Early and accurate analysis of breast cancer is critical for improving patient outcomes. Ultrasound imaging is widely used for breast tumor screening due to its safety, accessibility, and low cost. We propose DGTN (Diffused Graph-Transformer Network), a lightweight, end-to-end deep learning model that jointly performs breast tumor segmentation and multi-class classification (benign, malignant, and normal) from ultrasound images. DGTN integrates Graph Convolutional Networks (GCNs) and Transformer encoders through a bidirectional diffusive attention mechanism and a learnable gating strategy, enabling structured spatial information and global contextual features to co-evolve. We evaluate DGTN on the public BUSI breast ultrasound dataset using balanced sampling and a joint cross-entropy and Dice loss. The model achieves 68.8% classification accuracy and a Dice score of 0.6227. While its performance remains below that of recent state-of-the-art pipelines, DGTN offers a favorable trade-off between accuracy and computational efficiency within a single unified framework. Ablation experiments indicate that both diffusive attention and gating contribute meaningfully to performance (paired t-test across five cross-validation folds, p < 0.05, with large paired effect sizes, d ≈ 1.0–1.4); because this test is based on only five folds, the result should be interpreted as indicative rather than conclusive, and we report it alongside fold-level effect sizes rather than as a stand-alone confirmation of significance. To the best of our knowledge, this work represents one of the first applications of diffusive graph-transformer co-learning to breast ultrasound imaging, demonstrating the potential of graph-enhanced attention for efficient multi-task medical image analysis.

Objectives— We establish and rigorously evaluate an Android malware detector that optimizes not only discrimination (how well samples are ranked) but also reliability: (i) probabilities that correspond to empirical risks and (ii) explicit, distribution free uncertainty quantification. Our goals are to (1) benchmark strong tabular baselines on a canonical CSV corpus; (2) calibrate scores so a value like 0.9 aligns with “roughly 90% malicious”; and (3) add inductive conformal prediction (ICP) to provide finite-sample coverage guarantees and a principled abstain option. Methodology— The pipeline: (i) ingest and sanitize a dataset of 15,036 applications with static features; (ii) encode mixed datatypes via one-hot and factorization; (iii) train Logistic Regression (LR), Random Forest (RF), and XGBoost (GBDT); (iv) report ROC–AUC, PR–AUC, F1, Accuracy, Brier score; (v) apply post hoc calibration (Platt, Isotonic) and compute Expected Calibration Error (ECE) with reliability diagrams including Wilson intervals; and (vi) deploy ICP to return prediction sets achieving target error α ∈ {0.10,0.05,0.01}, yielding selective automation. Dataset— A text-only (CSV) Android corpus of static attributes (manifest/permissions/API-like proxies) with a binary label (benign/malicious). Preprocessing drops identifiers, coerces types, imputes missing values, applies de-duplication, and uses an 80/20 stratified split with a 20% calibration carve-out from training (effective 64/16/20 train/cal/test). Results— RF/GBDT provide the best ranking. Calibration consistently lowers ECE/Brier and aligns reliability curves with the identity line; we visualize the effect for RF. ICP tracks the nominal target coverage closely with average set size near one, indicating selective abstentions. We include a temporal slice analysis to illustrate robustness under modest distribution shift, plus an operational thresholding example linking (α,t) to analyst load. Applications— Calibrated probabilities support auditable thresholds in app stores and enterprise gateways; ICP provides a knob for predictable abstentions under finite-sample guarantees. The approach is model-agnostic, simple to reproduce, and easy to retrofit in static or hybrid pipelines. Availability— Scripts for preprocessing, calibration, ICP, metric computation, and figure generation, along with CSV/PNG artifacts referenced in the paper, are provided in the artifact bundle.

We study Vector+SQL hybrid retrieval under structured filtering conditions and propose a reproducible Top-K evaluation framework for uniformly analyzing the quality and tail latency of different retrieval strategies. We construct controllable selectivity workloads on three text domains: arXiv, news, and movies, and compute exact ground truth within the same filter candidate set to ensure fair comparison. Experiments uniformly evaluate exact prefilter, ANN post-filter, two-stage probing, partition-aligned retrieval, and adaptive routing, and report Recall@20 and p50/p95/p99 latency. The results show that filter selectivity and predicate structure significantly affect the quality--latency trade-off in hybrid retrieval: exact prefilter has the highest accuracy but the largest tail latency; ANN post-filter is faster, but recall drops significantly under strict filtering; adaptive routing achieves more robust overall performance on the evaluated workloads. These results demonstrate that reporting only average latency is insufficient to reflect the true system cost of hybrid retrieval. Our report provides a systematic framework for evaluating, designing, and deploying hybrid retrieval strategies under filtering conditions, accounting for correctness, tail latency, and reproducibility.

SadaColorDataset (SCD) is a publicly available image dataset designed to support research on robust color recognition and illumination-related color variation in real mobile captures. The dataset contains 10,843 photographs of nine physical color papers (Black, Blue, Gray, Orange, Pink, Purple, Sky Blue, White, and Yellow) recorded under four everyday lighting conditions: Fluorescent, Indoor, Indoor Night, and Sunlight. All images were captured using an Infinix NOTE 40 smartphone camera (108 MP) with a simple, repeatable setup intended to reflect practical conditions rather than laboratory calibration. For each color–illumination setting, multiple images were collected to cover natural variability due to exposure, white balance, shadows, and reflections. During acquisition, the paper was placed on a ground surface and the phone was mounted on a tripod; the viewpoint was varied by moving the tripod to different positions and orientations. However, because explicit angle labels were not recorded or reliably recoverable from the released file structure/metadata, SCD does not provide calibrated or discrete “angle IDs,” and it should be treated as a dataset with unlabeled viewpoint variation. Along with the images, we release machine-readable metadata and summary files that describe image counts across colors and illuminations and provide basic color statistics (e.g., RGB/CIELAB-derived measures) to facilitate reproducible analysis. SCD is distributed under a public license and is intended for benchmarking illumination robustness, dataset shift, and color stability in mobile vision pipelines.