Search Results (139)

As content creators increasingly assume roles once associated with journalism, marketing, and entrepreneurship, generative AI is reshaping the conditions under which professional value is produced and recognized. Drawing on ten semi-structured interviews with South Korean content creators and influencer-marketing professionals (185–210 min each, conducted between October 2025 and February 2026), this study examines how creators reconstruct professional value across news production, metric-driven brand partnerships, and AI-assisted content work. Reflexive thematic analysis identifies four themes: market-mediated journalistic norms, metric recalibration from reach to conversion, algorithmic expertise as craft knowledge, and generative AI as a labor environment with a negotiated delegation boundary. Participants delegated routine tasks, including drafting and thumbnail selection, to AI while preserving editorial judgment, planning ability, experiential knowledge, and authentic persona as distinctly human forms of expertise. The study introduces Professional Value Recalibration (PVR) as a conceptual framework for understanding how professional worth is redistributed under algorithmic and AI-mediated conditions. It further shows that these negotiations vary by creators’ scale and market position. The findings contribute to scholarship on platform labor, entrepreneurial journalism, and human–machine communication by showing that AI adoption is not only technological adaptation but also a process through which creator professionalism is renegotiated and, in some respects, reinforced. Full article

This article examines a group of Torah arks made for rural Jewish communities in German lands during the seventeenth and early eighteenth centuries. Scholarship on Ashkenazi synagogue furnishings has largely focused on monumental Torah arks associated with larger and wealthier communities. The examples discussed here reveal a different tradition, one that emerged under distinct social and material conditions and served smaller rural congregations. Drawing on an analysis of construction techniques, materials, and design, the article demonstrates that these arks were rooted in regional woodworking practices shared with contemporary furniture, cabinetry, and church furnishings. Produced in local workshops, likely by Christian craftsmen, they adapted familiar carpentry forms to Jewish liturgical requirements. Visible structural compromises, improvised solutions, and simplified construction techniques preserve traces of this process. Rather than evaluating these arks primarily against monumental urban examples, this study situates them within the regional material and craft environments in which they were produced. Their forms reflect a localized process of sacralization through which ordinary woodworking traditions were transformed into ritual furnishings and focal points of communal worship. By highlighting this group of objects, whose carpentry, construction, and relationship to regional furniture traditions have not been systematically examined, the article expands current scholarship on Torah ark design and demonstrates the diversity of material solutions developed by Jewish communities in early modern German lands. Full article

Traditional handicraft knowledge is not only embodied in final products and craft procedures but also embedded in the context formed by production spaces, tools, materials, practitioners, and their interrelationships. However, existing digital preservation practices often focus on individual objects or specific data types, making the spatial and semantic relationships among heterogeneous resources insufficiently represented and limiting public understanding of the broader context of craft practices. To address this issue, this paper proposes a digital resource organization and multi-terminal presentation framework. Using 3D point clouds as a unified spatial reference for the site, the framework links images, videos, interviews, craft records, and object-related materials to spatial locations through structured annotation, and visualizes relationships among practitioners, tools, materials, processes, and spaces through node-link representations. The Web-based viewer and CAVE immersive system access the same content dataset, enabling “input once, reuse across terminals”. User feedback suggests that the framework supports the integrated representation of spatial context, craft resources, and associated information within traditional handicraft sites, with relational visualization contributing to a more holistic understanding of craft practices. The framework provides a reusable workflow for organizing, linking, and presenting heterogeneous heritage resources in traditional handicraft transmission sites, offering digital support for a contextual understanding of craft practices. Full article

As artificial intelligence (AI) technologies advance, traditional craftsmen face new challenges: innovating using digital tools while preserving cultural authenticity and heritage knowledge. The “hybrid artisan,” who strategically integrates AI-based design tools with traditional craft, emerges as a response to this tension. This article addresses research questions regarding how integrating generative AI technologies into design processes influences: (1) artisans’ productivity and product quality; (2) cultural authenticity and heritage preservation; (3) sustainable business models in creative entrepreneurship. The research methodology employs a convergent design with mixed methods, combining: (a) a systematic literature review (SLR) guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020, n = 33 articles, 2022–2025); and (b) a qualitative survey (n = 13 artisans, Romania; semi-structured questionnaire, 34 items). The literature review identifies three dominant human–AI collaboration models: task-level cooperation, process-level coordination, and system-level co-creation. Diffusion models fine-tuned with low-rank adaptation (LoRA) and generative adversarial networks (GANs) achieve cultural authenticity scores of 73–95% while reducing design time by 30–70%. Empirical data reveal paradoxes: artisans value authentic creativity and sustainability (4 of 13 respondents (31%) rate sustainability as “extremely important”) but adopt AI cautiously (6 of 13 respondents (46%) report that they were not familiar with AI tools). Those using AI report 15–40% productivity gains without a proportional increase in sales, suggesting that market recognition of AI-assisted crafts remains uneven and that sustainability benefits are not yet clearly linked to AI use in practice. The successful “hybrid artisan” model relies on collaborative rather than autonomous AI positioning, explicit cultural safeguards in system design, and transparent communication with consumers about AI involvement. This research provides a conceptual heuristic, points to new research directions, and outlines policy implications for understanding when and how AI-assisted craft practices may support cultural integrity while also accepting that such benefits are context-dependent and not universally validated. Full article

By integrating cultural genes with extensible semantic concepts, this study explores the application forms and design methodologies of the material manifestations and underlying essence of Lu embroidery’s intangible cultural heritage within contemporary social contexts, thereby promoting outstanding national culture by selecting Lu embroidery, a representative ICH of the Qi–Lu region, and employing the dual-drill model to conduct genetic extraction and classification of its elemental characteristics. It provides a foundation for constructing a scalable element set. Using the Kegel semantic analysis method, we extract the fundamental model of Lu embroidery’s cultural genes and construct a set of Kegel-based visual design elements representing explicit cultural genes. Through graphical semantic analysis, we further develop a set of Kegel-based imagery elements corresponding to implicit genes, thereby facilitating the expression and design innovation of these cultural genes. Taking lamp design as an example, this study reconstructs and visualizes both explicit and implicit cultural elements to validate the feasibility and rationality of the research methodology. The semantic design-based translation that integrates the tangible manifestations and value essence of Shandong embroidery culture represents an innovative approach to digital preservation of this traditional craft, while also serving as a model reference for the inheritance and development of intangible cultural heritage in other regions. Full article

Automated cotton-percentage identification underpins sustainable textile recycling, but established near-infrared and ATR-FTIR spectroscopy systems cost USD 10,000–25,000 per unit and remain inaccessible to small recyclers. We address this on the CottonFabricImageBD dataset (1300 RGB originals, 13 ordinal cotton classes from 30% to 99%) and report three contributions. First, the Learnable Residual LBP stem, which retains the pretrained ResNet50 first convolution intact and adds a fully differentiable Local Binary Pattern branch as an additive contribution gated by a single learnable scalar $\alpha$ initialized to zero, ensuring the model is numerically equivalent to the baseline at initialization (verified to a maximum absolute logit difference below ${10}^{-4}$). Second, a controlled six-variant comparison (vanilla baseline, CLBP, LBP-Conv, LBP-Residual, LBP+SVM, LBP+ANN) under identical stratified five-fold cross-validation on the 1300 dataset originals. Third, the isolation of pretraining preservation as the dominant architectural variable: the 7.08 pp top-1 gap between LBP-Conv (43.77%) and LBP-Residual (50.85%), both embedding the identical learnable LBP module, is statistically significant ($p=0.004$, uncorrected paired t-test, $df=4$) and consistent across all five folds. This gap mainly reconfirms, in the LBP setting, the established cost of discarding pretrained early-layer filters; by contrast, the improvement of LBP-Residual over the vanilla baseline (1.31 pp top-1) is consistent in direction but not statistically significant at the five-fold level ($p=0.229$), so LBP-Residual, CLBP (50.23% top-1), and the baseline (49.54% top-1) are statistically tied on aggregate accuracy and the ranking among them is exploratory. Classical LBP+SVM and LBP+ANN baselines reach 31.85% and 34.46% top-1, confirming a genuine but limited cotton-density signal in hand-crafted descriptors. Compared to the concurrent triplet-architecture approach of Wiedemann et al. (2025), which achieves 48.15% top-1 accuracy on the same dataset under identical five-fold cross-validation, LBP-Residual attains 50.85% top-1 using a single lightweight backbone rather than an ensemble of three. These results support the design principle: augment, do not replace. Full article

