Search Results (34)

EEG-based motor imagery (MI) decoding is commonly evaluated in open loop, but strong offline performance does not necessarily translate into stable real-time control once decoder outputs drive a physical device. This issue is especially relevant for lower-limb exoskeletons, where initiating movement, sustaining movement, stopping movement, and maintaining rest can impose different controller-level demands. This study presents a proof-of-concept controller-level validation of an EEG-driven ankle exoskeleton framework, using a staged design that links open-loop decoder development to real-time closed-loop controller testing. Open-loop EEG data were collected from nine able-bodied participants during static and dynamic ankle MI using an eight-channel g.tec Unicorn Hybrid Black system, with matched PA-SEMI outputs available for eight participants in the primary open-loop comparison. We used a hybrid feature representation combining spectral, spatial covariance, and temporal complexity descriptors to compare a supervised Passive–Aggressive (PA) classifier with a Semi-supervised latent learning network (SEMI). Performance was assessed using epoch-level and persistence-based event metrics intended to reflect controller triggering. Under the evaluated model-specific protocols, SEMI produced higher open-loop accuracy and lower false-trigger rates than PA. The reported SEMI analysis was transductive: feature windows from the target-participant evaluation runs were available without labels during consistency training, and their labels were withheld until final evaluation. However, we selected PA for the primary matched closed-loop validation because it could be retrained, checked, and deployed within the same-day workflow. Since SEMI was not evaluated in a balanced matched closed-loop comparison, this study does not determine whether PA or SEMI provides superior real-time controller performance. Deployment-oriented PA updates were audited using limited same-day calibration data and evaluated in matched PA-based closed-loop trials with three participants, based on online controller logs. The closed-loop experiments were conducted on RehAnkle, a pre-commercial robotic ankle rehabilitation device operated here as a single-active-DoF ankle platform for dorsiflexion-oriented EEG control. In closed-loop trials, start and stop commands were generally reliable, whereas sustained movement and sustained rest were less stable. These proof-of-concept results indicate that command generation and state maintenance should be evaluated as separate controller-level problems, and that open-loop accuracy alone is insufficient to characterize real-time exoskeleton control. Full article

Artificial intelligence (AI) marketing systems increasingly plan campaigns, generate messages, assess performance, and revise outputs with limited human input. In such multi-agent systems (MAS), commercial optimization pressure may produce ethical compliance drift: gradual movement from acceptable persuasion toward detector-defined manipulation-risk signals. We examine this problem in a controlled OpenClaw simulation of a three-role AI marketing team. The study varies optimization pressure across baseline, moderate-pressure, and high-pressure operating regimes while holding the model backend, prompt pool, agent roles, and monitoring process constant. Optimization pressure significantly affected detector-defined manipulation-risk scores, and the observed pattern was non-monotonic. The moderate-pressure condition produced the highest observed mean, but moderate and high pressure were not statistically distinguishable in the pairwise comparison. These results reflect detector-based risk indicators in simulated marketing-agent outputs, not direct evidence of consumer harm, deception, or real-world behavioral manipulation. The findings support pressure-aware auditing as a standards-informed monitoring practice consistent with IEEE value-sensitive design principles, while also identifying the need for human annotation, hybrid detectors, and real-user validation before stronger governance claims are made. Full article

The global expansion of K-pop has increased demand for AI-driven choreography learning. However, existing motion recognition models often struggle to capture fine-grained rhythm patterns and dynamic motion transitions across consecutive frames, limiting their ability to provide accurate and objective feedback. To address these challenges, this paper proposes a Large Language Model-based Dance Generative Control (L-DGC), an integrated framework for controllable dance generation and evaluation. The framework comprises four stages: a Visual Analysis Phase (VAP) for skeletal extraction; an Audio Analysis Phase (AAP) for rhythmic synchronization; a Multimodal Data Phase (MDP), which employs Long Short-Term Memory (LSTM) and Transformer architectures to evaluate movement accuracy; and a three-dimensional (3D) Transformation Phase (3TP), which converts two-dimensional (2D) skeletal data into 3D character animations within the Unity engine. Guided by an LLM, the framework performs real-time inference and iterative refinement to optimize choreographic data without requiring subjective expert assessment. By quantifying choreographic components and transforming 2D motion data into 3D representations, L-DGC provides an objective evaluation framework for dance learning. The proposed system has significant potential for artificial intelligence (AI)-based dance education, real-time feedback applications, and automated audition platforms in the entertainment industry. Full article

