Journal of Global Engineering Review: Advancing multidisciplinary engineering research through global collaboration. Peer-reviewed publications since 2023.
Latency–Throughput Tradeoffs of ONNX Runtime, TensorRT-LLM, vLLM, and Triton: An Empirical Comparison on 1B–3B Parameter LLM Inference
Small-scale large language models with one to three billion parameters have emerged as a practical choice for latency-sensitive deployment, offering a balance between linguistic capability and operational cost. Serving these models efficiently depends heavily on the choice of inference engine, yet published comparisons often target 7B-scale or larger workloads and report metrics in isolation. This paper presents an empirical comparison of four widely adopted inference engines—ONNX Runtime, TensorRT-LLM, vLLM, and NVIDIA Triton configured with the TensorRT-LLM backend—on three open-weight checkpoints (TinyLlama-1.1B, Llama-3.2-1B, and Llama-3.2-3B). Using prompt distributions drawn from ShareGPT, LMSYS-Chat-1M, and HumanEval, we measure time-to-first-token, inter-token latency, sustained throughput across batch sizes from 1 to 64, GPU utilization, and peak memory footprint on an NVIDIA A100 40GB and an RTX 4090 24GB. Results indicate that TensorRT-LLM attains the lowest single-request latency (22.6 ms time-to-first-token on the 1.1B checkpoint), while vLLM delivers comparable throughput at large batch sizes with a 13.8% smaller memory footprint at the 3B scale due to its paged KV-cache layout. ONNX Runtime trails on both dimensions yet remains competitive for cross-platform deployment. The findings yield quantitative deployment guidance for cost-aware serving of small-scale language models.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Does an LLM-Based Feedback Agent Move the Needle? An Empirical Study of Student Correctness and Hint Dependency on the ASSISTments 2017 Interaction Logs
Large language model (LLM) feedback agents are increasingly proposed as substitutes for system-authored hints in intelligent tutoring environments, yet most existing evaluations rely on rubric-based offline judgments or small-scale human ratings rather than learner-behaviour evidence drawn from real interaction logs. This study presents an empirical analysis of three production-grade LLM feedback agents — GPT-4o-mini, Llama-3-70B-Instruct, and Gemini-1.5-Flash — evaluated on the ASSISTments 2017 click-stream corpus, which contains 942,816 records, 1,709 students, 3,162 problems, and 102 knowledge components. Three research questions are examined: whether LLM-generated feedback raises subsequent correctness, whether it reduces hint dependency, and whether automatic feedback-quality scores correlate with observed behavioural change. The evaluation pipeline pairs Prometheus and G-Eval as offline judges with a context-aware attentive knowledge-tracing counterfactual predictor, allowing each intervention to be scored both for textual quality and for predicted learning trajectory. Aggregate results show a moderate average correctness gain of 4.8 percentage points for GPT-4o-mini over the system-authored hint baseline, concentrated in conceptual knowledge components. Hint-request rates drop by 11.4 percent under GPT-4o-mini, while Llama-3-70B and Gemini-1.5 produce smaller and less stable shifts. The correlation between feedback-quality score and behavioural gain is moderate (Spearman ρ = 0.41, 95% CI [0.34, 0.47]), indicating that high rubric scores partially but not fully explain learner impact. Findings provide cautious empirical support for LLM feedback agents as supplements to template hints in middle-school mathematics tutoring and highlight open questions around procedural-knowledge components and ability-stratified responsiveness.
LLM feedback agent; automated formative feedback; knowledge tracing; Assessment’s
Applied Artificial Intelligence Across High-Stakes Domains: A Systematic Review of Methodological Foundations, Trustworthiness Pillars, and Cross-Domain Deployment Patterns
Artificial intelligence (AI) and machine learning (ML) have moved decisively from controlled laboratory benchmarks into operational, consequential decision systems spanning finance, security, healthcare, sustainability, autonomous systems, and a widening range of human-centered services. This breadth has produced a fragmented literature in which closely related methodological advances are repeatedly rediscovered under domain-specific vocabularies, while genuinely cross-cutting concerns of trustworthiness are addressed unevenly. This paper presents a systematic review of 196 recent studies (2023-2026) that organizes the field along three orthogonal axes: methodological foundations (graph and relational learning, deep sequence and temporal models, multimodal and sensor fusion, large language models and agents, ensemble and classical learning, and reinforcement and optimization learning); trustworthiness pillars (privacy-preserving learning, fairness, explainability, and robustness and security); and seven application domains. We synthesize how methods migrate across domains, where trustworthiness requirements are satisfied or neglected, and how deployment patterns differ under the constraints of each setting. A quantitative coverage analysis shows that financial risk and RegTech (24%) and healthcare and biomedicine (22%) dominate the corpus, that robustness and security are addressed far more frequently than fairness, and that privacy-preserving learning and explainability are concentrated in regulated domains. We discuss recurrent design tensions-accuracy versus interpretability, utility versus privacy, and centralized performance versus federated governance-and identify open challenges, including the trustworthiness of autonomous multi-agent systems, evaluation under distribution shift, and the uneven treatment of fairness outside credit scoring. The review is intended as a structured map for practitioners selecting methods under real deployment constraints and for researchers locating durable gaps.
