Spiking Neural Networks (SNNs) offer energy-efficient processing suitable for edge applications, but conventional sensor data must first be converted into spike trains for neuromorphic processing. Environmental sound–including urban soundscapes–poses challenges due to variable frequencies, background noise, and overlapping acoustic events, while most spike-based audio encoding research has focused on speech. This paper analyzes three spike encoding methods–Threshold Adaptive Encoding (TAE), Step Forward (SF), and Moving Window (MW)–across three datasets: ESC-10, UrbanSound8K, and TAU Urban Acoustic Scenes. Our multi-band analysis shows that TAE consistently outperforms SF and MW in reconstruction quality, both per frequency band and per class across datasets. Moreover, TAE yields the lowest spike firing rates, indicating superior energy efficiency. For downstream environmental sound classification with a standard SNN, TAE also achieves the best performance among the compared encoders. Overall, this work provides foundational insights and a comparative benchmark to guide the selection of spike encoders for neuromorphic environmental sound processing.
Cybersecurity taxonomies comprise complex relational dependencies and detailed textual descriptions, forming heterogeneous hierarchical knowledge graphs. Modeling such systems requires jointly leveraging semantic content and graph topology, particularly when new entities are continuously introduced. This work addresses inductive link prediction in dynamic cybersecurity taxonomies, focusing on associating previously unseen software weaknesses with established attack patterns under limited or absent relational context. In such scenarios, new weaknesses appear as isolated nodes, constraining structural information at inference. Although recent approaches rely on language models for textual encoding, they often underutilize the graph’s relational structure. To address this, the proposed method adopts a two-stage strategy: candidate links are generated through textual similarity to existing weaknesses, then an adapted SEAL+ framework learns topology-aware representations enriched with PCA-reduced semantic embeddings to discriminate links. Experimental evaluation across two inductive settings–absent and limited relational context—demonstrates that jointly modeling structural dependencies and textual semantics consistently outperforms text-only baselines, with ROC-AUC improvements of 3.2–4.5
Tabular classification is a central supervised learning problem in high-stakes domains. While tree ensembles are strong baselines and deep tabular models sometimes help, standard ensembles typically use global fusion (e.g., uniform averaging), implicitly assuming constant expert reliability across the input space. We propose a lightweight, architecture agnostic dynamic gating mechanism that performs instance-wise mixture weighting using raw covariates, expert probability vectors, and two confidence features (entropy and margin). Across 14 benchmark datasets, dynamic gating improves mean-ensemble accuracy from 0.9047 to 0.9149 in a heterogeneous pool ( +0.0102 ) and from 0.9082 to 0.9126 in a tree-only pool ( +0.0044 ), with 11/14 dataset wins in both settings. Aggregate paired Wilcoxon tests confirm significance ( p=2.44× 10^-4 for heterogeneous, p=1.07× 10^-2 for tree-only), showing that compact instance-wise weighting improves robustness without architecture-specific fusion.
Contextual audience construction has long relied on rule-based keyword and taxonomy matching, often applied as touch-based frequency rules. As third-party tracking weakens, contextual audience construction gains prominence, while embedding-based semantic cohorting emerges as a plausible alternative to represent intent under privacy constraints. In most workflows, advertisers provide targeting terms, and platforms determine how inputs are translated into deliverable cohorts. This motivates a practical product question: do keyword/touch-based cohorts reflect stated intent, and can embedding-based cohorting improve suitability without unacceptable scale loss? Using the Microsoft News Dataset (MIND), we compare classical touch-based cohorting versus semantic cohorting through controlled offline experiments. Under strict leakage prevention and chronological holdout, we audit resulting audiences using structural diagnostics (constraint violation rate, topic drift, coverage, concentration) and a blind held-out click proxy. Touch-based cohorts maximize reach, but exhibit a higher negative-topic association. Centroid-based semantic cohorting reduces negative-topic association by 27 K=10,000 while reducing inventory coverage. Brief-based intent embeddings can increase topical drift, whereas centroid cohorts improve alignment at larger audience sizes. Human-authored briefs under-perform empirical seed-based definitions. The contribution is primarily conceptual and evaluative: it frames contextual targeting as an audience-construction problem and introduces a structural audit framework for comparing the cohorts produced by different intent-encoding choices.
The rapid progress of large language models (LLMs) has unlocked significant capabilities across diverse domains, yet has also raised critical concerns regarding the trustworthiness and safety of these systems. Although many studies address isolated risks such as hallucinations, bias, or vulnerability to adversarial prompts, comprehensive frameworks for systematically evaluating LLM trustworthiness remain scarce. This research proposes a novel evaluation framework to benchmark LLMs along key dimensions: honesty, bias mitigation, calibration, consistency, and resistance to deception. Drawing inspiration from recent safety benchmarks, we design specialized evaluation protocols for each dimension and compute aggregate trustworthiness scores using weighted combinations of individual metrics. Experiments on prominent LLMs demonstrate significant variability in performance across dimensions, highlighting strengths and weaknesses of current models. Our results underscore the need for multidimensional safety evaluations and provide practical tools for developers, policymakers, and researchers looking to build and deploy more trustworthy AI systems.
