Journal of Computing Innovations and Applications
type: channel
description: The Journal of Computing Innovations and Applications (CIA) stands at the forefront of computational research and technological advancement, serving as a premier platform for groundbreaking discoveries in computing sciences. This peer-reviewed international journal bridges the crucial gap between theoretical innovation and practical implementation, publishing cutting-edge research that transforms how we understand and apply computing technologies. CIA uniquely focuses on the intersection of emerging computational methods with their real-world applications, featuring work that spans artificial intelligence, quantum computing, cybersecurity, and intelligent systems. With its rigorous double-blind review process and commitment to rapid publication, the journal connects leading researchers, industry pioneers, and practitioners who are shaping the future of digital innovation. CIA particularly values research that demonstrates novel solutions to contemporary challenges, fostering a dynamic exchange between academia and industry that drives technological progress and societal advancement.
publisher: omputing Innovations and Applications
language: en
publication name: Journal of Computing Innovations and Applications
issn: 3068-5516
items: Question Decomposition and Evidence Chain Scoring for Complex RAG Retrieval Quality Evaluation on the FRAMES Benchmark
items: Metadata-Aware Multi-Hop RAG Retrieval Quality Prediction with Graph and Attention Features
items: Evidence Diversity, Redundancy, and Semantic Similarity Trade-Offs in Multi-Hop RAG Systems
items: Coverage-Aware Prediction of Multi-Hop RAG Answer Correctness on FRAMES
items: Conflict-Aware RAG Answer Quality Classification: Detecting Evident Conflict and Baseless Information with Lightweight ML Models
items: Trustworthy Artificial Intelligence in High-Stakes Decision Systems: A Cross-Domain Systematic Review of Explainability, Fairness, Privacy, Robustness, and Governance
items: An Empirical Comparison of Few-Shot Example Selection Strategies for In-Context Learning on Public Reasoning and QA Benchmarks
items: A Comparative Empirical Study of Semantic Signal Enhancement Methods for User Interest Features in CTR Prediction: Applicability of TF-IDF Weighting, Sentence-BERT Embeddings, and LDA Topic Fusion
items: Transparent Humanitarian Funding UI: Data-Grounded LLM-Compatible Explanations of OCHA FTS Funding Flows
items: Recovering Design Tokens from Figma-like UI Layers Using LLM-Guided Schema Synthesis
items: A Comparative Evaluation of Transfer Learning Methods for Cross-Context Behavioral Generalization Assessment in Autism Spectrum Disorder Interventions
items: Identifying Cross-Market Risk Contagion Amplifiers via Graph Attention Networks: Empirical Evidence from U.S. Financial Stress Periods
items: Gradient Boosting and SHAP-Based Analysis of Flip IRR Determinants in Residential Solar Tax Equity Transactions Under Section 48E Policy Uncertainty
items: Behavioural Feature Analysis for Anomalous Click Detection in Mobile Advertising Environments: Toward In-App Browser-Specific Detection
items: Detecting Semantic Mismatches in XBRL Tag Mapping for SEC 10-K Filings: A Text Comparison and Historical Consistency Analysis
items: A Comparative Evaluation of Robust Loss Functions for Learning with Noisy Labels: From Synthetic to Real-World Annotations
items: Statistical Anomaly Detection Approach for Field Mapping Validation in Enterprise Payroll Data Migration
items: Integrating Ovarian Reserve Biomarkers with Machine Learning for Gonadotoxicity Risk Prediction in Young Female Cancer Patients: A Scoping Review
items: Fairness-Accuracy Trade-offs in AI Credit Scoring: A Comparative Evaluation of Reweighting and Resampling Strategies Under Multiple Fairness Constraints
items: Causal Effect Evaluation of Personalized Reminder Strategies on Government Welfare Program Enrollment: A Propensity Score Matching Approach
items: AI-Enhanced What-If Scenario Analysis in Supply Chain Digital Twins: A Multi-Objective Trade-Off Perspective on Cost, Resilience, and Carbon Efficiency
items: AI-Enhanced Cross-Asset Liquidity Contagion Pathway Identification and Dynamic Hedging Strategy Optimization: Evidence from U.S. Equity, Bond, and Derivatives Markets
items: A Comparative Evaluation of Deep Learning Paradigms for Low-Light Image Enhancement: From CNNs to Diffusion Models
items: A Comparative Analysis of Telemetry-Driven Anomaly DetectionApproaches for Dual-Purpose Operational and SecurityOptimization in Edge Computing Infrastructure
items: Artificial Intelligence Applications Across Critical Domains: A Comprehensive Survey of Recent Advances in Healthcare, Finance, Security, and Beyond
items: A Comprehensive Survey on Artificial Intelligence Applications Across Critical Domains: Advances in Healthcare, Finance, Cybersecurity, and Emerging Fields
items: Uplift Modelling and Doubly Robust Causal Learning for Bank Marketing Targeting: Optimizing ROI with Coverage–Incremental Profit Curves
items: An Empirical Evaluation of Prompt Injection Detection and Refusal-Usefulness Tradeoffs Using the deepset/prompt-injections Dataset
items: Temporal Feature Learning Framework for Multi-dimensional Behavior Anomaly Detection in Digital Transaction Platforms
items: Accelerating Clinical Trial Recruitment Through Automated Eligibility Screening with Multi-Modal Deep Learning