artificial intelligence; machine learning; systematic review; trustworthy AI; graph neural networks; large language models; federated learning; explainability; anomaly detection; cross-domain applications
AI-Driven Anomaly Detection and Risk Early-Warning Across Domains: A Systematic Review of Methods, Architectures, and Deployment Challenges
Anomaly detection and risk early-warning have become a unifying computational problem that recurs, with remarkably similar structure, across finance, cybersecurity, healthcare, regulatory compliance, recommendation, computer vision, sustainability, and large-scale systems operations. Although these domains differ in vocabulary and consequences, the underlying task is invariably the same: to learn what is normal from heterogeneous and often imbalanced data, to flag deviations with adequate lead time, and to do so in a manner that practitioners can trust and lawfully deploy. This paper presents a systematic review of one hundred and ninety-six recent studies that, taken together, trace the evolution of this field from classical feature engineering and ensemble classifiers to graph neural networks, deep temporal models, foundation models, and agentic architectures. We propose a unifying taxonomy that organizes methods along five layers—data and feature foundations, relational and graph models, temporal and deep models, ensemble and class-imbalance strategies, and foundation models and agents—over a common substrate of cross-cutting concerns comprising explainability, fairness, calibration, privacy preservation, and deployment monitoring. We then synthesize cross-domain evidence to identify which method families have proven most effective for which classes of risk, quantify the distribution of research effort across application areas, and examine recurring deployment obstacles including label scarcity, concept drift, adversarial manipulation, regulatory constraints on data sharing, and the calibration of warning thresholds against operational lead-time requirements. The review concludes that the most durable advances are not isolated algorithmic gains but architectural patterns—multi-source fusion, relational modeling, privacy-preserving collaboration, and explanation-aware scoring—that transfer across domains, and it outlines open challenges in robustness, governance, and the safe use of generative and agentic models for high-stakes detection.
anomaly detection; risk early-warning; graph neural networks; ensemble learning; deep learning; large language models; federated learning; differential privacy; explainable AI; fairness; concept drift; systematic review.
Memory Poisoning Propagation Dynamics and Defense Strategy Comparison in Multi-Agent LLM Collaboration: An Empirical Study on AutoGen and LangGraph Frameworks
The rapid adoption of multi-agent large language model (LLM) systems has elevated shared memory into a primary attack surface: a single corrupted entry can propagate across collaborating agents, contaminate downstream reasoning, and degrade task accuracy in ways that are difficult to localize after the fact. This paper presents a controlled empirical study of memory poisoning propagation dynamics in two widely used agentic orchestration frameworks, AutoGen and LangGraph. We construct a five-agent collaborative environment spanning code generation, tabular data analysis, and decision-reasoning tasks, inject five categories of synthetic poisoned entries into the shared memory at a controlled ratio ranging from 5% to 50%, and trace contamination spread through the communication graph using a custom propagation tracker. We compare four representative defense strategies — signature verification, consensus voting, time-based memory decay, and trusted-source weighting — both in isolation and as a hybrid ensemble. At a 30% poison ratio the hybrid defense raises task accuracy from a no-defense baseline of 54.83% to 86.27%, attains a poison-detection F1 of 0.8513, and contains 82.65% of poisoned entries within three communication rounds. Ablation, sensitivity, and case-study analyses confirm that each component contributes non-trivially and that the hybrid is robust across both AutoGen and LangGraph topologies. The fully synthetic setup makes every result reproducible without external datasets.
multi-agent LLM systems, memory poisoning, propagation dynamics, AutoGen, LangGraph, defense strategy comparison
Explainable Risk Stratification of Polypharmacy-Related Adverse Outcomes in the Elderly via Temporal Feature Engineering and Rule-Augmented Gradient Boosting
Polypharmacy in community-dwelling older adults is associated with a sharp rise in adverse drug events, falls, emergency department visits, and avoidable hospitalization, yet most existing risk stratification tools rely on static cross-sectional inputs that overlook the temporal dynamics of prescription churn and drug-drug interaction onset. This paper introduces a rule-augmented gradient boosting framework that combines time-windowed prescription features with curated pharmacological knowledge to stratify elderly patients into low, medium, and high adverse-outcome risk strata. Records from the FDA Adverse Event Reporting System (FAERS) are linked at a patient-archetype level with MIMIC-IV medication trajectories and a NHANES elderly subset, yielding 184,627 patient-episodes after harmonization and deduplication. We construct 142 temporal features-rolling prescription counts, drug-class entropy, anticholinergic burden ramp-up, concurrent-prescription synchronicity, and CYP-substrate co-exposure densities-and inject Beers Criteria and STOPP/START expert rules as soft constraints through a rule-aware loss term within the LightGBM objective. SHAP-based attribution provides clinician-readable explanations at both global and patient-instance levels. The pipeline attains AUROC = 0.9127, F1 = 0.8261, and Brier score = 0.0814, outperforming logistic regression, vanilla LightGBM, XGBoost, and a temporal convolutional network baseline by between 2.31% and 4.46% absolute AUROC. Ablation shows that temporal features contribute 1.94% AUROC and rule injection contributes 1.27%. The framework supports clinical deployment with sub-50 millisecond per-patient inference and stable performance across age and sex subgroups.
Polypharmacy, Adverse drug events, Explainable AI, Gradient boosting, Temporal feature engineering, Rule augmentation
Cross-Lingual Detection of Implicit Compliance Violations in Cross-Border Contracts: Few-Shot LLM, Legal-BERT, and Rule-Augmented Hybrid
The proliferation of cross-border commercial transactions has produced contractual portfolios that span multiple jurisdictions and natural languages, creating an acute need for automated tools that can detect compliance violations whose presence is implicit rather than explicit. Most existing systems focus on English-language documents and on overt clause matching, leaving implicit violations such as concealed jurisdiction shifts, unstated data-flow permissions, and indirect intellectual-property assignments largely untreated in a multilingual setting. This paper presents a comparative study of three detection paradigms on a curated four-language contract dataset covering English, Chinese, Japanese, and Korean: a rule-and-dictionary baseline, multilingual encoder fine-tuning with Legal-BERT and XLM-RoBERTa, and few-shot in-context learning with GPT-4 and Claude. We further introduce a rule-augmented hybrid that fuses neural confidence with rule-based vetoes through a calibration-aware decision layer. Across 8,624 annotated clauses covering six implicit-violation categories, the hybrid attains a macro-F1 of 0.8261 averaged across languages, outperforming the best single-paradigm system by 3.97 absolute points and the dictionary baseline by 23.18 points. Ablation studies show that the rule-veto layer contributes 1.84 points of F1 and that retrieval-anchored few-shot example selection adds another 1.27 points. The findings indicate that no single paradigm dominates across all languages or violation types and that disciplined fusion is the more reliable engineering choice for high-stakes cross-border compliance review.
implicit compliance detection, cross-lingual NLP, contract analysis, Legal-BERT, few-shot in-context learning, rule-augmented hybrid
A Multi-Layer Graph Attention Network for Tail Risk Spillover Path Identification Across U.S. Equity, Credit, and FX Markets
Cross-market financial stress propagates through pathways that are simultaneously heterogeneous, directional, and concentrated in the tails of joint return distributions, making contagion difficult to model with standard correlation-based or single-layer network approaches. This paper presents a Multi-Layer Heterogeneous Graph Attention Network (ML-HGAT) for tail risk spillover path identification across U.S. equity, high-yield credit, and foreign exchange markets. The framework couples three components: a quantile-regression CoVaR estimator and Dynamic Conditional Correlation GARCH that supply intra-layer tail-dependence edges, a rolling-window Granger causality network that supplies inter-layer directional edges, and a type-aware heterogeneous graph attention encoder that aggregates evidence across layers and emits both a contagion-event probability and a Top-k spillover path. Empirically, ML-HGAT is trained and evaluated on twenty years of daily observations drawn from FRED macroeconomic and liquidity series, CRSP equity returns, ICE-BofA U.S. High-Yield indices, and Federal Reserve liquidity indicators, spanning the 2008 global financial crisis, the 2011 European sovereign episode, the 2020 COVID-19 dislocation, the 2022 inflation shock, and the 2023 regional banking stress. Against six baselines, ML-HGAT attains an F1 of 0.8413, an AUC of 0.9237, a path hit-rate-at-ten of 0.7682, and a mean average precision of 0.6914, improving on the strongest single-layer GAT baseline by 3.46 absolute points in F1 and 5.58 points in path hit rate. Ablation analysis confirms that the inter-layer directional overlay and the tail-dependence intra-layer edges contribute additively to identification accuracy, and that the type-aware attention head is responsible for the bulk of the improvement during distinct stress regimes.
tail risk spillover, multi-layer graph attention network, cross-market contagion, CoVaR, Granger causality, financial stress
Multi-source heterogeneous data fusion: a methodological survey from early, late, and hybrid strategies to attention-based architectures
Modern decision systems rarely operate on a single, clean stream of data. Sensor arrays in autonomous vehicles, multi-omics records in cardiology, transaction logs combined with social signals in retail, and asynchronous text-tabular-graph evidence in finance all share the same underlying problem: how to fuse heterogeneous sources into a coherent representation that improves downstream estimation. This paper presents a methodological survey of the dominant fusion paradigms—early (feature-level), late (decision-level), hybrid, and attention-based fusion—then connects them to graph fusion and to the privacy- and fairness-aware variants now common in production systems. We construct a unified notation, classify 163 representative recent studies, and complement the survey with a four-scenario controlled experiment covering sensor fusion under degraded perception, multimodal medical risk prediction, multi-source financial early warning, and dynamic graph fusion on transaction networks. Across these scenarios, attention-based hybrid fusion outperforms early-only and late-only baselines on accuracy and calibration metrics by margins of 3.4–8.7 percentage points in F1 and 6–14 percentage points in AUPRC, while late fusion remains attractive when source reliability varies sharply across time. We discuss residual gaps and outline the design space for future fusion architectures combining retrieval, graph attention, and privacy-preserving training.
multi-source data fusion; multimodal learning; attention mechanism; sensor fusion; graph neural networks; federated learning; differential privacy; methodological survey.
Few-Shot, Transfer, and Meta-Learning in Naturally Data-Scarce Vertical Domains: A Cross-Sector Methodological Survey from Rare-Disease Diagnosis, Emerging-Financial-Product Risk Management, Autism Spectrum Variability, and Low-Resource Language Processin
Vertical applications of artificial intelligence increasingly run into a structural barrier that does not yield to better engineering alone: the underlying population is small, heterogeneous, or simply not well represented in any historical archive. Rare-disease cohorts assemble dozens to a few hundred patients across continents; freshly issued credit instruments such as buy-now-pay-later receivables, climate-linked structured notes, and digital-asset derivatives have at most a few quarters of post-origination behaviour; behavioural signals collected from children on the autism spectrum vary so much across individuals that a model trained on one child often fails on the next; and several hundred living languages have no standard parallel corpus large enough to train conventional encoders. We refer to these settings collectively as naturally data-scarce verticals, and we ask whether the few-shot, transfer, and meta-learning literature accumulated across otherwise unrelated fields exposes a transferable methodological pattern. To answer this we curated 142 recent studies that span clinical decision support, financial-network monitoring, autism-skill acquisition, multilingual healthcare communication, privacy-preserving multi-institution learning, multimodal fusion, and explainable risk attribution. We extract a common backbone — pretrain → adapt → calibrate → audit — and operationalise it through a unified evaluation protocol that combines four simulated benchmarks. Across 36 model–scenario combinations, parameter-efficient adaptation with prototype heads achieves the best accuracy–stability trade-off when per-class support is below 20, while metric-based meta-learning dominates when classes are rare but per-class support is moderate. Calibration matters as much as raw accuracy: post-hoc temperature scaling combined with Bayesian last-layer recalibration reduced the expected calibration error from 0.187 to 0.046 on the financial-instrument benchmark. We close with a discussion of fairness, privacy, and deployment audit obligations and sketch a research agenda built around shared benchmarks, federated meta-learning, and lifecycle-aware reliability accounting.
few-shot learning; transfer learning; meta-learning; rare-disease diagnosis; emerging financial products; autism spectrum disorder; low-resource languages; parameter-efficient adaptation; calibration; federated learning; data-scarce verticals
Concept drift monitoring and continual learning in production AI systems: an empirical cost–benefit comparison of detection methods and adaptation strategies
Production machine learning systems face a persistent operational challenge: the distribution of input features and the conditional distribution of labels can shift over time, eroding the predictive performance that motivated deployment. This paper conducts an empirical comparison of three widely used concept drift detectors—ADWIN, DDM, and Page–Hinkley—paired with two adaptation strategies, incremental learning and full retraining. Using two publicly available streaming benchmarks (Electricity and SEA) augmented with a synthetic noisy variant, we construct a cost–benefit framework that jointly accounts for predictive accuracy, drift-response latency, computational cost, and false-alarm rate. Across 60 controlled trials, ADWIN paired with incremental learning achieved the highest accuracy-to-cost ratio on stationary segments and gradual drifts, while DDM combined with periodic retraining reacted most decisively to abrupt shifts at the cost of higher compute. Page–Hinkley provided a useful middle ground when budget is moderately constrained. No single configuration dominated across regimes; engineers should select detectors based on the dominant drift profile of their pipeline.
concept drift, continual learning, online machine learning, production AI monitoring, cost–benefit analysis
A tri-axis reliability evaluation framework for large language models in high-stakes decision making: an empirical study on credit approval, clinical decision support, and compliance review
The deployment of large language models (LLMs) in high-stakes decision settings such as consumer credit approval, clinical decision support and regulatory compliance review has outpaced the development of methodologically grounded reliability assessment tools. Existing benchmarks tend to focus on a single dimension—accuracy on closed-form tasks, or membership-inference resistance, or demographic parity on tabular data—while leaving a structural gap between the intuitions of practitioners in regulated industries and the metrics actually reported by model developers. This paper proposes a tri-axis reliability evaluation framework that jointly measures hallucination, fairness and robustness for LLM-driven decisions, and applies the framework empirically to three scenarios constructed on top of MIMIC-III, LendingClub and SEC EDGAR. We formalise twelve operational metrics across the three axes, define an aggregation rule with axis-level guard floors, and instantiate the framework with three open-weight models and one frontier model. Results show that no model dominates on all three axes; the model with the lowest hallucination rate exhibits the largest disparity gap on protected attributes, while the model with the strongest robustness profile under prompt perturbations is the most cautious in clinical answer generation. We discuss what the gap-pattern says about deployment policy and outline how the framework can be extended to multi-agent settings and to retrieval-augmented architectures.
large language models, reliability evaluation, hallucination, algorithmic fairness, robustness, credit risk, clinical decision support, regulatory compliance
A transferability benchmark of graph neural networks for cross-domain risk contagion identification: GAT, GraphSAGE, and GCN across financial, cyber, and epidemiological networks
Risk contagion across heterogeneous networks—shock propagation between financial markets, lateral movement of intrusions across enterprise hosts, and pathogen spreading along contact graphs—shares a common formal structure but is rarely studied jointly. We construct a unified benchmark that aligns three publicly derived graph corpora (an interbank-style transaction subgraph, an authentication-flow subgraph from enterprise telemetry, and an SNAP contact graph augmented with synthetic exposure labels) under a single node-classification protocol, and run a controlled comparison of three widely deployed graph neural networks: Graph Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks (GAT). Models are evaluated in three regimes: in-domain training, cold-start cross-domain transfer with frozen encoders, and adapter-based fine-tuning on a 5% target sample. Across 27 (model × source × target) configurations, GAT attains the highest in-domain F1 and the most stable transfer in financial→cyber pairs, GraphSAGE leads on the epidemiological target due to neighborhood-sampling robustness on long-tailed degree distributions, and GCN shows the steepest performance gap when transferred without adaptation. We discuss when neighborhood attention helps versus when sampling-based aggregation suffices, and present an analysis of feature drift and label-noise sensitivity. The benchmark, while not exhaustive, offers a reproducible reference point for cross-domain GNN deployment in risk-sensitive applications.
Graph neural networks; risk contagion; cross-market financial spillover; lateral movement detection; epidemic spreading; transfer learning; benchmark.
A comparative synthesis of federated learning and differential privacy synergy: cross-domain insights from finance and healthcare deployments
The simultaneous demand for predictive accuracy and strict confidentiality has positioned federated learning (FL) and differential privacy (DP) as the two leading paradigms for privacy-preserving artificial intelligence. While each has been studied extensively in isolation, the operational synergy between them — and the way that synergy plays out under finance and healthcare regulatory regimes — remains under-characterized. This paper develops a comparative study of FL and DP cooperation across the two domains, focusing on three points of friction: how the privacy budget ε interacts with model utility under cross-silo communication, how communication overhead shapes the choice of DP composition strategy, and how compliance obligations from HIPAA, GLBA, and the GDPR shift the deployment surface. We construct a controlled experimental scaffold using a synthetic financial credit-default corpus and a synthetic hospital electronic-health-record corpus, design five competing pipelines, and evaluate them on a unified set of utility, privacy, and communication metrics. FL combined with central-DP achieved the best utility–privacy balance for healthcare risk prediction, while FL with local-DP showed a wider utility gap on the financial side under class imbalance. The study positions FL–DP synergy as an engineering design space rather than a single fixed recipe.
