Search Results (88)

Object detection and action/gesture recognition have become imperative in security and surveillance fields, finding extensive applications in everyday life. Advancement in such technologies will help in furthering cybersecurity and extended reality systems through the accurate identification of users and their interactions, which plays a pivotal role in the security management of an entity and providing an immersive experience. Essentially, it enables the identification of human–object interaction to track actions and behaviors along with user identification. Yet, it is performed by traditional camera-based methods with high difficulties and challenges since occlusion, different camera viewpoints, and background noise lead to significant appearance variation. Deep learning techniques also demand large and labeled datasets and a large amount of computational power. In this paper, a novel approach to the recognition of human–object interactions and the identification of interacting users is proposed, based on three-dimensional hand pose data from an egocentric camera view. A multistage approach that integrates object detection with interaction recognition and user identification using the data from hand joints and vertices is proposed. Our approach uses a statistical attribute-based model for feature extraction and representation. The proposed technique is tested on the HOI4D dataset using the XGBoost classifier, achieving an average F1-score of 81% for human–object interaction and an average F1-score of 80% for user identification, hence proving to be effective. This technique is mostly targeted for extended reality systems, as proper interaction recognition and users identification are the keys to keeping systems secure and personalized. Its relevance extends into cybersecurity, augmented reality, virtual reality, and human–robot interactions, offering a potent solution for security enhancement along with enhancing interactivity in such systems. Full article

Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information about the texture and the shape of objects. In this paper, we enhance X3D 3D graphics files with haptic features to create 3D objects with haptic feedback. We propose haptic attributes such as static and dynamic friction, stiffness, and maximum altitude that provide the optimal user experience in a virtual haptic environment. After numerous optimization attempts on the haptic textures, we propose various haptic geometrical textures for creating a virtual 3D haptic environment for the tactile Internet. These tangible geometrical textures can be attached to any geometric shape, enhancing the haptic sense. We conducted a study of user interaction with a virtual environment consisting of 3D objects enhanced with haptic textures to evaluate performance and user experience. The goal is to evaluate the realism and recognition accuracy of each generated texture. The findings of the study aid visually impaired individuals to better understand their physical environment, using haptic devices in conjunction with the enhanced haptic textures. Full article

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall short in the face of increasingly sophisticated fake content. This review article explores the emerging role of Large Language Models (LLMs) in enhancing the detection of fake news and fake profiles. We provide a comprehensive overview of the nature and spread of disinformation, followed by an examination of existing detection methodologies. The article delves into the capabilities of LLMs in generating both fake news and fake profiles, highlighting their dual role as both a tool for disinformation and a powerful means of detection. We discuss the various applications of LLMs in text classification, fact-checking, verification, and contextual analysis, demonstrating how these models surpass traditional methods in accuracy and efficiency. Additionally, the article covers LLM-based detection of fake profiles through profile attribute analysis, network analysis, and behavior pattern recognition. Through comparative analysis, we showcase the advantages of LLMs over conventional techniques and present case studies that illustrate practical applications. Despite their potential, LLMs face challenges such as computational demands and ethical concerns, which we discuss in more detail. The review concludes with future directions for research and development in LLM-based fake news and fake profile detection, underscoring the importance of continued innovation to safeguard the authenticity of online information. Full article

Wi-Fi channel state information (CSI)-based human action recognition systems have garnered significant interest for their non-intrusive monitoring capabilities. However, the integrity of these systems can be compromised by data leakage, particularly when improper dataset partitioning strategies are employed. This paper investigates the presence and impact of data leakage in three published Wi-Fi CSI-based human action recognition methods that utilize deep learning techniques. The original studies achieve precision rates of 95% or higher, attributed to the lack of human-based dataset splitting. By re-evaluating these systems with proper subject-based partitioning, our analysis reveals a substantial decline in performance, underscoring the prevalence of data leakage. This study highlights the critical need for rigorous dataset management and evaluation protocols to ensure the development of robust and reliable human action recognition systems. Our findings advocate for standardized practices in dataset partitioning to mitigate data leakage and enhance the generalizability of Wi-Fi CSI-based models. Full article

The functional structure of territorial space is an important factor for analyzing the interaction between humans and nature. However, the classification of remote sensing images struggles to distinguish between multiple functions provided by the same land use type. Therefore, we propose a framework to combine multi-source data for the recognition of dominant functions at the block level. Taking the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) as a case study, its block-level ‘production–living–ecology’ functions were interpreted. The whole GBA was first divided into different blocks and its total, average, and proportional functional intensities were then calculated. Each block was labeled as a functional type considering the attributes of human activity and social information. The results show that the combination of land use/cover data, point of interest identification, and open street maps can efficiently separate the multiple and mixed functions of the same land use types. There is a great difference in the dominant functions of the cities in the GBA, and the spatial heterogeneity of their mixed functions is closely related to the development of their land resources and socio-economy. This provides a new perspective for recognizing the spatial structure of territorial space and can give important data for regulating and optimizing landscape patterns during sustainable development. Full article

This article presents a thematic analysis of submissions made on New Zealand’s Emergency Management Bill. While a key focus is on the importance and frequency of animal welfare concerns raised by submitters, the analysis also examines other critical themes to provide context on the range of issues addressed. The impact of the “No Animal Left Behind” campaign launched by Animal Evac New Zealand in mobilising public engagement on animal welfare provisions is also assessed. Sixty-one percent (n = 191) of public submissions on the Bill raised the importance of including animals in new emergency management legislation and at least 48% (n = 149) of all public submissions were directly attributed to the campaign. Key animal welfare concerns include the need for clear statutory powers and requirements, better coordination and resourcing, and recognition of the human–animal bond. Other prominent non-animal-related themes relate to strengthening community resilience, improving Māori participation in emergency management, and enhancing readiness and response capabilities. Specific recommendations are made for legal changes to better protect animal welfare, including amending key provisions to explicitly address animal rescue and evacuation, mandating animal welfare emergency plans, strengthening animal seizure and disposal processes, and enhancing accountability for animal emergency response charities. With improving animal disaster management law being the most common issue identified, it is logical for a government to apply deliberative democracy to ensure animals are better protected in New Zealand emergency management reforms. The findings underscore the importance of comprehensive, multi-faceted reform to create a world-leading emergency management framework. Full article

Xie Fuya (N. Z. Zia, 1892–1991), a major Chinese Christian thinker, has contributed much to the development of Sino-theology. However, his work has yet to receive the recognition it deserves. As a thinker who is well-versed in both Chinese and Western philosophies while dedicating himself to the exploration of the philosophy of religion, Xie presents a dual feature in his writings. On the one hand, his work engages in a dialogical discourse between Eastern and Western philosophies. On the other hand, his writings represent an ambitious attempt to interpret traditional Chinese philosophical tenets within the context of Christian theology, transverse from the level of human nature to the level of ontological existence, representing an innovative model of contemplation in the field of Sino-theology. This contribution is of immense value to the development of Chinese philosophical thought. For this reason, this article attempts to illustrate, through Xie’s writings in various stages of his life, his relentless effort to promote the integration of Eastern and Western philosophies within the framework of Chinese thought. His most notable accomplishment in this East–West confluence effort is his unique assumption of God’s attributions as both zhonghe (literally “middle harmony”, connoted as interdependency by Xie) and bianyi (change). Full article