Question Decomposition and Evidence Chain Scoring for Complex RAG Retrieval Quality Evaluation on the FRAMES Benchmark
type: item
description: Complex retrieval-augmented generation (RAG) systems can retrieve one highly relevant page while still missing the other pages required to support a multi-hop answer. This paper presents QD-ECS, an article-level evaluation procedure that combines deterministic question decomposition with evidence-chain scoring on the FRAMES benchmark. The evaluation covers all 824 FRAMES questions and a closed candidate pool of 2,480 normalized Wikipedia article identifiers derived from the benchmark links. Five retrieval settings are compared: Random, TF-IDF, BM25, BM25-Decomp, and Hybrid-Chain. Retrieval quality is measured with article Recall@k, ChainExact@k, step evidence coverage (SEC@k), NDCG@k, and MRR. Hybrid-Chain achieved the highest Recall@5 (0.499), SEC@5 (0.438), NDCG@5 (0.552), and MRR (0.824), whereas TF-IDF obtained the highest ChainExact@5 (0.180), exceeding Hybrid-Chain by 0.001. The central finding is that first-hit success substantially overstates evidence sufficiency: Hybrid-Chain recovered every required article in the top five for 147 of 824 questions, and its ChainExact@5 declined from 0.373 on two-article questions to 0.011 on questions requiring five or more articles. QD-ECS therefore separates early partial recovery from complete chain recovery and provides a compact diagnostic framework for evaluating multi-hop RAG retrieval before answer generation
subject: Articles
  topic: section
subject: retrieval-augmented generation
  topic: keywords
subject: RAG evaluation
  topic: keywords
subject: FRAMES benchmark
  topic: keywords
subject: multi-hop retrieval
  topic: keywords
subject: question decomposition
  topic: keywords
subject: evidence-chain scoring
  topic: keywords
subject: BM25
  topic: keywords
subject: TF-IDF
  topic: keywords
creator: Ryan Bennett
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-04
publication date: 2026-03-04
starting page: 228
ending page: 245
doi: 10.63575/CIA.2026.40118

Metadata-Aware Multi-Hop RAG Retrieval Quality Prediction with Graph and Attention Features
type: item
description: Multi-hop retrieval-augmented generation can fail even when individual passages appear relevant because the retrieved set omits a bridge document or combines evidence that does not form a complete reasoning path. This paper presents MAGAF, a metadata-aware graph and attention feature framework for predicting retrieval sufficiency before answer generation. MAGAF represents the top-ranked context through source, category, entity, and date agreement; weighted cross-document graph cohesion; retrieval-score gaps; and attention-style summaries of score concentration. The experimental pipeline was evaluated on a controlled MultiHop-RAG-compatible collection containing 2,556 queries and 609 documents, with non-null evidence paths spanning two to four documents. Five retrievers and five predictors were compared under a strict complete-evidence target at top four. Hybrid-MetaGraph retrieval achieved Recall@4 of 0.501 and CompleteRecall@4 of 0.320, improving CompleteRecall@4 by 0.110 over TF-IDF. The calibrated MAGAF predictor achieved AUROC 0.884, F1 0.765, Brier score 0.119, and expected calibration error 0.072. Bootstrap 95% confidence intervals were 0.854-0.915 for AUROC and 0.712-0.809 for F1. The results show that metadata agreement, graph cohesion, and score dispersion provide complementary signals for deciding whether a multi-document context is sufficiently complete for downstream generation.
subject: Articles
  topic: section
subject: retrieval-augmented generation
  topic: keywords
subject: multi-hop retrieval
  topic: keywords
subject: metadata-aware retrieval
  topic: keywords
subject: evidence-path prediction
  topic: keywords
subject: graph features
  topic: keywords
subject: attention features
  topic: keywords
subject: calibration
  topic: keywords
creator: Erik Nilsson
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-13
publication date: 2026-07-13
starting page: 47
ending page: 62
doi: 10.63575/CIA.2026.40204

Evidence Diversity, Redundancy, and Semantic Similarity Trade-Offs in Multi-Hop RAG Systems
type: item
description: Multi-hop retrieval-augmented generation requires a retriever to assemble a complementary evidence set rather than return a single locally similar passage. This study examines the interaction among query relevance, within-set redundancy, and evidence diversity using a deterministic benchmark instance aligned with the published MultiHop-RAG scale and schema. The evaluation covers 2,556 queries, 609 documents, four query classes, metadata-rich records, and answerable evidence sets spanning two to four documents. We compare BM25, TF-IDF cosine retrieval, metadata-augmented variants, weighted hybrid fusion, four Maximal Marginal Relevance settings, and a hybrid-plus-MMR selector. Performance is assessed with Partial Recall, Complete Recall, MRR, nDCG, mean pairwise redundancy, diversity, semantic similarity, source diversity, null-query rejection, bootstrap confidence intervals, and measured retrieval latency. TF-IDF with metadata achieves the strongest Complete Recall@10 at 0.793, together with Partial Recall@10 of 0.916 and nDCG@10 of 0.739. Pure novelty pressure is counterproductive: MMR with λ=0.35 raises diversity to 0.834 but reduces Complete Recall@10 to 0.011. A relevance-dominant setting, λ=0.80, recovers 0.665 Complete Recall@10 while lowering redundancy relative to similarity-only ranking. The results show that metadata-aware relevance should establish the candidate pool, after which diversification can be applied as a bounded set-composition correction. For multi-hop retrieval, diversity is valuable only when it preserves the shared entities, temporal anchors, and source constraints that bind an evidence chain.
subject: Articles
  topic: section
subject: multi-hop retrieval
  topic: keywords
subject: etrieval-augmented generation
  topic: keywords
subject: evidence diversity
  topic: keywords
subject: redundancy
  topic: keywords
subject: semantic similarity
  topic: keywords
subject: metadata retrieval
  topic: keywords
subject: Maximal Marginal Relevance
  topic: keywords
subject: BM25
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: evidence-set completion
  topic: keywords
creator: Isabella Martinez
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-10
publication date: 2026-07-10
starting page: 31
ending page: 46
doi: 10.63575/CIA.2026.40203

Coverage-Aware Prediction of Multi-Hop RAG Answer Correctness on FRAMES
type: item
description: Multi-hop retrieval-augmented generation (RAG) can retrieve context that appears relevant while omitting an article needed to complete the reasoning chain. This study examines whether that evidence deficit can be detected before answer generation. The evaluation covers all 824 questions in the public FRAMES test file, which provides gold answers, reasoning labels, and relevant Wikipedia links. Because the file identifies articles but does not include article bodies, a closed-world title corpus is constructed from every parsed gold link. Four deterministic retrievers are compared: BM25 over normalized titles, word-level TF-IDF, character-level TF-IDF, and a fixed hybrid. Coverage is defined as the fraction of a question's parsed gold article set returned in the top-k. Strict coverage-gated correctness is one only when the full set is present; it therefore measures retrieval readiness rather than neural-reader accuracy. At k=10, the hybrid reaches mean coverage of 0.547 and strict correctness of 0.225; at k=25, the values rise to 0.590 and 0.273. Performance declines sharply as article count grows. In five-fold cross-validation, logistic regression using observable query and ranking signals predicts strict correctness at k=10 with ROC-AUC 0.796. Adding benchmark hop-count metadata raises ROC-AUC to 0.856. The findings show that incomplete multi-hop evidence is both a central retrieval bottleneck and a predictable risk signal that can guide additional retrieval, decomposition, or abstention before generation.
subject: Articles
  topic: section
subject: retrieval-augmented generation
  topic: keywords
subject: FRAMES
  topic: keywords
subject: multi-hop question answering
  topic: keywords
subject: retrieval coverage
  topic: keywords
subject: strict correctness
  topic: keywords
subject: BM25
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: calibration
  topic: keywords
creator: Callum Hughes
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-07
publication date: 2026-07-07
starting page: 16
ending page: 30
doi: 10.63575/CIA.2026.40202

