Search Results (396)

Cryptographic inventories and runtime detection identify cryptographic use but do not establish implementation conformance or policy satisfaction. We present a deterministic composition linking admitted runtime evidence, provenance closure, exact implementation identity, Contract-registered NIST test-vector conformance, and content-addressed policy appraisal to ACCEPT, REJECT, or REVIEW verdicts. Controlled SHA-256 and AES-256-GCM workloads produced 12 runtime executions and six provenance-distinct occurrences. The frozen subjects produced expected outputs for all 130 SHA-256 inventory entries and 375 AES-256-GCM ENCRYPT cases, yielding two conformance results. A policy precommitted before authentic appraisal produced six positive-path ACCEPT verdicts; controls demonstrated REJECT and REVIEW. Conformance replay was byte-identical in 10/10 executions per subject, and the complete six-occurrence appraisal reconstructed identically in 10/10 replays. All 12 conformance and 25 appraisal, REVIEW, and anti-fabrication controls passed. No raw AES key or registered direct encoding was detected within the scanned retained-artifact boundary. Public access supports validation of released non-secret evidence and eligible tests, not full regeneration of the original experiment: the workload key, private history, and runnable registered OCI images are unavailable in that release. The contribution is a bounded assurance composition; finite test success and policy ACCEPT do not establish exhaustive correctness, certification, or production authorization. Full article

Remote sensing (RS) images transmitted through distributed satellite, UAV, and sensor networks are vulnerable to interception and misuse, while conventional encryption may reduce the effectiveness of downstream image classification. This paper proposes FOX-SHIELD, a secure encrypted-domain remote sensing image classification framework that integrates Chebyshev-SHA dynamic encryption with a Fox-Optimized Fast Recurrent Neural Network. The Chebyshev-SHA module generates image-specific dynamic keys and applies permutation–diffusion encryption to protect image confidentiality and integrity. The encrypted image representation is then classified using an FRNN optimized by the Fox Optimization Algorithm to improve convergence, classification accuracy, and computational efficiency. Experiments on the UC Merced Land Use and NWPU-RESISC45 datasets evaluate FOX-SHIELD across different scene-classification settings. On UC Merced, FOX-SHIELD achieves 97.14% accuracy, 96.87% recall, and 96.96% F1-score, outperforming selected privacy-preserving and lightweight remote sensing classification baselines. Empirical security analysis further indicates improved entropy, key sensitivity, and resistance to statistical attacks. The results suggest that FOX-SHIELD is a promising framework for secure remote sensing image classification in distributed, resource-aware environments. Full article

Reliable exercise recognition across schools requires models that remain accurate when acquisition conditions change. This study compares classical machine learning, pose-sequence deep learning, and quantum machine learning to classify four standardized physical-fitness exercises: sit-up, biceps curl, sit-and-reach, and horizontal jump. The original repository contained 2034 videos. A SHA-256 audit of the temporal pose arrays identified 152 exact duplicate copies, all from the same school, leaving 1882 unique videos for the primary analysis. We used YOLO26n-Pose to extract 17 COCO keypoints. We evaluated two input representations: 110 aggregated biomechanical descriptors and 128-step sequences containing 34 local pose coordinates. Model selection and evaluation followed a nested Leave-One-School-Out protocol. Random Forest obtained the highest pooled outer-fold performance (Macro-F1 = 0.9964, accuracy = 0.9968, MCC = 0.9953), with six errors in 1882 videos; XGBoost and RBF-SVC followed with Macro-F1 values of 0.9932 and 0.9881. Among the deep models, BiLSTM, CNN1D, and ST-GCN achieved Macro-F1 values of 0.9711, 0.9666, and 0.9498, respectively. A paired stratified bootstrap supported the Random Forest advantage over XGBoost for Macro-F1, although the Holm-adjusted exact McNemar test for overall correctness was not significant. In exploratory controlled QML experiments, neither QSVC nor VQC exceeded its matched classical baseline under the tested configurations. These results show that compact biomechanical descriptors provide a strong and stable representation for cross-school exercise recognition in this dataset. Full article