Large Language Model (LLM)-based agents automate complex tasks by integrating external tools such as web browsers, e-mail clients, file readers, and APIs, but this same integration exposes them to indirect prompt injection (IPI) attacks, in which malicious instructions hidden in tool content hijack the agent. A central but often overlooked question is how defending against such attacks affects the LLM and its own task performance and computational efficiency. In this study, we design a comprehensive testbed and a layered defense, Spotlight-Guard, that combines spotlighting-based input isolation, an LLM detection-and-quarantine pipeline, and instruction integrity based on a Hash-based Message Authentication Code (HMAC) into a single framework, and we evaluate it jointly along two axes: security and LLM performance. Experiments on locally hosted 7B-class open-weight models (Qwen-2.5-7B, Mistral-7B, and DeepSeek-Coder) use Attack Success Rate (ASR) for security and benign-task success rate together with confusion-matrix-based metrics (precision, recall, and F1) for task performance, all with bootstrap 95% confidence intervals. Across a stratified, fixed-seed benchmark of 250 adversarial and 250 benign cases per configuration, the full system reduces the ASR from 36.0% to 17.2% while preserving a 97.2% benign-task success rate and raising the detection F1 from 0.749 to 0.892, demonstrating that strong protection need not degrade the model’s task performance. A component ablation isolates each layer’s contribution, an adaptive-attack evaluation confirms a low ASR (6.7%) under attacks crafted to target the pipeline, and an analysis of computational cost (model invocations per request) quantifies the efficiency overhead, characterizing the security–performance trade-off of layered defenses on open-weight LLMs. Full article

The growing affordability, autonomy, and swarming of small unmanned aerial vehicles (UAVs) turn low-altitude defense from single-shot interception into a multi-node cooperative decision problem, in which the loss of sensing, coordination, or engagement nodes breaks the closed loops linking them. This study formulates their recovery as the dynamic reconfiguration of cooperative counter-UAV task chains. Given a pre-disturbance plan and a set of failed defending nodes, reconfiguration is modeled as a constrained bi-objective optimization balancing recovered engagement effectiveness against the change to the baseline plan and is solved by Multi-Agent Heuristic Evolution (MAHE), an automated heuristic design framework whose evolution, coordinator, repair, and reflection agents—driven by a large language model—evolve scoring heuristics for a fixed reconfiguration solver. Across instances of varying scale and under light-to-heavy node loss conditions, MAHE outperforms both a single-agent heuristic design counterpart and a range of hand-crafted solvers: on ten held-out test instances spanning 8–320 targets it attains the highest overall normalized hypervolume (0.947, versus 0.935 for the single-agent counterpart and 0.30–0.45 for the hand-crafted solvers) and the best mean rank (1.43 of six methods, $p<{10}^{-5}$); the hand-crafted solvers lose most of their solution quality as the problem grows, whereas MAHE preserves it and sustains high recovery at a nearly constant reconfiguration cost. An ablation confirms that its agents contribute complementary gains. These simulation results indicate that automatically generated, reconfiguration-specific heuristics offer a scalable algorithmic foundation for dynamic, heterogeneous, and constraint-intensive counter-UAV task-chain reconfiguration. Full article

