distributions ...

We present a comprehensive solution to learn and improve text-to-image models from human preference feedback. To begin with, we build ImageReward—the first general-purpose text-to-image human preference reward model—to effectively encode human ...

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather ...

• 70% constriction of trachea marks the onset of a precipitous decrease in airflow.
• The Myer-Cotton system can be interpreted in aerodynamics-derived description.
• Bioengineering-informed evaluation for assessment of respiratory ...

The quantitative relationship between tracheal anatomy and ventilation function can be analyzed by using engineering-derived methods, including mathematical modeling and numerical simulations. In order to provide ...

Network slicing is a key enabler of 5G and beyond networks to satisfy the diverse quality of service (QoS) requirements of different services simultaneously. In network slicing, radio access network (RAN) slicing is essential to establish a functional ...

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, which is closely relevant to anatomical structures including the left atrium (LA) and the left atrial appendage (LAA). Thus, a thorough understanding of the LA and LAA ...

In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-batch. Its key to success is the negative sharing strategy, in which every ...

Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver. However, only transmitting task-relevant ...

Today’s cyber attacks have become more severe and frequent, which calls for a new line of security defenses to protect against them. The dynamic nature of new-generation threats, which are evasive, resilient, and complex, makes traditional security ...

Graph-based recommendation systems are blossoming recently, which models user-item interactions as a user-item graph and utilizes graph neural networks (GNNs) to learn the embeddings for users and items. A fundamental challenge of graph-based ...

The growing number of complex and heterogeneous Internet of Things (IoT) applications has imposed a high demand for scarce communications and computing resources. To meet this stringent requirement, it is desirable to develop large-scale highly adaptive ...

Federated learning (FL), as a type of distributed machine learning, is vulnerable to external attacks during parameter transmissions between learning agents and a model aggregator. In particular, malicious participant clients in FL can purposefully craft ...

Blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the ...

Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning ...