Search Results (128)

As human–robot shared service environments become increasingly common, autonomous navigation in narrow space environments (NSEs), such as indoor corridors and crosswalks, becomes challenging. Mobile robots must go beyond reactive collision avoidance and interpret surrounding risks to proactively select safer routes in dynamic and spatially constrained environments. This study proposes a deep reinforcement learning (DRL)-based navigation framework that enables mobile robots to interact with pedestrians while identifying and traversing open and safe spaces. The framework fuses 3D LiDAR and RGB camera data to recognize individual pedestrians and estimate their position and velocity in real time. Based on this, a human-aware occupancy map (HAOM) is constructed, combining both static obstacles and dynamic risk zones, and used as the input state for DRL. To promote proactive and safe navigation behaviors, we design a state representation and reward structure that guide the robot toward less risky areas, overcoming the limitations of traditional approaches. The proposed method is validated through a series of simulation experiments, including straight, L-shaped, and cross-shaped layouts, designed to reflect typical narrow space environments. Various dynamic obstacle scenarios were incorporated during both training and evaluation. The results demonstrate that the proposed approach significantly improves navigation success rates and reduces collision incidents compared to conventional navigation planners across diverse NSE conditions. Full article

The Furuta pendulum is a well-known benchmark in the field of underactuated mechanical systems due to its reduced number of control inputs compared to its degrees of freedom, and richly nonlinear behavior. This work addresses the challenge of accurately modeling and controlling such a system without relying on traditional linearization techniques. In contrast to the common approach based on Lagrangian analytical modeling and state–space linearization, we propose a methodology that integrates a high-fidelity multibody model developed in Simscape Multibody (MATLAB), capturing the complete nonlinear dynamics of the system. The multibody model includes all geometric, inertial, and joint parameters of the physical hardware and interfaces directly with Simulink, enabling realistic simulation and control integration. To validate the physical fidelity of the multibody model, we perform a frequency-domain analysis of the pendulum’s natural free response. The dominant vibration frequency extracted from the simulation is compared with the theoretical prediction, demonstrating accurate capture of the system’s inertial and dynamic properties. This validation strategy strengthens the reliability of the model as a digital twin. The classical analytical formulation is provided to validate the simulation model and serve as a comparative framework. This dual modeling strategy allows for benchmarking control strategies against a trustworthy nonlinear digital twin of the Furuta pendulum. Preliminary experimental results using a physical prototype validate the feasibility of the proposed approach and set the foundation for future work in advanced nonlinear control design using the multibody representation as a digital validation tool. Full article

Street networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modeled as nodes and streets as edges between them. Previous work has shown that raster representations of the original data can be created through a learning algorithm on low-dimensional representations of the street networks. In contrast, models that capture high-level urban network metrics can be trained through convolutional neural networks. However, the detailed topological data is lost through the rasterization of the street network, and the models cannot recover this information from the image alone, failing to capture complex street network features. This paper proposes a model capable of inferring good representations directly from the street network. Specifically, we use a variational autoencoder with graph convolutional layers and a decoder that generates a probabilistic, fully connected graph to learn latent representations that encode both local network structure and the spatial distribution of nodes. We train the model on thousands of street network segments and use the learned representations to generate synthetic street configurations. Finally, we proposed a possible application to classify the urban morphology of different network segments, investigating their common characteristics in the learned space. Full article

Striking belief distortions may accompany various disorders of awareness that are predominantly associated with right hemispheric cerebral dysfunction. Distortions may range on a continuum of pathological severity, from the unawareness of paralysis in anosognosia for hemiplegia, to a more startling disturbance in denial of paralysis where belief may starkly conflict with reality. The patients’ beliefs about their limitations typically represent attempts to make sense of limitations or to impart meaning to incongruous facts. These beliefs are often couched in recollections from past memories or previous experience, and are hard to modify even given new information. Various explanations of unawareness have been suggested, including sensory, cognitive, monitoring and feedback operations, feedforward mechanisms, disconnection theories, and hemispheric asymmetry hypotheses, along with psychological denial, to account for the curious lack of awareness in anosognosia and other awareness disorders. This paper addresses these varying explanations of the puzzling beliefs regarding hemiparesis in anosognosia. Furthermore, using the multi-dimensional nature of unawareness in anosognosia as a model, some startling belief distortions in other right-hemisphere associated clinical syndromes are also explored. Other neurobehavioral disturbances, though perhaps less common, reflect marked psychopathological distortions. Startling disorders of belief are notable in somatic illusions, non-recognition or delusional misattribution of limb ownership (asomatognosia, somatoparaphrenia), or delusional identity (Capgras syndrome) and misidentification phenomena. Difficulty in updating beliefs as a source of unawareness in anosognosia and other awareness disorders has been proposed. Processes of belief development are considered to be patterns of thought, memories, and experience, which coalesce in a sense of the bodily and personal self. A common consequence of such disorders seems to be an altered representation of the self, self-parts, or the external world. Astonishing nonveridical beliefs about the body, about space, or about the self, continue to invite exploration and to stimulate fascination. Full article