The pathogenesis of viral infection is attributed to two folds: intrinsic cell death pathway activation due to the viral cytopathic effect, and immune-mediated extrinsic cellular injuries. The immune system, encompassing both innate and adaptive immunity, therefore acts as a double-edged sword in viral infection. Insufficient potency permits pathogens to establish lifelong persistent infection and its consequences, while excessive activation leads to organ damage beyond its mission to control viral pathogens. The innate immune response serves as the front line of defense against viral infection, which is triggered through the recognition of viral products, referred to as pathogen-associated molecular patterns (PAMPs), by host cell pattern recognition receptors (PRRs). The PRRs–PAMPs interaction results in the induction of interferon-stimulated genes (ISGs) in infected cells, as well as the secretion of interferons (IFNs), to establish a tissue-wide antiviral state in an autocrine and paracrine manner. Cumulative evidence suggests significant variability in the expression patterns of PRRs, the induction potency of ISGs and IFNs, and the IFN response across different cell types and species. Hence, in our understanding of viral hepatitis pathogenesis, insights gained through hepatoma cell lines or murine-based experimental systems are uncertain in precisely recapitulating the innate antiviral response of genuine human hepatocytes. Accordingly, this review article aims to extract and summarize evidence made possible with bona fide human hepatocytes-based study tools, along with their clinical relevance and implications, as well as to identify the remaining gaps in knowledge for future investigations. Full article

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms’ fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment. Full article

Automatic speech recognition (ASR) has been widely used to realize daily human–machine interactions. Face masks have become everyday wear in our post-pandemic life, and speech through masks may have impaired the ASR. This study explored the effects of different kinds of face masks (e.g., surgical mask, KN95 mask, and cloth mask) on the Mandarin word accuracy of two ASR systems with or without noises. A mouth simulator was used to play speech audio with or without wearing a mask. Acoustic signals were recorded at distances of 0.2 m and 0.6 m. Recordings were mixed with two noises at a signal-to-noise ratio of +3 dB: restaurant noise and speech-shaped noise. Results showed that masks did not affect ASR accuracy without noise. Under noises, masks did not significantly influence ASR accuracy at 0.2 m but had significant effects at 0.6 m. The activated-carbon mask had the most significant impact on ASR accuracy at 0.6 m, reducing the accuracy by 18.5 percentage points compared to that without a mask, whereas the cloth mask had the least effect on ASR accuracy at 0.6 m, reducing the accuracy by 0.9 percentage points. The acoustic attenuation of masks on the high-frequency band at around 3.15 kHz of the speech signal attributed to the effects of masks on ASR accuracy. When training ASR models, it may be important to consider mask robustness. Full article

Glycerol-3-phosphate acyltransferase (GPAT) catalyzes the first step in triacylglycerol synthesis. Understanding its substrate recognition mechanism may help to design drugs to regulate the production of glycerol lipids in cells. In this work, we investigate how the native substrate, glycerol-3-phosphate (G3P), and palmitoyl-coenzyme A (CoA) bind to the human GPAT isoform GPAT4 via molecular dynamics simulations (MD). As no experimentally resolved GPAT4 structure is available, the AlphaFold model is employed to construct the GPAT4–substrate complex model. Using another isoform, GPAT1, we demonstrate that once the ligand binding is properly addressed, the AlphaFold complex model can deliver similar results to the experimentally resolved structure in MD simulations. Following the validated protocol of complex construction, we perform MD simulations using the GPAT4–substrate complex. Our simulations reveal that R427 is an important residue in recognizing G3P via a stable salt bridge, but its motion can bring the ligand to different binding hotspots on GPAT4. Such high flexibility can be attributed to the flexible region that exists only on GPAT4 and not on GPAT1. Our study reveals the substrate recognition mechanism of GPAT4 and hence paves the way towards designing GPAT4 inhibitors. Full article

The rapid expansion of e-commerce, particularly in the clothing sector, has led to a significant demand for an effective clothing industry. This study presents a novel two-stage image recognition method. Our approach distinctively combines human keypoint detection, object detection, and classification methods into a two-stage structure. Initially, we utilize open-source libraries, namely OpenPose and Dlib, for accurate human keypoint detection, followed by a custom cropping logic for extracting body part boxes. In the second stage, we employ a blend of Harris Corner, Canny Edge, and skin pixel detection integrated with VGG16 and support vector machine (SVM) models. This configuration allows the bounding boxes to identify ten unique attributes, encompassing facial features and detailed aspects of clothing. Conclusively, the experiment yielded an overall recognition accuracy of 81.4% for tops and 85.72% for bottoms, highlighting the efficacy of the applied methodologies in garment categorization. Full article