Federated learning, Differential privacy, Privacy budget, Financial risk modeling, Electronic health records, Regulatory compliance, Privacy–utility tradeoff.
Passage, Sentence, or Proposition? An Empirical Comparison of Retrieval Granularity Effects on LLM Answer Accuracy in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has become a dominant paradigm for grounding large language model (LLM) outputs in external knowledge. While extensive research has focused on retriever architectures and generation strategies, the choice of retrieval granularity—the textual unit indexed and retrieved—remains insufficiently studied. This paper presents a controlled empirical comparison of four retrieval granularity levels: document, passage (100-word window), sentence, and proposition. Experiments are conducted across three open-domain question answering benchmarks (Natural Questions, TriviaQA, and HotpotQA) using two representative dense retrievers (DPR and Contriever) paired with LLaMA-2-7B-Chat as the reader. Results indicate that finer-grained retrieval units consistently improve retrieval recall, with proposition-level indexing achieving up to 6.8 absolute points higher Recall@20 than passage-level on Natural Questions under DPR. End-to-end answer accuracy follows a similar trend for single-hop factoid questions, where proposition-level retrieval yields the highest Exact Match scores. On multi-hop questions in HotpotQA, this advantage diminishes and passage-level retrieval produces comparable or slightly superior accuracy, suggesting that broader contextual units are beneficial when reasoning across multiple evidence pieces. These findings provide practical guidance for RAG pipeline design: retrieval granularity should be selected in accordance with question complexity, and no single granularity level dominates across all conditions.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
A Comparative Evaluation of URL-Sharing, Content Similarity, and Temporal Synchronicity Signals for Detecting Coordinated Inauthentic Behavior in Multilingual Political Discourse
Coordinated inauthentic behavior (CIB) on social media platforms poses a persistent threat to the integrity of political discourse. While multiple signal types have been proposed to detect such coordinated manipulation, no prior study has systematically compared their detection efficacy across languages. This paper presents a comparative evaluation of three widely adopted coordination signals — URL co-sharing, content cosine similarity, and temporal posting synchronicity — applied to English and Russian political CIB datasets derived from the Twitter Information Operations Archive and the Clemson Internet Research Agency corpus. For each signal type, we construct a coordination network, apply Leiden community detection, and measure precision, recall, and F1 against ground-truth information operation labels. Our results indicate that URL co-sharing achieves the highest F1 in both languages (0.802 in English, 0.787 in Russian), while content similarity suffers a 16.0% F1 degradation in Russian attributable to morphological complexity. Temporal synchronicity yields the most language-stable performance with only a 2.3% cross-language gap. A late-fusion combination of all three signals reaches an F1 of 0.891 in English and 0.824 in Russian. Parameter sensitivity analysis reveals that optimal time-window configurations differ across languages and signal types. These findings offer practical guidance for platform governance teams selecting detection indicators for multilingual CIB monitoring.
LLM-Grounded Visual Alerts for Humanitarian Food-Price Crisis Dashboards: Reproducible Anomaly Detection on WFP VAM Market Data
Humanitarian non-governmental organizations need food-price dashboards that turn volatile market data into alerts that field teams, donors, and accountability officers can read quickly. This paper presents and evaluates a reproducible food-price anomaly detection workflow for an NGO dashboard built on World Food Programme Vulnerability Analysis and Mapping market data. The experiment uses a pre-2022 public WFP food-price CSV subset covering Afghanistan, Benin, Burkina Faso, Burundi, Chad, the Democratic Republic of the Congo, Ethiopia, Kenya, and Lebanon from 2000 to 2021. After deterministic filtering, the dataset contains 76,822 valid retail price records, 526 eligible market-commodity time series, and 12,813 held-out observations for 2019-2021. A controlled anomaly benchmark injects 1,239 reproducible price shocks into the test period while preserving the original clean series for operational alert review. Seven detectors are evaluated: percent-change, exponentially weighted moving average residuals, six- and twelve-month rolling median absolute deviation, seasonal median absolute deviation, trend-seasonal residuals, and a rank ensemble. The exponentially weighted moving average detector achieves the strongest F1 score, 0.5146, with precision 0.8510, recall 0.3688, AUROC 0.9303, and AUPRC 0.7116. The paper also implements a bounded LLM-style warning-copy generator that verbalizes only measured variables, producing map labels, severity tags, and donor-facing summaries without adding unsupported claims. The resulting dashboard design prioritizes auditability: every alert links to country, market, commodity, month, price, anomaly score, month-to-month change, threshold, and verification text. The results show that a transparent, high-precision visual alert pipeline can support NGO food-security monitoring while limiting false escalation.
AI-Assisted Fragmented UI Repair: From Layer Chaos to Clean Interface Structure
Design-to-code tools fail when high-fidelity interface mockups contain fragmented layers: a button becomes a background rectangle, a text label, icons, shadows, and decoration; a card becomes many unrelated rectangles and texts; a navigation bar becomes an arbitrary stack of visual primitives. This paper presents EGFE-RePair, an AI-assisted layer repair method that converts chaotic layer sets into clean interface hierarchy nodes for button, card, navbar, and form-group components. The method combines pairwise spatial learning with a deterministic, LLM-compatible semantic repair stage that applies explicit grouping decisions to candidate layer sets. The study conducts full experimental evaluation on the packaged EGFE-v1 and EGFE-v2 datasets, containing 100 and 300 samples respectively, with JSON layer metadata and rendered interface assets. All reported values are measured by the included code over fixed train, validation, and test splits; no illustrative or estimated result is used. On EGFE-v1, EGFE-RePair reaches 0.899 pair F1 and 0.907 hierarchy accuracy. On the larger EGFE-v2 test split, it reaches 0.868 pair F1 and 0.869 hierarchy accuracy, outperforming name-pattern, proximity, visual-rule, and learned pair-only baselines. Ablation shows that removing the semantic repair stage lowers EGFE-v2 hierarchy accuracy from 0.869 to 0.564, confirming that layer grouping requires both local evidence and component-level UI semantics. The results demonstrate a reproducible path from fragmented design layers to maintainable UIUX structure.