Class imbalance severely limits Network Intrusion Detection Systems (NIDSs) for minority Web attack classes: CICIDS2017 contains only 21 SQL Injection instances among 2.27 million benign flows. This study enriches CICIDS2017 with authentic SQL Injection, Cross-Site Scripting (XSS), and Web Brute Force (WBF) traffic captured from a controlled DVWA/XAMPP environment, processed with CICFlowMeter to match the original feature space. An anti-data-leakage protocol (stratified partitioning, post-split normalization, five-fold cross-validation, and a SHA-1 cryptographic membership audit of an 8881 –flow test sub-sample) found no hash collisions between this sub-sample and the evaluation partitions. The framework added 32,670 authentic flows, increasing SQL Injection from 21 to 10,678, XSS from 652 to 13,212, and WBF from 1507 to 10,960. Among four evaluated ensemble models, LightGBM performed best, achieving 99.85% Accuracy, 99.85% F1-score, 99.29% Balanced Accuracy, and 97.87 ± 1.88% in five-fold cross-validation, improving detection rates by 44.9% (XSS), 23.0% (WBF), and 16.6% (SQL Injection) over the original dataset. A volume-matched ablation study showed comparable aggregate accuracy to synthetic balancing methods (SMOTE, SMOTE-Tomek), while geometric diversity analysis confirmed that authentic traffic occupies feature-space regions unreachable by interpolation, and chronological holdout evaluation confirmed generalization to unseen traffic (F1: 98.53–99.90%). Real-traffic enrichment thus offers a practical, more realistic complement to synthetic balancing for minority Web-attack detection. Full article

Early-warning research for approaching small unmanned aerial vehicles (sUAVs) requires more than isolated images, sounds, or radio-frequency traces. A reusable resource must connect event context, synchronized observations, evidence strength, media quality, provenance, privacy treatment, and leakage-resistant evaluation units. This Data Descriptor presents uavews 0.1.0-rehearsal, an openly deposited software-and-data record that implements such a model and exercises it end-to-end on a fixed-seed synthetic corpus. The record is a rehearsal of dataset formation and validation, not a measurement campaign. It contains 180 synthetic events—90 controlled-flight simulations, 26 verified observations, 14 weak observations, and 50 hard negatives—together with 4119 analysis windows, 102 source profiles, 971 observations, 391 media objects (214 audio, 121 image, and 56 video objects), and 7242 released labels. The nominal synthetic coverage spans 30 generalized locations in three site groups from 1 April to 15 May 2025. Six canonical Parquet tables, five evaluation manifests, a machine-readable data dictionary, DataCite and PROV-O metadata, an RO-Crate description, validation outputs, source media bytes, and SHA-256 manifests are supplied. The versioned Python pipeline implements ten ordered stages, five reported equations, configurable quality gates, de-identification checks, near-duplicate grouping, and event-, location-, time-, source-, and hard-negative-aware splits. Validation of the rehearsal produced 100% schema and checksum pass rates, median and fifth-percentile completeness of 1.0, synchronization median/p95/max of 3.671/42.184/340.344 ms, exact and near-duplicate rates of 0.512% and 9.463%, cross-modal consistency of 89.88%, and Krippendorff’s alpha of 0.324 for vehicle presence. Seven of eleven release gates passed; the four failures deliberately expose properties that a real release would have to repair or adjudicate. The deposit includes 54 test functions but this article does not claim that the suite was independently executed during manuscript preparation. The accompanying field-trial calculations are planning assumptions, not observations. One of them, the reduced-design sortie count of 7200, omits the configured altitude and background factors and is reported as a known defect of the deposited code rather than as a usable experimental design. The record therefore provides a transparent, executable instrument for designing and auditing a future empirical dataset without representing synthetic values as measured performance. Full article

Hyaluronan (HA), a major extracellular matrix (ECM) glycosaminoglycan, plays a key role in breast cancer progression. Although native HA lacks sulfate groups, chemically modified sulfated hyaluronan (sHA) has demonstrated promising antitumor activity. Previous work from our group showed that sHA alters cellular functions and modulates ECM-related gene expression in triple-negative breast cancer (TNBC) cells. The aim of this study was to investigate the effects of low-molecular-weight HA (50 kDa) and its sulfated derivative of the same molecular weight, sHA, in MDA-MB-231 and MCF-7 breast cancer cells using advanced 3D cell culture models. Gene expression analyses focused on ECM components, including HA receptors and matrix metalloproteinases (MMPs) that were evaluated. The 3D cell morphology was examined using scanning electron microscopy (SEM). Spheroid growth and expression profiles linked to ECM remodeling and invasiveness were also assessed. Notably, sHA significantly inhibited 3D spheroid growth, reduced cell spreading in a cell line-dependent manner and influenced the expression of genes correlated with ECM remodeling and HA synthesis. These findings emphasize the importance of 3D models for studying ECM-driven breast cancer progression, further supporting sHA as a potential therapeutic modulator. Full article

