Human movement is generally evaluated through both observations and clinical assessment scales to identify the state and deterioration of a patient’s motor control. Lately, technological systems for human motion analysis have been used in clini...
Digitization is becoming more and more important in the medical sector. Through electronic health records and the growing amount of digital data of patients available, big data research finds an increasing amount of use cases. The rising amount of da...
Privacy risk mining, a crucial domain in data privacy protection, endeavors to uncover potential information among datasets that could be linked to individuals’ sensitive data. Existing anonymization and privacy assessment techniques either lac...
As the upsurge of information and communication technologies has become the foundation of all modern application domains, fueled by the unprecedented amount of data being processed and exchanged, besides security concerns, there are also pressing pri...
An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose th...
Assessing privacy within data-driven software is challenging due to its subjective nature and the diverse array of privacy-enhancing technologies. A simplistic personal/non-personal data classification fails to capture the nuances of data specificati...
Drawing on digital identity theories, social software engineering theory (SSE), and the Privacy Safeguard (PriS) methodology, we examined the way that personal information uploaded on social media (SM) imposes privacy issues. Throughout a review on u...
In V2G service operations, highly efficient data sharing among participants is useful in grid load balancing and renewable energy integration. However, the data quality and sharing efficiency greatly rely on entities’ willingness to share. More...
Advances of information and communication technologies in medical areas have led to the emergence of wireless body area network (WBAN). The high accessibility of media in WBAN can easily lead to the malicious tapping or tampering attacks, which may s...
The decarbonization of electricity generation by 2030 and the realization of a net-zero economy by 2050 are central to the United States’ climate strategy. However, large-scale renewable integration introduces operational challenges, including...
Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are...
In this work, we introduce a framework to combine arbitrary image segmentation algorithms from different agents under data privacy constraints to produce an aggregated prediction set satisfying finite-sample risk control guarantees. We leverage distr...
Digitization offers great opportunities as well as new challenges. Indeed, these opportunities entail increased cyber risks, both from deliberate cyberattacks and from incidents caused by inadvertent human error. Cyber risk must be mastered, and to t...
In vertical federated learning (FL), the features of a data sample are distributed across multiple agents. As such, inter-agent collaboration can be beneficial not only during the learning phase, as is the case for standard horizontal FL, but also du...
In the development process of the power industry, accurately assessing the level of development of the electricity business environment is of great significance. However, traditional evaluation systems have limitations, with the issue of “data...
The adoption of artificial intelligence (AI) in healthcare requires not only accurate predictions but also a clear understanding of its reliability. In safety-critical domains such as medical imaging and diagnosis, clinicians must assess the confiden...
In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC)...
Past research on the tiny house movement has primarily focused on understanding the individual motivations behind adopting the tiny house lifestyle. While some studies have suggested that tiny housers do entertain an interest in community, no systema...
We investigated patterns formed during the polymerization process of bifunctional monomers in a liquid crystal for both large polymer concentrations (polymer-dispersed liquid crystals, PDLC) and small concentrations (polymer-stabilized liquid crystal...
The social potential of Urban Air Mobility (UAM) as a greener and faster transportation system in and around urban environments is indisputable. Nevertheless, the success of UAM introduction and its wide use will strongly depend on acceptance by the...
With the rapid development of energy transformation, the proportion of new energy is increasing, and the efficient trading mechanism of multi-microgrids can realize energy sharing to improve the consumption rate of new energy. A distributed multi-mic...
Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neu...
Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, perfor...
The rising global burden of chronic diseases highlights the limitations of traditional dietary guidelines. Precision Nutrition (PN) aims to deliver personalized dietary advice to optimize individual health, and the effective implementation of PN fund...
Sensor networks generate high-dimensional temporally dependent data across healthcare, environmental monitoring, and energy management, which demands robust machine learning for reliable forecasting. While gradient boosting methods have emerged as po...
Background: Whole-slide imaging and algorithmic advances have moved computational pathology from research to routine consideration. Despite notable successes, real-world deployment remains limited by generalization, validation gaps, and human-factor...
Joint degeneration is a major global health issue requiring improved diagnostic and prognostic tools. This review examines whether integrating Bayesian graphical models with multiscale medical imaging can enhance detection, analysis, and prediction o...
AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However,...
Background/Objectives: Autoimmune rheumatic diseases (AIRDs) are complex, heterogeneous, and relapsing–remitting conditions in which early diagnosis, flare prediction, and individualized therapy remain major unmet needs. This review aims to syn...
Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and...