LLM-as-Design-Critic: Empirical Alignment of AI Mobile UI Critiques with Human Graphic Design Judgment
This paper evaluates whether large-language-model-generated user-interface critiques align with human graphic design judgment. The experiment uses the public UICrit CSV distributed with source-labeled critique comments, bounding boxes, and five human rating dimensions for mobile screens. The raw file contains 2,981 annotator rows, 1,000 unique RICO screen identifiers, and 11,344 canonical critique source labels. We parsed the complete file, retained the 2,898 rows in which the comment and source-label lists were aligned, excluded three zero-critique rows for text experiments, and evaluated 11,112 source-aligned comments. The central comparison treats comments marked human or both as the human side and comments marked llm or both as the LLM side. We measured natural language similarity, topic-set agreement, bounding-box overlap, and the ability of LLM-side comments to predict human ratings under grouped five-fold cross-validation by RICO screen identifier. The LLM side reached a row-level TF-IDF cosine of 0.412, token Jaccard of 0.357, and topic-set Jaccard of 0.660 with human-side critiques. However, when shared comments marked both were removed, strict TF-IDF cosine fell to 0.057. Bounding boxes showed the same pattern: source-inclusive mean best IoU was 0.759, whereas strict LLM-only to human-only IoU was 0.323. For overall design quality, the LLM critique TF-IDF model achieved Pearson r = 0.185 and MAE = 0.786, matching the mean baseline in error, while LLM topic/count features improved to r = 0.243 and MAE = 0.757. Human critique text remained substantially stronger with r = 0.625 and MAE = 0.595. The empirical result is therefore specific: LLM critiques align with human designers at the level of broad design topics, but they do not yet reproduce the rating signal or the specific localized judgments of human design critique.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
Temporal and Behavioural Feature Attribution Analysis for E-commerce Transaction Fraud Detection: A Comparative Study of Explainability Methods
Online transaction fraud imposes substantial financial losses on digital commerce ecosystems globally, with reported annual losses exceeding USD 48 billion as of 2024. While gradient boosting classifiers have achieved strong detection performance, the interpretability of their predictions remains inadequately addressed, particularly regarding the relative contribution of temporal and behavioral features to fraud identification. This study presents a systematic comparative analysis of three mainstream explainability methods—SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Permutation Feature Importance (PFI)—applied to XGBoost and LightGBM classifiers trained on two publicly available benchmark datasets: the ULB Credit Card Fraud Detection Dataset (284,807 transactions; fraud prevalence: 0.172%) and the IEEE-CIS Fraud Detection Dataset (590,540 transactions; fraud prevalence: 3.5%). A structured feature engineering pipeline constructs temporal interval features, cyclic time encodings, and rolling behavioral statistics. Class imbalance is addressed via Synthetic Minority Over-sampling Technique (SMOTE). Detection performance is evaluated using precision-recall area under the curve (PR-AUC) and F1-score. Cross-method attribution consistency is quantified via Spearman rank correlation across top-10 feature rankings. Results indicate that engineered temporal and behavioral features yield consistent PR-AUC improvements of 4.3–5.6 percentage points, and that SHAP demonstrates substantially higher attribution stability than LIME and PFI across all experimental configurations.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Comparative Evaluation of Zero-Shot and Few-Shot Performance of Large Language Models in Low-Resource Language Machine Translation
Large language models (LLMs) have demonstrated remarkable translation capabilities for high-resource languages, yet their effectiveness on low-resource languages under varying prompting conditions remains insufficiently understood. This study presents a comparative evaluation of four LLMs—GPT-4, GPT-3.5-Turbo, LLaMA-2-70B, and BLOOM-176B—alongside NLLB-200-3.3B as a supervised baseline, across ten translation directions spanning four resource levels. Using the FLORES-200 devtest set as the primary benchmark and NTREX-128 for cross-validation, we assess zero-shot, one-shot, five-shot, and eight-shot configurations with BLEU, chrF++, and COMET-22 metrics. Our results reveal three principal findings. The few-shot advantage is most pronounced for low-resource languages, with GPT-4 achieving an average BLEU gain of 5.3 points when moving from zero-shot to five-shot on low-resource pairs. One-shot prompting consistently degrades performance below zero-shot baselines, with an average BLEU reduction of 1.4 points across low-resource directions. The supervised NLLB-200 baseline outperforms all LLMs in zero-shot on eight of ten directions, while five-shot GPT-4 narrows this gap to within 1.0 BLEU on mid-resource pairs. These findings provide empirical guidance for practitioners selecting prompting strategies for LLM-based translation in resource-constrained settings.