Interior built environments influence user behavior through more than deliberate rational evaluation. They shape attention, movement, affective comfort, perceived safety, wayfinding, and well-being through bounded cognition, affective appraisal, heuristics, embodied perception, and automatic approach–avoidance processes. The research gap addressed in this review concerns the fact that prior work on interior environments, wayfinding, indoor environmental quality, neuroarchitecture, atmospherics, and behavioral decision-making remains fragmented across separate studies, and existing reviews rarely explain how these mechanisms can be organized into a design-usable framework for interior built environments. This narrative review synthesizes foundational and recent literature across building design, environmental psychology, neuroarchitecture, virtual reality, indoor environmental quality, wayfinding, and behavioral decision-making to clarify how decision mechanisms translate into interior design variables such as lighting, color, spatial organization, materiality, form, sensory atmosphere, environmental legibility, thermal comfort, and controllability. The review distinguishes bounded rationality, heuristics and biases, dual-process accounts, affective and atmospheric processing, prospect–refuge dynamics, mere exposure, and room-effect research rather than treating them as a single “non-rational” category. It proposes an integrative framework in which interior cues are processed through perceptual and affective appraisal; moderated by individual, cultural, contextual, temporal, and ethical factors; and expressed through behavioral outcomes such as navigation, approach or withdrawal, dwell time, perceived quality, usability, stress regulation, and well-being. The paper contributes to human-centered building design by formalizing a mechanism-based account of how interior environments can support behavior without reducing users to passive recipients of environmental manipulation. It concludes with practical implications for design briefing, post-occupancy evaluation, VR-based testing, healthcare and workplace audits, safety-critical settings, and future longitudinal validation. Full article

This study addresses two critical issues in power material warehouse management: insufficient positioning accuracy leading to inefficient inventory auditing and uncontrolled material movement, and procurement-demand imbalances caused by subjective forecasting methods. We present an integrated warehouse management system that synergizes Ultra-Wideband (UWB) centimeter-level real-time positioning with data-driven demand forecasting. The UWB subsystem, built on STM32F1 microcontrollers (STMicroelectronics, Geneva, Switzerland) and DW1000 RF modules (Decawave Ltd., Dublin, Ireland), achieves high-precision location tracking by employing the Double-Sided Two-Way Ranging (DS-TWR) method combined with trilateration and triangular centroid algorithms. The data-driven procurement subsystem utilizes a vast historical dataset (4.86 million records from 36,988 grid projects, 2020–2024) to train demand prediction models. A comparative evaluation of six algorithms identified the Random Forest (RF) model as optimal, demonstrating superior performance with 89.2% accuracy, a Mean Absolute Error (MAE) of 5.48, and a Mean Absolute Percentage Error (MAPE) of 4.89%. The RF model effectively incorporates key factors like failure rates and seasonal cycles. Experimental validation confirmed the UWB subsystem’s robustness, with an average positioning error of 12.05 cm. The integrated system enables precise material tracking, 3D trajectory reconstruction, and generates data-informed procurement signals—including replenishment warnings, optimized order quantities, and adaptive resupply cycles. This approach significantly reduces surplus inventory while maintaining high material availability, offering a scientific, data-driven solution for enhancing efficiency in power material management. Full article

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems remain a widely deployed defense against automated abuse, but advances in machine learning have reduced the effectiveness of traditional challenge-based designs and exposed limitations in proprietary risk-scoring systems. This paper presents an adaptive, reinforcement learning-based CAPTCHA defense framework for high-security web applications. The proposed system formulates bot detection as a partially observable Markov decision process and uses a Proximal Policy Optimization (PPO) agent with Long Short-Term Memory to analyze streamed behavioral telemetry, including mouse movements, clicks, keystrokes, and scrolling, over sequential interaction windows. During the observation phase, the agent can continue observing or deploy a honeypot as an early-intervention and evidence-gathering action; after sufficient session evidence is accumulated, it can issue graded CAPTCHA challenges, allow a session, or block it. To complement the sequential agent, the framework also includes an XGBoost classifier that produces a session-level human-likelihood score as a supervised benchmark. The accompanying reinforcement learning environment and code base are publicly available, allowing future researchers to train, evaluate, and extend adaptive CAPTCHA policies as bot capabilities evolve. Experiments conducted on a sandbox ticket-purchasing web application demonstrate that the proposed methodology achieves strong preliminary performance on human-generated sessions and real bot sessions produced by scripted, replay-based, and Large Language Model (LLM)-powered agents. Among the evaluated reinforcement learning algorithm variants, Soft PPO achieved the best performance with 97.7% accuracy, 100% precision, and a 97.6% F1 score. Correspondingly, the XGBoost classifier achieved 99.48% accuracy, a 1.000 ROC-AUC (receiver operating characteristic area under the curve), and a 0.9919 F1 score. Our results indicate that sequential reinforcement learning can support accurate and low-friction bot detection, while the accompanying classifier provides a complementary binary benchmark. Compared to proprietary systems, the proposed framework emphasizes transparency, auditability, and explicit sequential decision-making rather than black-box risk scoring. Overall, this work introduces a publicly available, open, and adaptive CAPTCHA defense framework that supports transparent experimentation with behavior-based bot mitigation while also identifying the remaining limits that must be addressed before commercial deployment. Full article