Multi-objective optimization provides the mathematical foundation for reasoning about trade-offs in complex decision problems, from engineering design to resource allocation. Software testing exemplifies such problems: practitioners must simultaneously optimize for fault detection capability, code coverage, execution cost, and test suite diversity—objectives that are fundamentally incommensurable. Since the early 2000s, multi-objective evolutionary algorithms (MOEAs) such as NSGA-II, MOEA/D, and their many-objective extensions (MOSA; DynaMOSA) have served as the dominant mathematical framework for navigating these trade-offs through Pareto-front approximation with hand-crafted fitness functions. However, the recent emergence of reinforcement learning (RL) and large language models (LLMs) is shifting the optimization paradigm from numerical Pareto-front approximation toward neural, semantically aware decision making over learned representations. This paper presents a systematic mapping study of multi-objective optimization algorithms, tracing their evolution from classical Pareto-based methods toward AI-driven and hybrid approaches, with software testing as the primary application domain. We survey 120+ papers published from 2000 to 2025 and propose a novel five-level taxonomy (L1–L5) that classifies optimization approaches along the intelligence spectrum: classical MOEAs, ML-guided MOEAs, RL-driven optimization, LLM-driven optimization, and hybrid neuro-evolutionary systems. For each level, we analyze the mathematical problem formulations (Pareto optimality conditions, Markov decision processes, and neural loss landscapes), objective function design, algorithmic convergence properties, and computational complexity. We further conduct a cross-cutting mathematical analysis comparing these paradigms along dimensions of convergence, diversity, scalability, and interpretability. Our survey identifies critical open mathematical challenges: the lack of formal convergence guarantees for LLM-driven optimization, the under-exploration of many-objective ($m\ge 4$) formulations in AI-driven testing, the sample complexity of reinforcement learning for combinatorial test optimization, and the absence of standardized benchmarks with known Pareto-optimal frontiers. We conclude by outlining a research roadmap for the next generation of multi-objective optimization systems that combine the complementary mathematical strengths of neural function approximation and evolutionary diversity preservation. Full article

Porous microspheres based on 1,4-(dimethacryloyloxymethyl)naphthalene (DMN) and trimethylolpropane trimethacrylate (TRIM) were obtained by suspension–emulsion polymerization in the presence of toluene as a porogenic diluent. The obtained copolymers were subsequently modified using tetrachloromethane in the presence of anhydrous AlCl₃ via a Friedel–Crafts-type reaction. The influence of monomer composition and post-polymerization modification on the porous structure parameters and thermal stability of the materials was investigated. The synthesized copolymers exhibited well-developed porous structures with surface areas ranging from 368 to 494 m²/g. Increasing the TRIM content resulted in higher crosslinking density, earlier phase separation during polymerization, and formation of a finer porous architecture characterized by increased surface area and lower pore diameters. Chemical modification caused moderate and composition-dependent changes in the porous structure while preserving the mesoporous character of the materials. The highly crosslinked copolymers demonstrated the greatest structural stability during modification. Thermogravimetric analysis performed in helium revealed high thermal resistance of both parent and modified copolymers. The degradation process proceeded in two main stages characteristic of highly crosslinked methacrylate networks. Increasing TRIM content improved resistance toward advanced thermal decomposition, increasing the T_50% values up to 415 °C. Post-polymerization modification slightly decreased the temperature of the second degradation stage, probably due to the introduction of thermally less stable chlorinated fragments, while simultaneously increasing char residue formation. The synthesized materials were also evaluated as stationary phases for gas chromatography. Owing to their high thermal stability and the presence of polar ester functionalities, the copolymers enabled efficient separation of aliphatic alcohols at elevated temperatures. The obtained results demonstrate that porous poly(DMN-co-TRIM) microspheres constitute promising thermally stable materials with tunable porous structure and potential applications in chromatographic separation techniques. Full article