Air quality management concerns the assessment, analysis and mitigation strategies associated with ensuring that air is breathable and non-toxic. Successful management is a cognitively intensive task, knowledge-focused and converges multiple sources of information to develop a shared understanding of a problem. To operate effectively in this space, managers and operational teams share common points of reference in discussing problems and solutions, strategies, tactical briefings, etc., and communication and technical language use are key to the discipline. However, few studies have homed in on the language communities of air quality management discourse, and fewer still have exploited this to gain insight into the cognitive processes underpinning salient operational knowledge production. This paper draws upon a discussion from a multi-stakeholder workshop on bioaerosols and the built environment and draws upon Cognitive Linguistics to systematically examine the cognitive structuring of those different stakeholder representations. This approach is then explored as a contribution to good practice in air quality knowledge management and communication that is consistent with studies on cognitive and learning science and has potential for policy formulation. Full article

Automated handwritten signature verification continues to pose significant challenges. A common approach for developing writer-independent signature verifiers involves the use of a dichotomizer, a function that generates a dissimilarity vector with the differences between similar and dissimilar pairs of signature descriptors as components. The Dichotomy Transform was applied within a Euclidean or vector space context, where vectored representations of handwritten signatures were embedded in and conformed to Euclidean geometry. Recent advances in computer vision indicate that image representations to the Riemannian Symmetric Positive Definite (SPD) manifolds outperform vector space representations. In offline signature verification, both writer-dependent and writer-independent systems have recently begun leveraging Riemannian frameworks in the space of SPD matrices, demonstrating notable success. This work introduces, for the first time in the signature verification literature, a Riemannian dichotomizer employing Riemannian dissimilarity vectors (RDVs). The proposed framework explores a number of local and global (or common pole) topologies, as well as simple serial and parallel fusion strategies for RDVs for constructing robust models. Experiments were conducted on five popular signature datasets of Western and Asian origin, using blind intra- and cross-lingual experimental protocols. The results indicate the discriminative capabilities of the proposed Riemannian dichotomizer framework, which can be compared to other state-of-the-art and computationally demanding architectures. Full article

Malware remains a central tool in cyberattacks, and systematic research into adversarial attack techniques targeting malware is crucial in advancing detection and defense systems that can evolve over time. Although numerous review articles already exist in this area, there is still a lack of comprehensive exploration into emerging artificial intelligence technologies such as reinforcement learning from the attacker’s perspective. To address this gap, we propose a foundational reinforcement learning (RL)-based framework for adversarial malware generation and develop a systematic evaluation methodology to dissect the internal mechanisms of generative models across multiple key dimensions, including action space design, state space representation, and reward function construction. Drawing from a comprehensive review and synthesis of the existing literature, we identify several core findings. (1) The scale of the action space directly affects the model training efficiency. Meanwhile, factors such as the action diversity, operation determinism, execution order, and modification ratio indirectly influence the quality of the generated adversarial samples. (2) Comprehensive and sensitive state feature representations can compensate for the information loss caused by binary feedback from real-world detection engines, thereby enhancing both the effectiveness and stability of attacks. (3) A multi-dimensional reward signal effectively mitigates the policy fragility associated with single-metric rewards, improving the agent’s adaptability in complex environments. (4) While the current RL frameworks applied to malware generation exhibit diverse architectures, they share a common core: the modeling of discrete action spaces and continuous state spaces. In addition, this work explores future research directions in the area of adversarial malware generation and outlines the open challenges and critical issues faced by defenders in responding to such threats. Our goal is to provide both a theoretical foundation and practical guidance for building more robust and adaptive security detection mechanisms. Full article