Graph Neural Networks (GNNs) have become a foundational tool for e-commerce recommendation systems, yet they consistently fail in zero-shot cold-start scenarios where new products enter the market without prior interactions. In this paper, we diagnose this failure as a structural vulnerability rather than a simple data sparsity issue. We introduce the concept of asymmetric learning, demonstrating that in severely imbalanced bipartite graphs, minority-type nodes (products) become disproportionately reliant on topological signals. By evaluating this phenomenon alongside a relatively balanced control dataset, we confirm that this performance collapse is a byproduct of the data structure rather than architectural design. To mitigate this limitation, we propose Similarity-Driven History Augmentation (SHA), a data-centric approach that assigns synthetic interaction histories to cold-start products by matching them with semantically similar established donors. To prevent these synthetic signals from degrading the representations of established nodes, we further introduce a decoupled hybrid framework alongside an enhanced SHA strategy that selectively filters active customers. Comprehensive evaluations across multiple real-world e-commerce datasets, including DataCo and Amazon Gift Cards, confirm the effectiveness and stability of our approach across different GNN architectures. Full article

Existing chaos-based image ciphers may suffer from limited dynamical complexity in low-dimensional maps and insufficient diversification of encryption-control sequences across different encryption instances. To address these limitations, a one-dimensional logarithmic chaotic map and a plaintext-dependent encryption scheme with dynamic DNA point mutation are presented. The map combines logarithmic stretching with modular folding and exhibits persistent complex dynamics over the investigated parameter interval. Compared with the logistic map and the one-dimensional Cosine Logistic compound map (1DCLC), it shows a broader positive-Lyapunov-exponent region and more stable normalized permutation entropy, while derived binary sequences satisfy the adopted NIST SP 800-22 criteria. The encryption architecture integrates block-reversal permutation, dynamic DNA encoding, involutive point mutation, and chaotic XOR masking. A SHA-512 plaintext digest, a fresh 128-bit public nonce, and a 256-bit master key are processed through HMAC-SHA-256 and HKDF-SHA-256 to derive the chaotic parameters. Experiments on standard grayscale images show near-uniform ciphertext distributions, negligible adjacent-pixel correlations, near-maximal entropy, differential characteristics close to theoretical references, and pronounced one-bit key sensitivity. The MATLAB implementation achieves encryption and decryption throughputs of approximately 1 MB/s. Full article

The sponge construction serves as a fundamental design framework for hash functions and authenticated encryption algorithms. In differential cryptanalysis of large-state permutations underlying such algorithms, conventional approaches are constrained by state size and diffusion speed. The feature extraction capability of deep learning offers a potential alternative to mitigate these limitations. To improve the distinguishing performance of differential neural distinguishers against sponge-based algorithms, a methodology integrating data construction and network architecture optimization is proposed. Specifically, a multi-sample triplet input format is designed to preserve differential characteristics, and a convolutional block attention module is introduced to capture long-range dependencies along both the channel and spatial dimensions within the large-state permutation. Experimental evaluations were conducted on the Keccak and Ascon algorithms. For Keccak, the maximum distinguishable round number was identified as 3. At this round number, Keccak-p achieved full distinguishability (100% accuracy), while the sponge-based SHA-3-256 attained a distinguishing accuracy of 99.99%, improving upon the previous best result by 0.95 percentage points. For Ascon, the maximum distinguishable round number was 4, where Ascon-p achieved an accuracy of 54.85% with 64 sample pairs—the highest reported accuracy for this setting—while delivering comparable performance at the matched 32-pair setting (53.40% vs. 53.54% in prior work) with approximately one-twelfth of the training epochs; under the same setting, the sponge-based Ascon-Hash256 achieved an accuracy of 53.06%. These findings demonstrate the effectiveness of the proposed framework in enhancing neural distinguisher accuracy against sponge-based algorithms and offer an analytical approach for empirical security evaluation, with results qualitatively consistent with the indifferentiability bound of the sponge construction. Full article