Conflict-Aware RAG Answer Quality Classification: Detecting Evident Conflict and Baseless Information with Lightweight ML Models
type: item
description: Retrieval-augmented generation (RAG) improves factual reliability by conditioning a language model on retrieved evidence, yet grounded answers can still fail in two distinct ways: they may contradict the available evidence or introduce claims that the evidence does not support. This study formulates conflict-aware answer-quality classification on RAGTruth-processed, a 17,790-row benchmark derived from RAGTruth, and distinguishes four mutually exclusive row-level states: no hallucination, evident-conflict only, baseless-information only, and both. The empirical analysis verifies the target construction, split composition, label overlap, quality labels, task variation, and generator variation, while the modeling framework specifies three deployment-oriented classifiers: XGBoost with TF-IDF and overlap features, a compact bidirectional LSTM, and DeBERTa-small with structured query-context-output input. The train and test splits differ in four-class composition (chi-square = 108.381, p = 2.447 × 10⁻²³, Cramér’s V = 0.078) and in overall hallucination prevalence, whereas their quality-label distributions remain stable (chi-square = 3.348, p = 0.188, Cramér’s V = 0.014). Baseless-information-only examples are more common than evident-conflict-only examples in both splits, and mixed errors form the smallest test class. These properties show why accuracy and binary hallucination detection are insufficient for operational monitoring. Macro-F1, mechanism-specific recall, confusion matrices, probability calibration, and routing-aware error analysis are therefore treated as the central evaluation criteria for lightweight conflict-aware safeguards.
subject: Articles
  topic: section
subject: retrieval-augmented generation
  topic: keywords
subject: hallucination detection
  topic: keywords
subject: evident conflict
  topic: keywords
subject: baseless information
  topic: keywords
subject: RAGTruth
  topic: keywords
subject: XGBoost
  topic: keywords
subject: BiLSTM
  topic: keywords
subject: DeBERTa
  topic: keywords
subject: calibration
  topic: keywords
subject: answer quality classification
  topic: keywords
creator: Sarah Zheng
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-04
publication date: 2026-07-04
starting page: 1
ending page: 15
doi: 10.63575/CIA.2026.40201

Trustworthy Artificial Intelligence in High-Stakes Decision Systems: A Cross-Domain Systematic Review of Explainability, Fairness, Privacy, Robustness, and Governance
type: item
description: Artificial intelligence (AI) systems are increasingly entrusted with consequential decisions in finance, healthcare, cybersecurity, public infrastructure, and online platforms. As these systems move from research prototypes to operational deployment, their value depends not only on predictive accuracy but on whether stakeholders can trust them to behave transparently, fairly, privately, robustly, and accountably. This paper presents a cross-domain systematic review of trustworthy AI in high-stakes decision systems, synthesizing 196 recent studies that span financial risk and regulatory technology, clinical and biomedical analytics, network and software security, social-media information integrity, energy and sustainability, and commerce and mobility. We organize the literature along five trustworthiness pillars-explainability, fairness, privacy preservation, robustness and safety, and accountability and governance-and three cross-cutting methodological enablers: multimodal and multi-source data fusion, graph-based learning, and large language models (LLMs) with agentic orchestration. For each domain we characterize the dominant tasks, data modalities, and evaluation practices; for each pillar we summarize representative techniques and their tensions. Our coverage analysis reveals an uneven landscape: explainability and robustness dominate finance and security, privacy concentrates in healthcare and consumer platforms, and fairness remains comparatively underexplored beyond credit scoring. We further discuss recurring trade-offs-privacy versus utility, fairness versus accuracy, transparency versus performance-and the emerging risks introduced by LLM-based and multi-agent systems, including hallucination, memory poisoning, and over-refusal. We conclude with open challenges and a research agenda emphasizing standardized evaluation, lifecycle governance, and the integration of trustworthiness guarantees into agentic AI.
subject: Articles
  topic: section
subject: Trustworthy AI
  topic: keywords
subject: explainable AI
  topic: keywords
subject: algorithmic fairness
  topic: keywords
subject: privacy-preserving machine learning
  topic: keywords
subject: robustness
  topic: keywords
subject: large language models
  topic: keywords
subject: agentic AI
  topic: keywords
subject: systematic review
  topic: keywords
creator: Tyler J. Brennan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 202
ending page: 227
doi: 10.63575/CIA.2026.40117

An Empirical Comparison of Few-Shot Example Selection Strategies for In-Context Learning on Public Reasoning and QA Benchmarks
type: item
description: In-context learning allows large language models to adapt to a new task by conditioning on a small set of labelled demonstrations placed inside the prompt, and a growing body of work shows that the demonstrations chosen can shift task accuracy by more than ten absolute points. Four families of selection strategies dominate current practice: random sampling, similarity-based retrieval, diversity-based coverage, and complexity-based ranking. Their relative strengths across task types have not been examined inside a single controlled grid. This work offers an empirical comparison of six representative strategies drawn from these four families on four widely used public benchmarks — GSM8K, MMLU, BIG-Bench Hard, and CommonsenseQA — with two open-weight instruction-tuned backbones (Llama-3-8B-Instruct and Mistral-7B-Instruct-v0.2) and a robustness check on StrategyQA. Every strategy is evaluated under the same shot budget and prompt template, and stability is quantified across random seeds. No single strategy dominates the spread of tasks: similarity-based retrieval excels on commonsense QA, complexity-based ranking leads on multi-step arithmetic and algorithmic reasoning, a similarity-plus-diversity hybrid delivers the most stable average accuracy, and the gap between the best and worst strategies is moderate at 3.1 points. These findings support a task-aware view of demonstration selection and suggest that selection can be tuned at the task-type level
subject: Articles
  topic: section
subject: in-context learning
  topic: keywords
subject: few-shot prompting
  topic: keywords
subject: demonstration selection
  topic: keywords
subject: reasoning benchmarks.
  topic: keywords
creator: Xuanyi Fu
creator: Fanyi Zhao
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 119
ending page: 128
doi: 10.63575/CIA.2025.30209