Residential valuation under the comparison principle requires systematic adjustment for material differences between comparable units. In high-density, high-rise housing markets, however, visual amenities such as harbour and skyline views are often treated qualitatively or implicitly embedded in comparable evidence, reducing transparency and auditability. This study examines whether view quality is systematically capitalized into transaction prices in Hong Kong and whether such premiums vary across market conditions. Using 352 secondary market transactions from six prime high-rise estates (2015–2024), we estimate hedonic models with the logarithm of price per saleable area as the dependent variable. View quality is specified as an ordered categorical variable (nil, partial, full), constructed from listing descriptions and cross-validated using map and street-view evidence. Controlling for floor level, estate age, monthly market movements proxied by the Centa-City Index (CCI), and estate fixed effects, the pooled estimates indicate that partial views command an approximate 11% premium and full views an approximate 22% premium relative to nil view, with a clear incremental premium for full over partial views. Split-sample estimation using GDP-defined regimes reveals partial state dependence: full view premiums remain economically meaningful across market conditions, whereas partial view effects become less precisely identified during weaker periods. The findings demonstrate that view quality is a material and systematically priced attribute in Hong Kong’s vertically differentiated housing market. By providing transparent percentage-based adjustment benchmarks grounded in within-estate variation, the study enhances the consistency, transparency, and evidential rigor of comparable-based valuation practice. Full article

Reliable short-term air traffic forecasts are important for operational planning in national airspace systems. This study develops a transparent forecasting framework for Türkiye’s monthly aircraft movements using publicly available data from the General Directorate of State Airports Authority (DHMİ) for 2018–2025. Because DHMİ releases may follow cumulative within-year reporting, month-specific increments are reconstructed through within-year differencing and checked through simple audit procedures. The empirical analysis compares seasonal naïve, ETS, and a constrained SARIMA family under leakage-free evaluation, combining a strict 2025 holdout with expanding-window rolling-origin validation. Forecast performance is assessed using standard accuracy metrics and complemented by Diebold–Mariano comparisons, which are interpreted cautiously, given the short holdout length. To examine instability around the pandemic period, this study also reports structural-break and stability diagnostics as supportive evidence rather than definitive identification. Uncertainty is evaluated through backtested 80% and 95% prediction intervals, comparing nominal SARIMA intervals, parametric bootstrap, split conformal prediction, and adaptive conformal inference (ACI). The results show that SARIMA provides the strongest point-forecast performance among the benchmarked models, while adaptive conformal calibration offers a useful balance between empirical coverage and interval width under changing conditions. Overall, this study provides a reproducible and operationally interpretable baseline for national air traffic forecasting in Türkiye and a clear benchmark for future multivariate extensions. Full article

Network operators increasingly rely on abstracted telemetry (e.g., flow records and time-aggregated statistics) to achieve scalable monitoring of high-speed networks, but this abstraction fundamentally constrains the forensic and security inferences that can be supported from network data. We present a design-time audit framework that evaluates which threat hypotheses become non-supportable as network evidence is transformed from packet-level traces to flow records and time-aggregated statistics. Our methodology examines three evidence layers (L0: packet headers, L1: IP Flow Information Export (IPFIX) flow records, L2: time-aggregated flows), computes a catalog of 13 network-forensic artifacts (e.g., destination fan-out, inter-arrival time burstiness, SYN-dominant connection patterns) at each layer, and maps artifact availability to tactic support using literature-grounded associations with MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK). Applied to backbone traffic from the MAWI Day-In-The-Life (DITL) archive, the audit reveals selectiveinference loss: Execution becomes non-supportable at L1 (due to loss of packet-level timing artifacts), while Lateral Movement and Persistence become non-supportable at L2 (due to loss of entity-linked structural artifacts). Inference coverage decreases from 9 to 7 out of 9 evaluated ATT&CK tactics, while coverage of defensive countermeasures (MITRE D3FEND) increases at L1 (7 → 8 technique categories) then decreases at L2 (8 → 7), reflecting a shift from behavioral monitoring to flow-based controls. The framework provides network architects with a practical tool for configuring telemetry systems (e.g., IPFIX exporters, P4 pipelines) to reason about and provision the minimum forensic coverage. Full article