The increasing prevalence of microplastic (MP) pollution and the labor-intensive nature of existing identification methods necessitate improved large-scale detection approaches. Nile Red (NR) fluorescence, which varies with polarity, offers a potential classification method, but standardization of carrier solvents and fluorescence differentiation techniques remains lacking. This study evaluated eight NR-carrier solvents (n-hexane, chloroform, acetone, methanol, ethanol, acetone/hexane, acetone/ethanol, and acetone/water) across ten common MP polymers (HDPE, LDPE, PP, EPS, PS, PC, ABS, PVC, PET, and PA). Fluorescence intensity, Stokes shift, and solvent-induced polymer degradation were analyzed. The study also assessed HSV (Hue/Saturation/Value) color spaces for Stokes shift representation and MP differentiation. Fenton oxidation effectively quenched fluorescence in natural organic matter (e.g., eggshells, fingernails, wood, cotton) while preserving NR-stained MPs. Acetone/water [25% (v/v)] emerged as the optimal solvent, balancing fluorescence performance and minimal degradation. Full article

This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a linear quadratic regulator (LQR) acting as a guiding controller during training. Flexible-link mechanisms common in advanced robotics and aerospace systems exhibit oscillatory behavior that complicates precise control. To address this, the system is first identified using experimental input-output data from a Quanser^® virtual plant, generating an accurate state-space representation suitable for simulation-based policy learning. The hybrid control strategy enhances sample efficiency and accelerates convergence by incorporating LQR-generated trajectories during TD3 training. Internally, the TD3 agent benefits from architectural features such as twin critics, delayed policy updates, and target action smoothing, which collectively improve learning stability and reduce overestimation bias. Comparative results show that the guided TD3 controller achieves superior performance in terms of vibration damping, transient response, and robustness, when compared to conventional LQR, fuzzy logic, neural networks, and GA-LQR approaches. Although the controller was validated using a high-fidelity digital twin, it has not yet been deployed on the physical plant. Future work will focus on real-time implementation and structural robustness testing under parameter uncertainty. Overall, this research demonstrates that guided reinforcement learning can yield stable and interpretable policies that comply with classical control criteria, offering a scalable and generalizable framework for intelligent control of flexible mechanical systems. Full article

In realistic hyperspectral image (HSI) cross-scene classification tasks, it is ideal to obtain target domain samples during the training phase. Therefore, a model needs to be trained on one or more source domains (SD) and achieve robust domain generalization (DG) performance on an unknown target domain (TD). Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. However, recent studies have shown that the activation functions of neurons in a network exhibit asymmetry for different categories, which results in the learning of task-irrelevant features while attempting to learn task-related features (called “feature contamination”). For example, even if some intrinsic features of HSIs (lighting conditions, atmospheric environment, etc.) are irrelevant to the label, the neural network still tends to learn them, resulting in features that make the classification related to these spurious components. To alleviate this problem, this study replaces the common nonlinear generative network with a specific linear projection transformation, to reduce the number of neurons activated nonlinearly during training and alleviate the learning of contaminated features. Specifically, this study proposes a dimensionally decoupled spatial spectral linear extrapolation (SSLE) strategy to achieve sample augmentation. Inspired by the weakening effect of water vapor absorption and Rayleigh scattering on band reflectivity, we simulate a common spectral drift based on Markov random fields to achieve linear spectral augmentation. Further considering the common co-occurrence phenomenon of patch images in space, we design spatial weights combined with label determinism of the center pixel to construct linear spatial enhancement. Finally, to ensure the cognitive unity of the high-level features of the discriminator in the sample space, we use inter-class contrastive learning to align the back-end feature representation. Extensive experiments were conducted on four datasets, an ablation study showed the effectiveness of the proposed modules, and a comparative analysis with advanced DG algorithms showed the superiority of our model in the face of various spectral and category shifts. In particular, on the Houston18/Shanghai datasets, its overall accuracy was 0.51%/0.83% higher than the best results of the other methods, and its Kappa coefficient was 0.78%/2.07% higher, respectively. Full article

This paper investigates the existence of common attractors for generalized $\theta$-Hutchinson operators within the framework of partial metric spaces. Utilizing a finite iterated function system composed of $\theta$-contractive mappings, we establish theoretical results on common attractors, generalizing numerous existing results in the literature. Additionally, to enhance understanding, we present intuitive and easily comprehensible examples in one-, two-, and three-dimensional Euclidean spaces. These examples are accompanied by graphical representations of attractor images for various iterated function systems. As a practical application, we demonstrate how our findings contribute to solving a functional equation arising in a dynamical system, emphasizing the broader implications of the proposed approach. Full article