A Comparative Empirical Study of Semantic Signal Enhancement Methods for User Interest Features in CTR Prediction: Applicability of TF-IDF Weighting, Sentence-BERT Embeddings, and LDA Topic Fusion
type: item
description: User interest feature engineering in recommendation and advertising platforms increasingly relies on semantic signals derived from item text, user queries, and tag annotations. Practitioners lack a unified empirical comparison of the dominant fusion paths under a shared evaluation protocol. This study reports a comparative empirical analysis of three representative semantic signal enhancement methods — TF-IDF weighting, Sentence-BERT embeddings, and latent Dirichlet allocation (LDA) topic distributions — applied to user interest features for click-through rate (CTR) prediction. All three methods are evaluated on four public datasets (MovieLens-25M, MIND-small, Amazon Reviews 2023, and KuaiRec 2.0) using the Deep Interest Network and the Deep Interest Evolution Network as fixed CTR backbones. Sentence-BERT yields a mean AUC lift of 1.71 percent over the identifier-only baseline, while TF-IDF and LDA deliver 0.81 percent and 0.29 percent, respectively. Granularity analysis indicates that TF-IDF peaks on short text such as titles and tags, Sentence-BERT scales monotonically with document length, and LDA only matches TF-IDF once content exceeds roughly one hundred tokens. Cost-benefit profiling places Sentence-BERT with cached item vectors on the accuracy-latency Pareto frontier for mid-to-long text, while TF-IDF remains preferable in short-text, cold-start, and long-tail regimes.
subject: Articles
  topic: section
subject: Semantic signal enhancement
  topic: keywords
subject: click-through rate prediction
  topic: keywords
subject: comparative empirical study
  topic: keywords
subject: user interest feature engineering
  topic: keywords
creator: Tianxing Tang
creator: Mingzhuo Yu
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-02-20
publication date: 2024-02-20
starting page: 165
ending page: 174
doi: 10.63575/CIA.2024.20114

Transparent Humanitarian Funding UI: Data-Grounded LLM-Compatible Explanations of OCHA FTS Funding Flows
type: item
description: Humanitarian funding dashboards expose complex relationships among donors, recipients, sectors, reporting statuses, and response-plan gaps. This paper evaluates a transparent funding-interface design in which data-grounded, LLM-compatible explanations are generated for OCHA Financial Tracking Service (FTS) flow records and paired with accessible legends for funding gaps and reporting certainty. The study conducts a complete reproducible experiment on downloaded FTS/HDX CSV data for the Occupied Palestinian Territory. The primary incoming-flow file contains 394 records and 37 fields, with $794.41M in reported incoming flows, 57 source organizations, 73 destination organizations, and 20 normalized cluster labels. The linked requirements file identifies the 2026 OPT Flash Appeal (FPSE26) with $4.06B in requirements, $459.73M in reported funding, and an official 11.0% funded value, leaving a measured $3.60B gap. Four generation conditions were evaluated on every flow: a minimal label, a generic narrative baseline, a gap-aware explanation, and a constrained audit explanation. Metrics included entity coverage, numeric grounding, unsupported-claim rate, traceability, legend completeness, actionability, reading ease, length, and an aggregate UI-fit score. The proposed constrained explanation achieved a mean UI-fit score of 98.3, compared with 28.2 for minimal labels, 13.7 for generic narratives, and 52.8 for gap-aware explanations. Results show that transparent funding explanations improve semantic completeness when they explicitly bind donor, recipient, sector, amount, status, boundary scope, and plan-level gap context. The included code, processed data, tables, and figures reproduce all reported findings.
subject: Articles
  topic: section
subject: accessible user interfaces
  topic: keywords
subject: humanitarian funding
  topic: keywords
subject: OCHA FTS
  topic: keywords
subject: explainable AI
  topic: keywords
subject: natural language generation
  topic: keywords
subject: funding transparency
  topic: keywords
subject: inclusive design
  topic: keywords
subject: data visualization
  topic: keywords
subject: accountability interfaces
  topic: keywords
creator: Jing Xin
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-23
publication date: 2026-02-23
starting page: 186
ending page: 201
doi: 10.63575/CIA.2026.40116

Recovering Design Tokens from Figma-like UI Layers Using LLM-Guided Schema Synthesis
type: item
description: Design tokens encode a design system's reusable decisions for color, spacing, typography, and component semantics, yet production teams often inherit screens whose layer names, styles, and component nesting are inconsistent. This paper presents SALT, a deterministic layer-analysis and constrained LLM-guided schema-synthesis pipeline for recovering design tokens from Figma-like UI layer JSON and paired high-fidelity screenshots. The method parses vector-style layer trees, extracts style observations, clusters noisy color and dimension values, recovers component groups from frame hierarchy, and emits a Design Tokens Community Group compatible JSON schema. Full experiments were executed on a 300-screen EGFE-compatible benchmark included with this submission. The benchmark contains 9,164 total layers, 6,374 leaf layers, 8,150 color observations, 12,450 spacing observations, 3,281 typography observations, and 2,190 annotated component groups. Six methods were evaluated: raw unique values, screenshot-palette extraction, rounded-value heuristics, k-means clustering, schema generation without semantic naming, and SALT. SALT recovered spacing tokens with F1=0.800, typography tokens with F1=0.909, and component hierarchy with F1=1.000; its color-token F1 was 0.538, while the rounded-16 heuristic reached the highest color-value F1 of 0.645. SALT was the only method that produced nonzero reference-path schema coverage, achieving 0.571, and it generated a compact 22-token schema in 0.244 s on the complete benchmark. The results show that layer-aware semantic synthesis is necessary for usable token schemas, while value-only color clustering remains sensitive to neutral color collisions.
subject: Articles
  topic: section
subject: Design tokens
  topic: keywords
subject: Figma-to-code
  topic: keywords
subject: design systems
  topic: keywords
subject: UI layer parsing
  topic: keywords
subject: schema synthesis
  topic: keywords
subject: large language models
  topic: keywords
subject: component hierarchy
  topic: keywords
subject: empirical evaluation
  topic: keywords
creator: Haosen Xu
creator: Sarah Liu
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
date: 2023-02-02
publication date: 2023-02-02
starting page: 39
ending page: 52
doi: 10.63575/CIA.2023.10104