Human skin, the body’s largest organ, plays a crucial role in perceiving mechanical stimulation and facilitating interaction with the external environment. Leveraging the unique attributes of human skin, electronic skin technology aimed at replicating and surpassing the capabilities of natural skin holds significant promise across various domains, including medical care, motion tracking, and intelligent robotics. In recent research, triboelectric nanogenerators have emerged as a compelling solution for addressing the energy challenge in electronic skins. Triboelectric nanogenerators harness the combination of the triboelectric effect and electrostatic induction to efficiently convert mechanical energy into electrical power, serving as self-powered sensors for electronic skins, which possess the advantages of self-powered operation, cost-effectiveness, and compatibility with a wide range of materials. This review provides an introduction to the working principles and the four operational modes of triboelectric nanogenerators, highlighting the functional features of electronic skins, such as stretchability, self-healing, and degradability. The primary focus is on the current applications of self-powered electronic skins based on triboelectric nanogenerators in medical care, motion tracking, and machine tactile recognition. This review concludes by discussing the anticipated challenges in the future development of self-powered electronic skins based on triboelectric nanogenerators. This review holds practical significance for advancing the practical use of self-powered electronic skins based on triboelectric nanogenerators and offers valuable guidance for individuals interested in pursuing scientific and healthy endeavors. Full article

Gonorrhea, a sexually transmitted disease caused by Neisseria gonorrhoeae, poses a significant global public health threat. Infection in women can be asymptomatic and may result in severe reproductive complications. Escalating antibiotic resistance underscores the need for an effective vaccine. Approaches being explored include subunit vaccines and outer membrane vesicles (OMVs), but an ideal candidate remains elusive. Meningococcal OMV-based vaccines have been associated with reduced rates of gonorrhea in retrospective epidemiologic studies, and with accelerated gonococcal clearance in mouse vaginal colonization models. Cross-protection is attributed to shared antigens and possibly cross-reactive, bactericidal antibodies. Using a Candidate Antigen Selection Strategy (CASS) based on the gonococcal transcriptome during human mucosal infection, we identified new potential vaccine targets that, when used to immunize mice, induced the production of antibodies with bactericidal activity against N. gonorrhoeae strains. The current study determined antigen recognition by human sera from N. gonorrhoeae-infected subjects, evaluated their potential as a multi-antigen (combination) vaccine in mice and examined the impact of different adjuvants (Alum or Alum+MPLA) on functional antibody responses to N. gonorrhoeae. Our results indicated that a stronger Th1 immune response component induced by Alum+MPLA led to antibodies with improved bactericidal activity. In conclusion, a combination of CASS-derived antigens may be promising for developing effective gonococcal vaccines. Full article

The genus Aurelia is one of the most extensively studied within the class Scyphozoa. However, much of the research was historically attributed to the species Aurelia aurita (Linnaeus, 1758) before the recognition of its taxonomic complexity. Initially considered cosmopolitan and globally distributed, recent phylogenetic analysis has challenged this assumption. Consequently, the current distribution of species within the genus Aurelia and the processes that led to this distribution remain largely unexplored. After genetically confirming that the species traditionally present in the Mar Menor coastal lagoon in the southwestern Mediterranean corresponds to A. solida, we compiled data on the locations where moon jellyfish species have been genetically identified and mapped these coordinates to the geological period when the genus Aurelia diverged from other scyphozoan genera. We propose two hypotheses to explain the disjunct distribution of certain species. The first one assumes recent human-mediated introductions, while the second posits an absence of introductions. Both hypotheses, supported by fossil and historical records, suggest a Paleo-Tethys origin of the genus Aurelia. Migration from this area explains most of the genus’s current distribution without human intervention, being the Mediterranean Sea, where A. solida should be considered autochthonous, part of their natural distribution range. Full article