Comparative Analysis of Pre-trained Language Models for ESG Financial News Sentiment Classification and Market Signal Correlation
Environmental, Social, and Governance (ESG) investing has become a central framework in modern capital markets, with the volume of ESG-related news growing at an unprecedented pace. Accurate sentiment extraction from ESG financial news constitutes a foundational capability for investment decision support, portfolio risk monitoring, and regulatory compliance analytics. This paper presents a systematic comparative study of pre-trained language model (PLM) approaches applied to ESG financial news sentiment classification, alongside an empirical analysis of their market signal predictive validity. Experiments are conducted across three publicly available benchmarks: FinancialPhraseBank (4,840 sentences; 75%-agreement subset: 3,453), FiQA Task 1 (1,174 entries), and the Twitter Financial News Sentiment dataset (11,932 entries). Market correlation analysis draws on S&P 500 component-level daily price data spanning January 2016 to July 2023, sourced from Yahoo Finance. The comparative evaluation covers lexicon-based methods (VADER, TextBlob, Loughran-McDonald) and domain-adapted transformer models (BERT-base, RoBERTa-base, FinBERT, FinBERT-Tone). Domain-adapted PLMs consistently outperform lexicon-based baselines by 12–19 weighted F1 percentage points on FinancialPhraseBank. Sector-level market correlation analysis reveals significant heterogeneity: Energy and Utilities sectors exhibit the strongest ESG sentiment–return linkages (Pearson r > 0.25), while Technology shows the weakest correlations (r < 0.16), providing sector-specific guidance for ESG-integrated quantitative investment strategies.
A Comparative Evaluation of Prompting Strategies for Code Generation with Large Language Models
Large language models have demonstrated remarkable capabilities in automated code generation, yet the relative effectiveness of different prompting strategies remains insufficiently characterized under zcontrolled experimental conditions. This study presents a systematic comparative evaluation of six prompting strategies — zero-shot, few-shot, chain-of-thought, structured chain-of-thought, self-debugging, and self-refine — across four representative models (GPT-4o, GPT-3.5-Turbo, DeepSeek-Coder-33B-Instruct, and CodeLlama-34B-Instruct) on three established benchmarks (HumanEval+, MBPP+, and LiveCodeBench). The evaluation encompasses functional correctness measured by pass@1, computational cost quantified by average token consumption, and difficulty-stratified performance analysis on contamination-free competitive programming tasks. Results indicate that structured chain-of-thought prompting yields the most consistent improvements across all models, achieving 6.7–8.5 percentage point gains over zero-shot baselines on HumanEval+. Multi-turn strategies such as self-debugging deliver the highest absolute performance on frontier models (80.5% pass@1 for GPT-4o on HumanEval+) while offering diminished returns on smaller open-source models. Cost-effectiveness analysis reveals that single-turn structured prompting achieves 77–93% of multi-turn performance at 72% lower token cost. These findings provide empirically grounded guidance for practitioners selecting prompting strategies under varying model capability levels and computational resource constraints.
Evaluating the Quality of Large Language Model-Generated Explanations in Recommendation Tasks: A Multi-Dimensional Comparative Analysis
The integration of large language models (LLMs) into recommendation systems has introduced new possibilities for generating natural language explanations that accompany recommended items. While prior research has explored methods of leveraging LLMs for explanation generation, limited attention has been given to systematically evaluating the quality of these explanations across different models, domains, and prompting strategies. This paper presents a multi-dimensional comparative analysis of LLM-generated recommendation explanations, examining four commercially available and open-source LLMs across three public recommendation datasets. A structured evaluation framework is proposed that encompasses four quality dimensions: faithfulness, informativeness, persuasiveness, and personalization. The evaluation employs both automatic metrics (BLEU-4, ROUGE-L, BERTScore) and human annotation protocols involving 12 trained evaluators. The experimental results indicate that larger-parameter models produce more informative and faithful explanations, though the gap narrows substantially when context-enhanced prompting strategies are applied. Automatic metrics show moderate correlation with human judgments on informativeness and faithfulness but limited alignment on persuasiveness and personalization. These findings offer practical guidance for selecting appropriate LLMs and prompting strategies in explanation-augmented recommendation applications.
Multi-Metric Trustworthiness Evaluation of AI-Assisted Medical Imaging Diagnosis: Integrating Confidence Calibration and Distribution Shift Detection
The rapid proliferation of artificial intelligence in medical imaging diagnosis has created an urgent need for comprehensive trustworthiness evaluation frameworks. This study proposes a multi-metric evaluation methodology that integrates confidence calibration assessment with distribution shift detection to address the critical challenge of silent failures in deployed AI diagnostic systems. The proposed framework encompasses Expected Calibration Error measurement across demographic subgroups, statistical distance-based distribution shift detection in deep feature spaces, and a novel trustworthiness scorecard that synthesizes prediction confidence, distributional similarity, and historical accuracy metrics. Experimental validation across three chest radiograph datasets demonstrates that the integrated evaluation approach identifies 23.7% more potentially unreliable predictions compared to single-metric methods while maintaining clinical utility. The framework provides actionable insights for FDA postmarket surveillance requirements and supports evidence-based clinical AI governance strategies.
Graph Neural Network-Based Cascading Disruption Path Identification in Multi-Tier Rare Earth Processing Networks
The United States confronts acute strategic vulnerability across rare earth supply chains, with import dependency on China exceeding 80% at most processing stages. This concentration risk is amplified by the sequential multi-tier structure of rare earth processing, in which primary mining, oxide separation, metal refining, alloy manufacturing, and permanent magnet production form a strictly ordered transformation hierarchy. A disruption at any single tier propagates to adjacent stages in nonlinear and often asymmetric ways, yet existing quantitative frameworks remain insufficient for tracing these cascading pathways at the tier level. This paper develops a graph neural network (GNN) framework applied to a heterogeneous multi-tier network representing rare earth processing flows across major producing and consuming nations. The framework integrates bilateral trade data from the BACI International Trade Database, processing capacity statistics from the U.S. Geological Survey (USGS) and the International Energy Agency (IEA), and the Caldara-Iacoviello Geopolitical Risk Index as node-level features. A relational graph convolutional network (R-GCN) simulates the propagation of disruption signals across processing tiers, and a graph attention module ranks cascading pathways by cumulative vulnerability scores. Experiments conducted on 34 documented disruption events from 2000 to 2024 identify oxide separation as the dominant structural bottleneck tier, and ablation analysis confirms that heterogeneous edge treatment and geopolitical risk features each contribute independently to model performance. The framework quantifies how alternative sourcing corridors alter network-level vulnerability across four geopolitical disruption scenarios.