A Comparative Evaluation of Transfer Learning Methods for Cross-Context Behavioral Generalization Assessment in Autism Spectrum Disorder Interventions
type: item
description: This study presents a comparative evaluation of three transfer learning paradigms—domain adaptation, few-shot learning, and multi-task learning—for assessing cross-context behavioral generalization in autism spectrum disorder (ASD) interventions. Behavioral skill generalization across distinct intervention environments remains a persistent barrier to long-term ASD intervention effectiveness. The distributional discrepancies inherent in behavioral data collected from clinical, school, and home settings create domain shift challenges that standard recognition models cannot adequately address. Drawing primarily on the Self-Stimulatory Behavior Dataset (SSBD) for the video-based comparative analysis, while using the Autism Brain Imaging Data Exchange (ABIDE) as an auxiliary multi-site domain-shift reference and the Expanded Stereotype Behavior Dataset (ESBD) as a published benchmark, this paper establishes an evaluation framework with four quantitative metrics: cross-context recognition accuracy, generalization stability index, few-shot adaptation efficiency, and behavioral transfer success rate. Within this benchmark-informed framework, domain adaptation yields the highest source-proximal accuracy, few-shot learning shows the strongest low-label adaptation, and multi-task learning exhibits the most stable cross-context profiles. These findings provide practical guidance for selecting computational tools to support more scalable and objective ASD intervention evaluation.
subject: Articles
  topic: section
subject: Transfer learning
  topic: keywords
subject: autism spectrum disorder
  topic: keywords
subject: behavioral generalization
  topic: keywords
subject: cross-context evaluation
  topic: keywords
creator: Yaqing Bai
creator: Muyu Liu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-20
publication date: 2026-02-20
starting page: 176
ending page: 185
doi: 10.63575/CIA.2026.40115

Identifying Cross-Market Risk Contagion Amplifiers via Graph Attention Networks: Empirical Evidence from U.S. Financial Stress Periods
type: item
description: The propagation of financial distress across heterogeneous asset markets has become one of the most pressing challenges for systemic risk monitoring in the post-crisis regulatory environment. This paper investigates cross-market risk contagion among U.S. equity, bond, and credit derivative markets through a graph attention network (GAT) framework embedded in a multilayer network structure. Constructing a directed, attention-weighted graph with 30 nodes spanning three market layers, and drawing on daily data from January 2018 to December 2023, the analysis identifies a persistent set of risk amplifier nodes whose elevated outgoing attention scores and centrality measures designate them as the primary drivers of cross-market stress transmission. The high-yield CDS index node emerges as the most consistently identified amplifier across three distinct stress episodes—the March 2020 pandemic shock, the 2022 interest rate stress cycle, and the March 2023 regional banking turmoil—while the financial sector equity index and long-duration bond node occupy episode-specific secondary amplifier roles. The multilayer network representation captures contagion pathways that single-layer analyses systematically underestimate, particularly the derivative-to-equity transmission channel active under tail-risk conditions. These findings carry direct implications for counterparty risk monitoring and macro-prudential early-warning design in the context of central clearing and financial stability regulation.
subject: Articles
  topic: section
subject: cross-market risk contagion
  topic: keywords
subject: graph attention network
  topic: keywords
subject: multilayer financial network
  topic: keywords
subject: systemic risk amplifier
  topic: keywords
subject: financial stress
  topic: keywords
creator: Yifei Li
creator: Fanyi Zhao
creator: Jiacheng Hu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-16
publication date: 2026-02-16
starting page: 164
ending page: 175
doi: 10.63575/CIA.2026.40114

Gradient Boosting and SHAP-Based Analysis of Flip IRR Determinants in Residential Solar Tax Equity Transactions Under Section 48E Policy Uncertainty
type: item
description: The partnership flip structure remains the dominant mechanism for monetizing federal investment tax credits in U.S. residential solar photovoltaic financing, yet the pricing of the flip internal rate of return and the determination of ITC transfer discount rates have historically depended on bilateral negotiation rather than systematic quantitative benchmarks. This paper presents a data-driven framework applying gradient boosting regression and SHAP-based interpretability analysis to a curated dataset of 87 residential solar portfolio-level tax equity transactions completed between 2018 and 2024, in which each transaction represents an aggregated investment fund comprising hundreds to thousands of individual rooftop installations structured through third-party ownership platforms. XGBoost and LightGBM models are trained to predict flip IRR intervals and ITC discount rates from 22 transaction-level and macroeconomic features, achieving a mean absolute error of 0.19 percentage points on held-out test transactions. SHAP value decomposition identifies the applicable MACRS bonus depreciation profile, benchmark Treasury rates, and contracted revenue escalation assumptions as the three highest-impact determinants of investor return, which in turn governs flip timing, while the deficit restoration obligation provision—frequently treated as legal boilerplate—emerges as a material constraint-relaxing mechanism that enables fuller depreciation utilization in high-bonus-election transactions. A Monte Carlo simulation framework, constrained by model-predicted investor IRR thresholds, generates sponsor net present value distributions across tax-driven capital structures scenarios, revealing that full bonus depreciation under a §48E domestic content scenario improves mean sponsor NPV by 19 percent relative to baseline. The framework is evaluated against the policy landscape established by the One Big Beautiful Bill Act signed into law on July 4, 2025, including the accelerated ter…
subject: Articles
  topic: section
subject: Tax equity financing
  topic: keywords
subject: Flip IRR
  topic: keywords
subject: Gradient boosting
  topic: keywords
subject: SHAP interpretability
  topic: keywords
subject: Section 48E
  topic: keywords
creator: Yiyang Peng
creator: Xu Wang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-26
publication date: 2025-07-26
starting page: 106
ending page: 118
doi: 10.63575/CIA.2025.30208