Rough set methodology has gained broad acceptance as a potent mathematical apparatus for feature selection within data mining and machine learning. Yet, classical rough sets hinge on equivalence relations to partition the universe, thereby demanding strict reflexivity, symmetry, and transitivity conditions that are arduous to satisfy in realistic settings. Although fuzzy rough sets have been explored to mitigate this rigidity, the entropy-based uncertainty measures employed in fuzzy approximation spaces remain acutely sensitive to data quality and noise corruption, potentially inducing severe bias in feature evaluation. Moreover, the literature currently lacks noise-tolerant uncertainty measures capable of accommodating a controlled fraction of classification errors while safeguarding the discriminative strength of feature subsets. Inspired by these gaps, this study develops a feature selection framework grounded in variable precision fuzzy entropy within the fuzzy rough set context. To this end, fuzzy decision is adopted to portray the membership degree of samples relative to decision classes, thereby enabling more precise detection and elimination of redundant attributes during approximation. An uncertainty quantifier termed fuzzy relational entropy is then introduced to appraise the distinguishing power of fuzzy similarity relations generated by attribute subsets. Leveraging fuzzy decision, a portfolio of uncertainty measure variants, specifically the variable precision joint discriminant index, the variable precision conditional discriminant index, and the variable precision mutual discriminant index, is developed to counteract noisy data effects. These variable precision discriminant indexes sanction a regulated error proportion and afford a measure of noise resistance. Finally, knowledge reduction for fuzzy decision systems is attacked from the angle of discriminative capability preservation, and a heuristic feature selection algorithm is crafted around the variable precision conditional discriminant index. Evaluation on twelve public UCI datasets reveals that the proposed algorithm effectively prunes redundant features and delivers competitive results against three representative alternatives: classical rough set, neighbourhood-based discriminant index, and fuzzy rough set feature selection. Additionally, it sustains stable classification performance across an extensive sweep of the variable precision parameter. Full article

Traditional carvings, such as stone and wooden carvings, are important material carriers of intangible cultural heritage craftsmanship. High-quality three-dimensional (3D) digital replicas of these carvings provide essential support for their preservation, inheritance, interpretation, and dissemination. Owing to their intricate geometries, fine surface details, diverse materials, and complex acquisition backgrounds, high-fidelity 3D reconstruction of traditional carvings remains a challenging issue. In this study, we propose an object-centric 3D Gaussian Splatting (3DGS) framework for traditional craft carving reconstruction. Built upon the baseline 3DGS model, the proposed framework leverages the advanced segmentation capability of Segment Anything Model 2 (SAM-2) to extract foreground masks and generate alpha-channel inputs, enabling the reconstruction process to focus on the target carving. In addition, depth priors are used to guide local densification in regions with insufficient Gaussian coverage, providing auxiliary support for weakly textured or locally blurred carving details. Experiments were conducted on a self-built image dataset of stone and wooden carvings collected from Hui’an County, Quanzhou, Fujian Province, China. The experimental results show that the proposed object-centric strategy effectively preserves the original visual textures and local geometric features of traditional carvings while improving rendering efficiency. Furthermore, the optimized 3D Gaussian models are exported as lightweight digital assets and integrated into Unreal Engine 5, enabling multi-perspective visualization and interactive virtual exhibition in a contextualized digital environment. These results suggest that the proposed workflow is more suitable for producing compact object-level Gaussian assets for carving exhibition, while the depth-guided module mainly improves local details in weakly textured or shallow-relief regions. Full article

The European crafts ecosystem faces critical structural threats, declining practitioner numbers, weakening intergenerational transmission, limited digital literacy, and competition from industrial imitation. Existing online craft communities are narrowly material-specific and structurally ill-suited to the cross-disciplinary dialogue required for systemic sector transformation. This paper presents the design, iterative development, and pilot evaluation of the Craeft Community, a multi-stakeholder Virtual Community of Practice (VCoP) developed within the Horizon Europe CRAEFT project. Three research questions guided the study: how a multi-stakeholder VCoP should be structured to overcome disciplinary fragmentation; to what extent a stewarded digital forum can operationalize Situated Learning and Communities of Practice theory; and what factors facilitate or inhibit engagement and post-funding sustainability. Using design-based research, the platform evolved through four iterative phases, culminating in restructuring from a material-based architecture into five transversal thematic pillars, driven by survey evidence from 151 European craft professionals and systematic stakeholder feedback. The pilot phase yielded 86 registered members, 31 posts, and 27 interactions, with Transmission & Training as the most engaged pillar. Qualitative analysis reveals substantive cross-disciplinary discourse alongside a structural Effort-Engagement Gap, a persistent tension between forum participation demands and the gravitational pull of mainstream social media. The study demonstrates that a thematically organized, stewarded VCoP can meaningfully operationalize apprenticeship-based learning in digital settings, advancing craft heritage preservation, economic resilience, and hybrid professional identity formation at the intersection of craft and technology. Full article

Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations. Addressing these degradations jointly—rather than sequentially—has been shown to improve physical interpretability, yet existing joint deblurring–unmixing methods rely primarily on hand-crafted regularizers that do not fully exploit spatial–spectral structure. Meanwhile, recent plug-and-play (PnP) approaches applied to HSI leverage deep priors but focus solely on either deconvolution or unmixing in isolation. To bridge this gap, we formulate the joint inverse problem of hyperspectral deblurring and spectral unmixing and propose, to our knowledge, the first plug-and-play framework tailored for this coupled task using the Alternating Direction Method of Multipliers (ADMM) and a pretrained deep denoiser (DnCNN) as an implicit PnP prior. Our method uses the natural splitting properties of ADMM to separate a physics-driven subproblem that enforces fidelity to the hyperspectral forward model, which includes linear mixing and blur under a linear, space-invariant convolution approximation, from the data-driven prior step. This synergy of model-based fidelity and learned spatial prior enables more accurate abundance estimates than those obtained with approaches relying solely on analytical regularizers. Experimental results on real hyperspectral datasets demonstrate that the proposed Plug-and-Play Joint Deconvolution and Unmixing (PnP-JDU) method outperforms conventional unmixing baselines, stand-alone PnP unmixing methods, and the Deblurring and Sparse Unmixing via the Alternating Direction Method with Total Variation (DSUnADM-TV) baseline in reconstruction and abundance accuracy metrics. Across the tested datasets and imaging conditions, PnP-JDU achieves lower RMSE, higher PSNR, lower reconstruction and abundance errors, and lower SAD values, while preserving fine spatial details and producing physically meaningful abundance maps. Full article

Accurate kidney tumor segmentation from abdominal CT is essential for quantitative assessment and treatment planning. However, indistinct tumor boundaries and substantial inter-patient shape variability render traditional hand-crafted feature-based methods unreliable for precise delineation. Although deep learning has advanced this task, these methods still struggle with multi-scale tumor characteristics, complex morphological variations, and background noise in medical images. To address these challenges, we propose MDA-Net, an end-to-end segmentation method based on enhanced multi-scale feature extraction and attention refinement. Specifically, we introduce a Multi-Scale Feature Extraction (MSFE) module into encoder–decoder skip connections to aggregate dilated features across multiple receptive fields and learn branch-wise weights for adaptive refinement and fusion, thereby enhancing boundary details and semantic cues to reduce tumor-tissue ambiguity. At the bottleneck, a Deformable Pyramid Feature Refinement (DPFR) module combines deformable sampling with pyramid contextual modeling, thereby improving adaptability to variations in tumor shape and scale while preserving feature resolution. Moreover, a Channel and Spatial Attention (CASA) module is embedded in the decoder to suppress background interference and enhance boundary-sensitive structures during upsampling via coordinated channel and spatial reweighting, thereby improving the reconstruction of fine-grained tumor morphology and contours. Experiments on both KiTS19 and KiTS21 show that MDA-Net consistently improves tumor boundary delineation, lesion localization, and mask reconstruction, demonstrating stronger robustness and cross-dataset generalizability than representative baseline methods. Ablation studies further confirm the complementary effects of MSFE, DPFR, and CASA. In addition, Grad-CAM visualizations improve the clinical transparency and interpretability of the model. Overall, this method advances deep learning for medical image analysis and supports precise diagnosis and treatment of renal tumors. Full article