Visible–infrared person re-identification (VI-ReID) aims to minimize the modality gaps of pedestrian images across different modalities. Existing methods primarily focus on extracting cross-modality features from the spatial domain, which often limits the comprehensive extraction of useful information. Compared with conventional approaches that either focus on single-frequency components or employ simple multi-branch fusion strategies, our method fundamentally addresses the modality discrepancy through systematic frequency-space co-learning. To address this limitation, we propose a novel bi-frequency feature fusion network (BiFFN) that effectively extracts and fuses features from both high- and low-frequency domains and spatial domain features to reduce modality gaps. The network introduces a frequency-spatial enhancement (FSE) module to enhance feature representation across both domains. Additionally, the deep frequency mining (DFM) module optimizes cross-modality information utilization by leveraging distinct features of high- and low-frequency features. The cross-frequency fusion (CFF) module further aligns low-frequency features and fuses them with high-frequency features to generate middle features that incorporate critical information from each modality. To refine the distribution of identity features in the common space, we develop a unified modality center (UMC) loss, which promotes a more balanced inter-modality distribution while preserving discriminative identity information. Extensive experiments demonstrate that the proposed BiFFN achieves state-of-the-art performance in VI-ReID. Specifically, our method achieved a Rank-1 accuracy of 77.5% and an mAP of 75.9% on the SYSU-MM01 dataset under the all-search mode. Additionally, it achieved a Rank-1 accuracy of 58.5% and an mAP of 63.7% on the LLCM dataset under the IR-VIS mode. These improvements verify that our model, with the integration of feature fusion and the incorporation of frequency domains, significantly reduces modality gaps and outperforms previous methods. Full article

Recent national and international emergencies have repeatedly highlighted the role of information, and local information in particular, in synthesising various social and cultural policies proposed by public authorities and providing a correct representation of the living conditions of citizens on the ground, overcoming national media logics that are often based on the speed and spectacularisation of disasters. In fact, citizens have an “innate need” to know what is happening beyond their direct experience, to be aware of events that affect them or that are not happening in front of their eyes. A sociographic approach can be a supportive methodology to remember victims and report on disasters, but also to reconstruct new narratives by socially anticipating future environmental emergencies with the support of the media. Sociography as social narrative weaves together scientific analysis and journalistic storytelling, an old qualitative method that needs to be rediscovered, updated and integrated with new tools and methods. In this study, disaster narratives and analyses are supported by visual journalistic sources. In part, it takes up the gauntlet that Bruno Latour throws down to sociologists in Down to Earth, arguing that the latter should shift the focus of inquiry from theoretical analyses of social problems to descriptions of the existence of problems in experimental contexts, local shared spaces and common practices. This paper considers the description of (and within) the journalistic field as a methodological problem, examines the strengths and limitations of existing descriptive approaches and develops a different way of using a sociographic imagination in an attempt to make sense of changing journalistic practices with reference to specific Italian crisis events. Full article

The Top App Bar (TAB) seamlessly integrates essential elements such as app titles, navigation icons, action buttons, and search fields without creating visual clutter. However, designing a well-structured TAB presents a challenge, particularly for novice UI designers, due to the need to balance aesthetics, functionality, usability, and user experience within a limited space. This study introduces an auxiliary design method to address this challenge. It proposes the sequence representation learning technique to cluster TABs in software repositories based on their structure. A novice designer can input their preconceptualized structure to retrieve design examples from the software repository’s TAB clusters that have structures identical or similar to their concepts. Experimental results demonstrate the method’s effectiveness, achieving an accuracy of 66.7% and an F-1 score of 0.717, highlighting its alignment with human clustering. This method not only enhances the design efficiency of novice designers but also helps them understand successful design practices in various contexts. By avoiding common pitfalls and design errors, designers can adapt and innovate based on existing solutions. The dataset used in this study, containing approximately 4228 TABs, is available on Zenodo. Full article

Multimedia recommendation systems aim to accurately predict user preferences from multimodal data. However, existing methods may learn a recommendation model from spurious features, i.e., appearing to be related to an outcome but actually having no causal relationship with the outcome, leading to poor generalization ability. While previous approaches have adopted invariant learning to address this issue, they simply concatenate multimodal data without proper alignment, resulting in information loss or redundancy. To overcome these challenges, we propose a framework called M³-InvRL, designed to enhance recommendation system performance through common and modality-specific representation learning, invariant learning, and model merging. Specifically, our approach begins by learning modality-specific representations along with a common representation for each modality. To achieve this, we introduce a novel contrastive loss that aligns representations and imposes mutual information constraints to extract modality-specific features, thereby preventing generalization issues within the same representation space. Next, we generate invariant masks based on the identification of heterogeneous environments to learn invariant representations. Finally, we integrate both invariant-specific and shared invariant representations for each modality to train models and fuse them in the output space, reducing uncertainty and enhancing generalization performance. Experiments on real-world datasets demonstrate the effectiveness of our approach. Full article