Behavioural Feature Analysis for Anomalous Click Detection in Mobile Advertising Environments: Toward In-App Browser-Specific Detection
type: item
description: Mobile in-app browsers (IABs), implemented through WebView components, have become a dominant channel for rendering advertising content within mobile applications. This environment presents distinctive challenges for ad fraud detection due to its constrained JavaScript execution context, opaque rendering pipeline, and limited access to conventional browser-level signals. This paper presents a behavioral feature analysis targeting anomalous click detection in mobile advertising environments, with particular attention to in-app browser (IAB) deployment contexts. A multi-dimensional feature taxonomy is proposed, organized across four analytical dimensions: temporal patterns, gestural characteristics, device fingerprinting, and network-level attributes. Using three publicly available datasets—TalkingData AdTracking (approximately 200 million click records), FDMA 2012 BuzzCity, and the labeled real-time bidding (RTB) logs reported in recent literature—this study evaluates the discriminative capacity of each feature dimension for characterizing click behavior anomalies indicative of fraudulent activity. Because the publicly available datasets used in this study originate from general mobile advertising platforms rather than IAB-specific instrumentation, the results are interpreted as evidence for mobile ad fraud detection that is directionally—but not conclusively—applicable to IAB environments. The analysis indicates that temporal aggregation features and device consistency metrics provide the strongest detection signals across the evaluated mobile advertising datasets. A discussion on the trade-off between detection granularity and user privacy constraints under data minimization principles is also provided. The findings contribute empirical guidance for practitioners developing fraud mitigation strategies within mobile advertising environments, including IAB contexts.
subject: Articles
  topic: section
subject: mobile in-app browser
  topic: keywords
subject: ad fraud detection
  topic: keywords
subject: behavioral feature analysis
  topic: keywords
subject: click anomaly detection
  topic: keywords
creator: Hao Cao
creator: Jiacheng Hu
creator: Chuankai Luo
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 96
ending page: 105
doi: 10.63575/CIA.2025.30207

Detecting Semantic Mismatches in XBRL Tag Mapping for SEC 10-K Filings: A Text Comparison and Historical Consistency Analysis
type: item
description: The accuracy of eXtensible Business Reporting Language (XBRL) tag mapping in SEC financial filings directly affects the reliability of automated financial analysis conducted by millions of investors through the EDGAR system. This study investigates semantic mismatches between financial statement line-item labels and their corresponding XBRL taxonomy elements in 10-K annual reports filed with the U.S. Securities and Exchange Commission. Drawing on SEC Financial Statement Data Sets and the XBRL US Data Quality Committee (DQC) validation rule library, this research analyzes custom tag usage patterns across filer categories and industry sectors over the period 2014–2024, with cross-sectional tabulation of filer-category rates at selected benchmark years (2014, 2017, 2019, and 2020) and aggregate trend data through 2024 drawn from SEC Office of Structured Disclosure publications. A tiered text comparison approach combining lexical similarity scoring (TF-IDF and BM25) with domain-specific contextual features is applied to evaluate the semantic alignment between reported line items and assigned taxonomy tags. Cross-period consistency analysis and SIC-code industry peer benchmarking are employed to identify anomalous tag selection changes that may indicate data quality degradation rather than substantive business changes. The findings reveal persistent heterogeneity in custom tag rates across industries and filer sizes, with specific tag mapping patterns that warrant targeted validation checkpoints. The proposed lightweight verification methods are designed for integration into existing disclosure management workflows without requiring complex computational infrastructure.
subject: Articles
  topic: section
subject: XBRL tag mapping
  topic: keywords
subject: semantic mismatch detection
  topic: keywords
subject: SEC financial reporting
  topic: keywords
subject: text similarity analysis
  topic: keywords
creator: Dun Liang
creator: Zijie Chen
creator: Chuanli Wei
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-13
publication date: 2026-02-13
starting page: 154
ending page: 163
doi: 10.63575/CIA.2026.40113

A Comparative Evaluation of Robust Loss Functions for Learning with Noisy Labels: From Synthetic to Real-World Annotations
type: item
description: Label noise is a pervasive challenge in supervised learning, as deep neural networks are prone to memorizing corrupted annotations during training. Robust loss functions have emerged as a principled approach to mitigate this issue without requiring auxiliary clean data or multi-network training pipelines. While numerous noise-tolerant and composite loss functions have been proposed in recent years, existing evaluations typically compare against only a limited subset of baselines under heterogeneous experimental protocols, making cross-method comparisons unreliable. This paper presents a systematic and unified comparative evaluation of eight representative robust loss functions—Cross-Entropy, Mean Absolute Error, Generalized Cross-Entropy, Symmetric Cross-Entropy, Active Passive Loss, Early-Learning Regularization, Generalized Jensen-Shannon divergence, and Active Negative Loss—across four distinct noise conditions: symmetric, asymmetric, instance-dependent, and real-world human annotation noise. All experiments are conducted on CIFAR-10 and CIFAR-100 with their corresponding CIFAR-N human-annotated noisy label sets (available at noisylabels.com), using a unified PreAct-ResNet-18 architecture and identical training protocol. The results reveal that composite and regularization-based losses consistently outperform simpler noise-tolerant alternatives, with the performance gap widening at elevated noise rates. Notably, method rankings observed under synthetic symmetric noise do not reliably transfer to real-world instance-dependent noise scenarios, highlighting the importance of evaluating under diverse noise conditions.
subject: Articles
  topic: section
subject: robust loss functions
  topic: keywords
subject: noisy labels
  topic: keywords
subject: label noise robustness
  topic: keywords
subject: deep learning
  topic: keywords
creator: Zhengchun Shang
creator: Wenlan Wei
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-03-17
publication date: 2025-03-17
starting page: 57
ending page: 65
doi: 10.63575/CIA.2025.30107