Secure transmission and storage of color images remain challenging tasks due to strong inter-pixel correlations and high data volume. This work proposes a one-dimensional sine-tent-logistic-exponential map (STLEM) equipped with numerical boundary correction rules to mitigate finite-precision numerical degradation so as to enhance the unpredictability of chaos-driven cryptosystems. We benchmark STLEM against classic logistic, tent, and sine maps via Lyapunov exponents, autocorrelation, approximate entropy, permutation entropy, Lempel-Ziv complexity, and Kolmogorov–Sinai entropy. Bifurcation diagrams, the 0–1 test, and NIST statistical tests are further adopted to characterize its chaotic dynamics and randomness. Comparative results verify that STLEM achieves improved dynamical complexity and randomness performance. Built upon the proposed STLEM, this paper constructs a color-image encryption scheme that employs a 256-bit master key and two groups of chaotic parameters to produce key-related chaotic sequences. The cryptosystem integrates dynamic edge expansion, chaotic permutation, position-dependent adaptive DNA encoding, DNA-domain chained diffusion, and two successive row-column permutation phases. HMAC-SHA-256 is utilized to generate plaintext-aware initial conditions and perform ciphertext authentication prior to decryption. Experimental validations demonstrate complete plaintext recovery under valid secret inputs, while authentication rejects invalid keys and tampered ciphertexts. Ciphered images exhibit high information entropy, negligible adjacent-pixel correlations, and satisfactory number of pixel change rate (NPCR) and unified average changing intensity (UACI) metrics. Benefiting from a sufficiently large key space and $O\left(MN\log \right(MN\left)\right)$ computational complexity, the proposed scheme is resilient against brute-force attacks and well suited for secure color-image communication scenarios, rather than acting as a general-purpose replacement for standard block ciphers. Full article

MQ silicone resins are widely used reinforcing fillers for addition-curing liquid silicone rubber (LSR); however, establishing a quantitative composition–property relationship remains challenging because published data are fragmented across matrix chemistries, crosslinkers, catalyst systems and testing standards. Here we present a machine-learning-guided workflow integrating literature data mining, interpretable random-forest (RF) modelling and independent experimental validation for the design of MQ-reinforced LSR. An RF model trained on 55 curated literature points spanning RTV and LSR systems, using four physically motivated descriptors (MQ content, M/Q ratio, curing system and vinyl content), yielded leave-one-out coefficient of determination (R²) values of 0.741 for tensile strength (TS) and 0.730 for Shore A hardness (HA), with mean absolute errors of 0.63 MPa and 8.95 ShA, respectively. Feature-importance and partial-dependence analyses identified MQ content as the dominant descriptor. Guided by the model, seven LSR formulations (vinyl content 4 wt%, M/Q = 0.8, loading 5–35 wt%) were designed and fully characterised: the model reproduced the measured TS and HA for all seven formulations within the corresponding training mean-absolute-error tolerance, whereas elongation at break (EB), whose prediction is substantially weaker (LOO R² ≈ 0), was captured only as a qualitative trend with respect to MQ loading. This workflow demonstrates that a modest, curated literature dataset, mined by an interpretable ML model, can support formulation design and independent experimental validation—an efficient, low-cost alternative to trial-and-error optimisation. Full article

Respiratory emergencies create recurrent seasonal pressure on hospital services, yet reproducible forecasting research is often hindered by the effort required to retrieve, interpret, harmonize, and document public administrative data. Chile-ED-Resp is a curated secondary dataset and reproducible processing pipeline derived from the official Chilean Ministry of Health/DEIS dataset “Atenciones de urgencias de causas respiratorias por semana epidemiológica”, produced by the Sistema de Atención Diaria de Urgencias (SADU). Version 1.1.0 uses the closed 2022–2025 period and was built from the official Parquet snapshot retrieved on 30 August 2026. After restriction to hospitals and hospital emergency units (UEH), the curated long artifact contains 449,844 rows and 32 variables, representing 180 hospitals and 12 official respiratory cause labels. A complementary forecasting artifact contains 37,620 hospital–week rows and 38 variables on a fixed 209-position statistical-week calendar (52 weeks in 2022–2024 and 53 in 2025); 178 of 180 hospitals (98.9%) satisfy the predefined ≥$95\%$ target-coverage criterion. Acute upper respiratory infection is retained under its official Spanish label and acronym, IRA Alta. Missing source records remain missing and are distinguished from explicitly reported zeros. Precomputed target-derived predictors use outcomes available through the preceding week and are intended for rolling-origin one-week-ahead evaluation, not an unqualified fixed-origin forecast of the entire test year. Reproducibility is supported through source metadata, SHA-256 checksums, machine-readable descriptive audits, 15 validation checks, unit tests, and an example that recomputes predictors at each forecast origin. The resource provides a transparent foundation for forecasting, benchmarking, health-informatics teaching, and secondary analyses while preserving source provenance and licensing conditions. Full article