Effectiveness Evaluation of Attention Mechanism Strategies in Deep Learning-Based Single Image Super-Resolution
Single image super-resolution (SISR) has experienced rapid advancements through the integration of attention mechanisms into deep neural networks. Diverse attention strategies, including channel attention, spatial attention, second-order attention, and self-attention, have been incorporated into SISR architectures with varying degrees of success. A systematic and fair comparison of these attention strategies under unified experimental conditions remains absent from the current literature. This study conducts a controlled benchmark evaluation of five representative SISR algorithms—EDSR, RCAN, SAN, SwinIR, and HAT—each embodying a distinct attention paradigm, across four standard datasets (Set5, Set14, BSD100, Urban100) at ×2 and ×4 scaling factors. The evaluation encompasses distortion-oriented metrics (PSNR, SSIM), perceptual quality metrics (LPIPS), and computational efficiency indicators (parameters, FLOPs, inference time). Experimental results reveal that hybrid attention mechanisms in HAT achieve the highest reconstruction fidelity, with 1.25 dB PSNR gain over the non-attention baseline EDSR on Urban100 at ×2 scale, while window-based self-attention in SwinIR offers the most favorable accuracy-efficiency trade-off. The findings provide practical guidance for selecting attention strategies in SISR applications under different resource constraints.
Artificial Intelligence Applications Across Interdisciplinary Domains: A Comprehensive Survey of Methods, Challenges, and Future Directions
The rapid advancement of artificial intelligence (AI) has catalyzed transformative innovations across a multitude of disciplines, ranging from healthcare and biomedicine to finance, cybersecurity, energy sustainability, and beyond. This comprehensive survey systematically reviews 133 recent studies published between 2023 and 2026 that collectively represent the state of the art in AI-driven methodologies and their real-world applications. We categorize the reviewed literature into nine principal domains: (1) healthcare and biomedical sciences, (2) finance and risk management, (3) privacy-preserving computing and data security, (4) natural language processing and communication, (5) transportation and logistics, (6) energy and environmental sustainability, (7) education and social impact, (8) media and advertising technology, and (9) emerging cross-domain applications. For each domain, we analyze the predominant AI techniques employed, including deep learning, federated learning, reinforcement learning, natural language processing, and graph-based methods, while highlighting their respective strengths and limitations. Furthermore, we present a taxonomic framework that elucidates the interconnections among these application domains and identify critical research gaps that warrant future investigation. Our analysis reveals several overarching trends, notably the increasing adoption of privacy-preserving paradigms, the integration of multimodal data fusion, and the growing emphasis on explainability and fairness in AI systems. This survey provides researchers and practitioners with a holistic reference for understanding the current landscape and future trajectory of applied AI research.
A Comprehensive Survey on Artificial Intelligence Applications Across Interdisciplinary Domains: Techniques, Challenges, and Future Directions
Artificial Intelligence (AI) has rapidly evolved into a transformative technology with far-reaching applications across healthcare, finance, cybersecurity, natural language processing, computer vision, and beyond. This survey systematically reviews 120 recent studies published between 2023 and 2026, offering a comprehensive analysis of AI-driven methodologies including deep learning, federated learning, reinforcement learning, differential privacy, and multimodal data fusion. The reviewed works are organized thematically and cited in sequential order to facilitate a coherent narrative of the current research landscape. In addition, we present comparative tables summarizing key techniques, application domains, and open challenges. Our analysis reveals that while AI has achieved significant breakthroughs in domain-specific applications, persistent challenges related to data privacy, model interpretability, algorithmic fairness, and cross-domain transferability remain. This survey aims to provide researchers and practitioners with a holistic overview and to inspire future interdisciplinary research endeavors.
Diagnosing CI/CD Failures and Recommending Repair Diff Footprints with Lightweight LLM-Assisted Models: Full Experimental Evaluation on BugSwarm CI Fail–Pass Pairs
CI/CD pipelines execute builds and tests on every change, generating high-volume failure signals and log artifacts that require rapid diagnosis and repair. This paper studies two operational tasks that appear in modern DevOps automation: (i) failure diagnosis and (ii) repair recommendation in a Top-k setting. We conducted full experimental evaluations on a public BugSwarm-derived artifact list containing 325 Java fail–pass CI pairs (SHA-256: 267fdfc1ee603af3613db96ec79230c7c2e856fa5b4594ffda6ec51f38809df6). We defined three failure types from CI outcome metadata—TEST_FAIL, BUILD_OR_SETUP_FAIL, and NONTEST_FAIL—and defined repair targets as diff-footprint patterns A{a}C{c}D{d} based on the numbers of added/changed/deleted files, mapped to 24 classes (23 frequent patterns with support ≥3 plus OTHER). Using only information available at failure time (commit message, repository metadata, build system, test framework, and test counters), we compared six baselines and a proposed probability-averaging ensemble of text-only and numeric-only multinomial logistic regression. On failure diagnosis (5-fold stratified CV), the proposed ensemble achieved macro-F1=0.881 and accuracy=0.926. On repair recommendation (3-fold stratified CV), the proposed ensemble achieved Hit@1=0.511, Hit@3=0.754, Hit@5=0.852, Hit@10=0.920, and MRR=0.657. The manuscript reports detailed tables and figures and specifies all hyperparameters to make the reported empirical findings reproducible.