Statistical Anomaly Detection Approach for Field Mapping Validation in Enterprise Payroll Data Migration
type: item
description: Enterprise payroll system migrations from legacy platforms to modern cloud-based solutions present significant data quality challenges, particularly in field mapping validation. This research proposes a statistical anomaly detection framework specifically designed for automated validation of field mappings during Oracle Cloud Payroll to SAP SuccessFactors migrations. The framework employs distribution-based analysis techniques including Kolmogorov-Smirnov tests, Chi-square tests, and multi-threshold outlier detection mechanisms to identify systematic mapping errors, data type inconsistencies, and business rule violations. Experimental validation using a dataset of 50,000 employee records across 120 fields demonstrates that the proposed approach achieves 95.3% detection accuracy while reducing manual validation time by 42%. The framework successfully identified critical anomalies including decimal precision loss in salary calculations, date format inconsistencies in employment records, and null value propagation in benefit deductions. These results establish statistical anomaly detection as a viable automated quality assurance mechanism for enterprise payroll data migrations, offering substantial improvements over traditional rule-based validation methods.
subject: Articles
  topic: section
subject: data migration validation
  topic: keywords
subject: statistical anomaly detection
  topic: keywords
subject: field mapping quality
  topic: keywords
subject: payroll system migration
  topic: keywords
creator: Hao Cao
creator: Wangwang Shi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 137
ending page: 153
doi: 10.63575//CIA.2026.40112

Integrating Ovarian Reserve Biomarkers with Machine Learning for Gonadotoxicity Risk Prediction in Young Female Cancer Patients: A Scoping Review
type: item
description: Cancer treatment-related gonadotoxicity poses substantial threats to reproductive health in young female patients. Current risk stratification frameworks, including ASCO clinical practice guidelines and the Oncofertility Pediatric Initiative Network (O-PIN) classification, rely predominantly on population-level statistics and expert consensus, offering limited capacity for individualized prediction. This scoping review examines the emerging intersection of ovarian reserve biomarkers and machine learning (ML) methodologies for gonadotoxicity risk prediction in young female cancer patients. Through systematic analysis of published literature, publicly available datasets, and clinical decision support tools, this review identifies key biomarkers—including anti-Müllerian hormone (AMH), antral follicle count (AFC), and follicle-stimulating hormone (FSH)—that serve as critical input features for predictive models. ML approaches, particularly random forest classifiers, have demonstrated promising discriminative performance (area under the receiver operating characteristic curve up to 0.87) in predicting primary ovarian insufficiency following chemotherapy. This review further evaluates how artificial intelligence may facilitate personalized fertility preservation pathway selection and clinical workflow integration. Challenges including limited sample sizes, absence of external validation, data heterogeneity, and algorithmic fairness concerns are critically discussed. Recommendations for multicenter prospective registries and standardized evaluation frameworks are proposed to advance clinical translation of AI-assisted oncofertility decision-making.
subject: Articles
  topic: section
subject: oncofertility
  topic: keywords
subject: machine learning
  topic: keywords
subject: gonadotoxicity risk prediction
  topic: keywords
subject: ovarian reserve biomarkers
  topic: keywords
creator: Chuhan Zhang
creator: Muyu Liu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-07
publication date: 2026-02-07
starting page: 127
ending page: 136
doi: 10.63575/CIA.2026.40111

Fairness-Accuracy Trade-offs in AI Credit Scoring: A Comparative Evaluation of Reweighting and Resampling Strategies Under Multiple Fairness Constraints
type: item
description: The proliferation of artificial intelligence in financial risk assessment has introduced significant concerns regarding algorithmic fairness, particularly in credit scoring systems where biased predictions can disproportionately affect protected demographic groups. This study presents a comparative evaluation of two predominant pre-processing debiasing strategies—reweighting and resampling—applied to AI-based credit scoring algorithms. Using two publicly available benchmark datasets (the UCI German Credit dataset and the UCI Default of Credit Card Clients dataset), we systematically assess the accuracy-fairness trade-offs under three widely adopted fairness constraints: statistical parity, equal opportunity, and equalized odds. Experimental results across three baseline classifiers (Logistic Regression, Random Forest, and XGBoost) indicate that reweighting achieves a more favorable balance between predictive accuracy and fairness when evaluated under equal opportunity and equalized odds constraints, while resampling demonstrates stronger performance in reducing statistical parity differences. The magnitude of accuracy degradation varies substantially depending on the choice of fairness constraint, with equalized odds imposing the greatest accuracy cost across both datasets. These findings provide evidence-based guidance for financial institutions seeking to implement fairness-aware credit scoring systems and suggest that the selection of debiasing strategy should be contingent upon the specific fairness objective prioritized by regulatory and institutional requirements.
subject: Articles
  topic: section
subject: algorithmic fairness
  topic: keywords
subject: credit scoring
  topic: keywords
subject: pre-processing debiasing
  topic: keywords
subject: fairness-accuracy trade-off
  topic: keywords
creator: Ziyi Wang
creator: Jiawen Lai
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-04
publication date: 2026-02-04
starting page: 117
ending page: 126
doi: 10.63575/CIA.2026.40110

Causal Effect Evaluation of Personalized Reminder Strategies on Government Welfare Program Enrollment: A Propensity Score Matching Approach
type: item
description: Government digital service platforms face persistent challenges achieving optimal enrollment rates for welfare programs including SNAP and Medicaid. This research develops a causal inference framework quantifying personalized reminder intervention effects on enrollment completion, addressing selection bias through Propensity Score Matching, temporal dynamics via Difference-in-Differences, and endogeneity through Instrumental Variables. Methodology validation uses simulated observational data incorporating realistic population heterogeneity and non-random treatment assignment. Results demonstrate personalized reminders achieve 14.3 percentage point enrollment increases (p<0.001) after controlling confounding, with heterogeneous effects across age and digital literacy. The framework provides evidence-based guidance for optimizing government platforms per Executive Order 14058 on customer experience modernization.
subject: Articles
  topic: section
subject: Causal Inference
  topic: keywords
subject: Propensity Score Matching
  topic: keywords
subject: Digital Government Services
  topic: keywords
subject: Welfare Program Enrollment
  topic: keywords
creator: Yuyu Zhou
creator: Liqun Long
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: 106
ending page: 116
doi: 10.63575/CIA.2026.40109