Objectives: Major depressive disorder (MDD) frequently co-occurs with substance use disorders, resulting in greater clinical severity and poorer outcomes. Sedative–hypnotic/anxiolytic agents (SHA) are commonly used to manage anxiety and insomnia, also in MDD. However, their long-term intake might lead to misuse and physical and cognitive complications. Trazodone, an antidepressant with sedative and anxiolytic properties, has been proposed as an option in SHA detoxification, but data on patients with comorbid MDD and SHA use disorders (MDD+SHA-UD) are limited. This study retrospectively evaluated the effects of a three-month treatment with extended-release trazodone in this population. Methods: Seventy-four outpatients with MDD+SHA-UD treated with extended-release trazodone were evaluated at baseline and after one and three months. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Secondary outcomes included anxiety (Hamilton Anxiety Rating Scale), sleep disturbances (Pittsburgh Sleep Quality Index), physical symptoms (Hamilton Depression–Anxiety subscales, 36-Item Short-Form Health Survey), cognitive functioning (Perceived Deficits Questionnaire–Depression, 5-item), and quality of life (World Health Organization-Five Well-Being Index). Results: Trazodone was associated with a significant reduction in depressive symptoms (p < 0.001), with 47.3% of the initial sample achieving remission at endpoint. Improvements were also observed in measures of anxiety, physical symptoms, subjective sleep quality, perceived cognitive functioning, and quality of life (all p < 0.001). Side effects were mild (25.7% at one month) and declined over time. Conclusions: In this uncontrolled preliminary study, extended-release trazodone was associated with significant improvements across multiple symptom domains in MDD+SHA-UD, warranting controlled investigation. Full article

Background/Objectives: Reported performance on public brain tumour MRI benchmarks is hard to interpret because of near-duplicate train/test overlap, ImageNet pretraining bias, and single-seed evaluation. We address this with a leakage-aware evaluation protocol and a compact model trained entirely from scratch. Methods: WICA-Net-M is a 2.47 M-parameter CNN whose gated Haar Discrete Wavelet Transform (DWT) separates low- and high-frequency components and fuses them through a learnable gate, complemented by Coordinate Attention. We evaluate it on the standard and image-level deduplicated splits of the Nickparvar brain tumour MRI dataset under a three-seed, leakage-aware protocol, benchmark it against five ImageNet-pretrained baselines and conduct a near-duplicate overlap audit against BRISC 2025. Results: WICA-Net-M reaches 99.42 ± 0.16% accuracy on the standard V1 split and 95.25 ± 0.32% accuracy/95.17 ± 0.31% macro F1 on the deduplicated V2 split, closely matching five ImageNet-pretrained baselines (95.17–95.67%) with fewer parameters, with sub-half-point differences across three seeds indicating comparable rather than superior accuracy. The audit identifies 861 exact SHA-256 pairs involving 857 of the 1000 BRISC test images against the full Nickparvar collection. Alongside perceptual-hash candidate pairs, this exact overlap shows that BRISC cannot serve as independent external validation. Conclusions: The descriptive 4.17-point V1-to-V2 difference reflects the combined effects of duplicate removal, class rebalancing, and altered sample composition, with none separable from public releases, though it shows that a scratch-trained compact model can approach pretrained performance on the controlled split. Patient-level leakage remains unresolved because patient identifiers are unavailable. Leakage-aware, multi-seed evaluation should be standard before clinical translation. Full article

Industrial IoT (Internet of Things) systems increasingly depend on sensor reports for monitoring, automation, and operational decisions. However, sensor faults or Byzantine behavior can produce inconsistent or missing reports. PBFT (practical Byzantine fault tolerance) can maintain consistent processing among replicas despite a bounded number of Byzantine faults. Its overhead grows when many factory sensors participate in one expanding consensus group. Processing complete sensor-event records also increases local computation as record size grows. This paper proposes independent PBFT groups using fixed-length SHA-256 sensor-event digests. Groups are formed according to production processes or sensor characteristics. Each group limits consensus participation, while digests keep consensus-command size fixed. Raspberry Pi experiments separated grouping benefits from digest-processing benefits. Fixed-size groups moderated aggregate replica-local computation growth across 10–100 logical sensors. Digest processing became more beneficial as original sensor-event records increased in size. A four-device deployment also maintained consensus under evaluated Byzantine backup and primary faults. These results indicate that the scheme can reduce PBFT processing burden in resource-constrained IIoT deployments. Full article