Model Context Protocol (MCP) clients may approve a Tool whose authenticated, client-visible contract later changes. Directional Contract Transition Revalidation (DCTR) retains grant-bound operational and last-human-reviewed baselines, canonicalizes contracts with RFC 8785/JCS, and assigns L0–L4 renewal actions to typed directional changes. Because a maximum of individual path levels can miss jointly introduced behavior, the final proposed DCTR-IA-2.0 decision rule requires a hash-bound whole-contract assessment for multi-path or coupled schema changes in either the local or cumulative delta; unresolved cases remain pending human reapproval. With official MCP Python SDK clients, this gate withheld the original compound counterexample in simultaneous and sequential forms and a four-case SDK outcome matrix (one reusing the paired field pattern and three testing different mutation types); three benign controls also incurred review. On 20 natural N2 Tool transitions (43 changed paths, 14 maintainer clusters), a reproducible structural replay routed 14 contracts to whole assessment, without establishing natural interaction severity or unnecessary review rates. Independent expert agreement was 28/34 paths in the frozen held-out study (weighted κ = 0.720), 18/21 in the separate pilot, and 37/48 (κ = 0.710 on 47 ordinal pairs) across 20 selected N3 transitions in eight repositories; jointly assigned L3 paths occurred in 11 clusters across five repositories, with ten subsequently documented casewise panel agreements and one separately documented DEFERRED case; the author-submitted post-review tally classifies 47/48 (L0 = 3, L1 = 5, L2 = 19, L3 = 20, L4 = 0) without assigning a level to N3K0023 or claiming independently authenticated expert sign-offs or natural L4. A separate selected Study B yielded 10/10 agreement distinguishing six adapted R4/L4 poisoning stimuli from four disclosed R3/L3 controls. These controlled assessments and enforcement tests do not establish autonomous semantic risk detection, reduced production reapproval burden, or prevented harm. ETDI and Microsoft contrasts are comparisons of policy design only.
Acoustic sensor nodes protecting logistics sites use fixed-label classifiers, so adding a class requires new data, retraining, and redeployment of every node. Audio–language models promise to add classes by text description. We evaluated three frozen contrastive audio–language models on 25,599 events (15,241 gunshots) and on an external field set, and measured their cost on six edge platforms. On supplied clips, a vocabulary-independent score calibrated on separate data for a 5% false-positive target recalled 79% to 84% of gunshots at realized rates of 4.8% to 5.6% against unseen background sounds. On field recordings from an unseen domain, separation held on full clips (area under the ROC curve 0.95 to 0.97), but the clip-level false-positive rate rose to 7% to 11%, and only 24% to 25% of gunshots were detected end to end because the onset gate missed distant shots. Fine-grained zero-shot classification was weak: balanced accuracy was 0.53 against 0.50 for supersonic versus subsonic fire and 0.05 against 0.04 for firearm identity. A linear probe reached 0.82 at the regime level, but 0.59 with calibers held out. A firearm class added by one sentence reached 6.4% accuracy, against 54% with twenty labeled examples.
Private keys are the single point of failure in blockchain systems; if lost, assets are irrecoverable, and if stored, they are exposed. This paper presents a template-free biometric cryptosystem that regenerates a 256-bit private key directly from a fingerprint, storing neither the biometric template nor the key. A Siamese convolutional network trained with a two-stage composite loss produces a 1024-bit binary representation; an adaptive, margin-based binarisation with stability-aware selection retains 288 reliable bits; and a block-wise BCH fuzzy extractor regenerates the exact key, while a layered scheme of Scrypt, AES-256-GCM, and Shamir secret sharing protects the helper data. Evaluated on the PolyU fingerprint dataset under a strict cross-session protocol, the system reduces the mean intra-user bit error rate from 8.13% to 2.35%, attains an equal-error rate of 6.27% and an area under the curve of 0.9831, and achieves a 95.83% key-reconstruction success rate with a 0% false key-acceptance rate. The 288-bit key has an estimated effective min-entropy of 269 bits, and helper-data leakage is negligible (mutual information 0.0132). The results show that template-free, cross-session key generation is feasible at the 256-bit level required for blockchain.
Real-time monitoring of muscle fatigue could support safer strength training, yet most wearable systems depend on cloud processing, which adds latency and exposes physiological data. This work presents and evaluates an edge-based prototype for biometric monitoring during biceps curls. A wearable node integrating a nine-axis IMU (MPU-9250) and a photoplethysmography heart-rate sensor (MAX30102) streams data from an ESP32 microcontroller over UDP to a Raspberry Pi 5, which performs feature scaling and inference with a compact feedforward neural network (two hidden layers of 64 and 32 units) trained on a publicly available biceps-curl dataset. The classifier takes a 10-element feature vector of IMU, heart-rate and acceleration-derived inputs and outputs three fatigue levels. Under leave-one-subject-out cross-validation on ten participants, the three-class classifier attained a mean accuracy of 0.77 (SD 0.25; 95% CI 0.59–0.95), with a strongly bimodal per-subject distribution (0.40–0.98). Deployed on the edge node and evaluated end-to-end over 5172 annotated windows under a binary no fatigue versus fatigue formulation, the model recovered 91% of non-fatigued but only 10% of fatigued windows (balanced accuracy 0.51, Matthews correlation 0.02), which is no better than chance and below a majority-class baseline. In its current form the classifier is therefore not a reliable fatigue detector; we analyse candidate causes, including training-side class skew and differences between the source-dataset and deployment sensors, and compare them against majority-class and chance reference points. On the systems side, the pipeline runs fully locally with 10 ms model inference per sample, 42 ms gateway processing and a mean wireless round-trip time of 180 ms after rate-limiting the link. The implemented physical, communication and edge-inference layers are designed as the sensing foundation for a future Digital Twin, which is not implemented in this work.
This cross-sectional survey examines the associations of smart-device usage, personalization, and IoT integration with perceived customer engagement among 392 Greek business and digital-marketing professionals. The leading reported functions were instant communication (35.2%), tailored recommendations (37.5%), and real-time data collection (30.6%); privacy concerns were the most frequently reported adoption barrier (34.2%). The three-predictor regression was statistically significant: F(3, 388) = 138.713, p < 0.001, with R2 = 0.517 and adjusted R2 = 0.514. The reported standardized coefficients were positive for smart-device usage (β = 0.381), personalization (β = 0.304), and IoT integration (β = 0.265). The estimates concern professional perceptions and remain provisional because sampling and measurement diagnostics are incompletely documented; they do not establish causal effects or actual consumer behavior. The implications concern relevant customer interactions, interoperable infrastructure, transparent data practices, and privacy-conscious personalization. Managers should evaluate connected-marketing investments alongside implementation cost and infrastructure readiness. Further research should document sampling and measurement procedures, reproduce the model with respondent-level data, and evaluate generalizability across countries and industries.