Pedestrian accessibility is a critical dimension of sustainable and inclusive transportation systems, yet many cities lack reliable data on infrastructure features that support visually impaired users. Among these, podotactile paving plays a vital role in guiding movement and ensuring safety at intersections and transit nodes. However, tactile paving networks remain largely absent from digital transport inventories and automated mapping pipelines, limiting the ability of cities to systematically assess accessibility conditions. This paper presents a scalable approach for identifying and mapping podotactile areas from mobile and handheld laser scanning data, broadening the scope of data-driven urban modelling to include infrastructure elements critical for visually impaired pedestrians. The framework is evaluated across multiple sensing modalities and geographic contexts, demonstrating robust generalization to diverse transport environments. Across four dataset configurations from Madrid and Mantova, the proposed DeepLabV3+ model achieved podotactile F1-scores ranging from 0.83 to 0.91, with corresponding IoUs between 0.71 and 0.83. The combined Madrid–Mantova dataset reached an F1-score of 0.86 and an IoU of 0.75, highlighting strong cross-city generalization. By addressing a long-standing gap in transportation accessibility research, this study demonstrates that podotactile paving can be systematically extracted and integrated into transport datasets. The proposed approach supports scalable accessibility auditing, enhances digital transport models, and provides planners with actionable data to advance inclusive and equitable mobility. Full article

The atmosphere of an interior space within an architectural built form can be defined by the interactions between the material and immaterial elements surrounding the inhabitant of a space, expressed through our own responding embodied experience. These psychologically tangible yet often immaterial experiences are deeply embodied, realised through our interconnected visual perception, haptic engagement, auditory characteristics, temporal movement and thermal comfort. The study questions how we can harvest useful data to explore atmosphere as an “in-between” state between perceiver and surroundings, through aligning physical environmental recordings with felt personal responses over parallel time-based studies. The approach explored analyses a set of existing spaces through the harvesting of sensory elements using on-site, temporal recordings and participatory haptic engagement. Physical presence is recorded through measured environmental data and audited through a theoretical stance of “conservation of mass”, as each extracted element is replaced and balanced by the other sensorial elements, supporting a holistic experience. Evolving thinking around design approaches promoting an awareness of atmospheric sensibilities can ensure that we do not lose the rich opportunities that sensory design can provide for contemporary architectural design practice. Harvesting atmospheres seeks to describe the broad, elemental nature of sensory design, defining examples of real-time temporary, elusive boundaries and fluid domains that shift spaces between atmospheric experiences, whilst supporting the interconnected collage of the “in-between” complexity of designing with this realm. Full article

Rankings drive consequential decisions in science, sports, medicine, and business. Conventional evaluation methods typically analyze rank concordance, dispersion, and extremeness in isolation, inviting biased inference when these properties co-move. We introduce the Concordance–Dispersion–Extremeness Framework (CDEF), a copula-based audit that treats dependence among these properties as the object of interest. The CDEF automatically detects forced versus non-forced ranking regimes, then screens dispersion mechanics via ${\chi }^2$ tests that distinguish independent multinomial structures from without-replacement structures and, for forced dependent data, compares Mallows structures against appropriate baselines. The framework estimates upper-tail agreement between raters by fitting pairwise Gumbel copulas to mid-rank pseudo-observations, summarizing tail co-movement alongside Kendall’s W and mutual information, then reports likelihood-based summaries and decision rules that distinguish genuine from phantom agreement. Applied to pre-season college football rankings, the CDEF reinterprets apparently high concordance by revealing heterogeneity in pairwise tail dependence and dispersion patterns that inflate agreement under univariate analyses. In simulation, traditional Kendall’s W fails to distinguish scenarios, whereas the CDEF clearly separates Phantom from Genuine and Clustered agreement settings, clarifying when agreement stems from shared tail dependence rather than stable consensus. Rather than claiming probabilities from a monolithic trivariate model, the CDEF provides a transparent, regime-aware diagnosis that improves reliability assessment, surfaces bias, and supports sound decisions in settings where rankings carry real stakes. Full article

The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions. Full article

In the ever-evolving world full of technologies, blockchain proves itself to be the most secure way of dealing with tampering of data. This paper proposes an innovative model for tracking employees within facilities using RFID, IoT devices and blockchain technology implemented on the Hyperledger Fabric platform. The blockchain system supports a secure and tamper-proof recording of employee movement because it keeps the data in a decentralized system. Smart contracts automate activities like control access, generate alerts and create audit trails without the need for centralized management. This implementation shows a high level of security and efficiency, making it a good approach to improve monitoring and compliance within organizations. Full article

Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates its own information system, tailored to manage internal data, but often lacks the ability to communicate effectively with external systems. Moreover, when data exchange between different systems is required, it becomes critical to maintain full control over the shared data and to manage access rights precisely. In this work, we propose the concept of interoperable traceability. We present a model that enables the seamless integration of data from sensors, IoT devices, data management platforms, and distributed ledger technologies (DLT) within a newly designed data space architecture. We also demonstrate a practical implementation of this concept by applying it to real-world scenarios in the agri-food sector, with direct implications for transportation systems and all stakeholders in a supply chain. Our demonstrator supports the secure exchange of traceability data between existing systems, providing stakeholders with a novel approach to managing and auditing data with increased transparency and efficiency. Full article