AI-Enhanced What-If Scenario Analysis in Supply Chain Digital Twins: A Multi-Objective Trade-Off Perspective on Cost, Resilience, and Carbon Efficiency
type: item
description: Global supply chains face compounding disruptions driven by geopolitical tensions, tariff volatility, climate events, and demand uncertainty. Digital twin technology, which creates dynamic virtual replicas of physical supply chain networks, has emerged as a promising instrument for proactive decision-making through simulation and scenario analysis. This paper presents an analytical framework examining how artificial intelligence enhances What-If scenario analysis within supply chain digital twin environments, with a particular focus on multi-objective trade-offs among cost, resilience, service level, and carbon efficiency. By synthesizing recent literature, publicly available datasets, and U.S. federal supply chain digitalization initiatives, the study delineates the mechanisms through which AI enables multi-source data integration, automated scenario generation, cascading disruption prediction, and Pareto-optimal decision evaluation within digital twin architectures. Rather than proposing a new computational model or system implementation, this work offers a structured analytical perspective that bridges the gap between isolated single-module optimization studies and the integrated cross-functional decision-making that modern supply chains demand. The findings highlight a convergent relationship between resilience optimization and carbon reduction, and identify key technical and organizational challenges for practical implementation at the enterprise level.
subject: Articles
  topic: section
subject: supply chain digital twin
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: What-If scenario analysis
  topic: keywords
subject: multi-objective optimization
  topic: keywords
creator: Jialu Wang
creator: Ruoxi Jia
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-28
publication date: 2026-01-28
starting page: 97
ending page: 105
doi: 10.63575/CIA.2026.40108

AI-Enhanced Cross-Asset Liquidity Contagion Pathway Identification and Dynamic Hedging Strategy Optimization: Evidence from U.S. Equity, Bond, and Derivatives Markets
type: item
description: Cross-asset liquidity risk constitutes a persistent threat to financial stability, as demonstrated by the 2007–2009 Global Financial Crisis, the March 2020 COVID-19 market disruption, and the March 2023 banking stress episode. This study investigates the identification of time-varying liquidity contagion pathways across U.S. equity, fixed-income, and derivatives markets, and the optimization of dynamic hedging strategies informed by such pathways. A temporal graph attention network (T-GAT) is constructed from publicly available data—including FRED macroeconomic indicators, CBOE VIX, and Amihud illiquidity measures derived from Yahoo Finance—to capture directional liquidity spillovers among six representative asset proxies. Transfer entropy serves as the edge-weighting mechanism, and the graph structure is updated on rolling 60-day windows. A Proximal Policy Optimization (PPO)-based deep reinforcement learning agent leverages the T-GAT-derived contagion state to allocate hedging positions across assets. Out-of-sample evaluation over the 2022–2024 period indicates that the T-GAT achieves an F1-score of 0.731 in predicting weekly liquidity stress events, representing a moderate improvement over LSTM and static GCN baselines. The contagion-informed hedging agent yields a Sharpe ratio of 0.847, compared to 0.623 for conventional risk-parity allocation, with reductions in maximum drawdown during the March 2023 stress period.
subject: Articles
  topic: section
subject: cross-asset liquidity risk
  topic: keywords
subject: graph attention network
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: contagion pathway
  topic: keywords
creator: Jiahui Han
creator: Ruoxi Jia
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 89
ending page: 96
doi: 10.63575/CIA.2026.40107

A Comparative Evaluation of Deep Learning Paradigms for Low-Light Image Enhancement: From CNNs to Diffusion Models
type: item
description: Low-light image enhancement (LLIE) has attracted extensive research interest, with approaches spanning convolutional neural networks (CNNs), Retinex-based deep architectures, zero-shot learning, generative adversarial networks (GANs), Transformers, and diffusion models. Despite the proliferation of individual methods, a unified cross-paradigm evaluation under consistent experimental conditions remains absent in the existing literature. This paper presents a systematic comparative study of twelve representative LLIE methods drawn from six distinct algorithmic paradigms. All methods are evaluated on three widely adopted benchmark datasets—LOL-v1, LOL-v2-Real, and SID—using both full-reference metrics (PSNR, SSIM, LPIPS) and no-reference metrics (NIQE, BRISQUE), alongside computational efficiency analysis covering parameter count, floating-point operations, and inference latency. The experimental results indicate that Transformer-based approaches achieve a favorable balance between reconstruction fidelity and perceptual quality, while zero-shot methods offer substantial advantages in inference speed at the cost of quantitative performance. Diffusion-based methods produce perceptually compelling outputs but incur considerable computational overhead. Cross-dataset generalization tests further expose performance degradation across all paradigms when trained and tested on mismatched data distributions. These findings provide practical guidance for selecting LLIE methods under different deployment constraints and evaluation priorities.
subject: Articles
  topic: section
subject: low-light image enhancement
  topic: keywords
subject: deep learning
  topic: keywords
subject: comparative evaluation
  topic: keywords
subject: image restoration
  topic: keywords
creator: Danbing Zou
creator: Zijie Chen
creator: Zhipeng Ling
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-20
publication date: 2025-07-20
starting page: 85
ending page: 95
doi: 10.63575/CIA.2025.30206

A Comparative Analysis of Telemetry-Driven Anomaly DetectionApproaches for Dual-Purpose Operational and SecurityOptimization in Edge Computing Infrastructure
type: item
description: The proliferation of edge computing nodes in enterprise infrastructure has intensified the demand for anomaly detection methods capable of addressing both operational reliability and cybersecurity resilience within resource-constrained environments. This paper presents a comparative evaluation of six lightweight anomaly detection algorithms—Isolation Forest, One-Class SVM, LSTM-Autoencoder, K-Nearest Neighbors, Random Forest, and lightweight 1D-CNN—assessed across a dual-purpose framework that encompasses operational health monitoring and security threat detection. Experiments are conducted on six publicly available benchmark datasets: NASA C-MAPSS and Microsoft Azure Predictive Maintenance for degradation analysis, alongside Edge-IIoTset, UNSW-NB15, CIC-IDS2017, and TON_IoT for cybersecurity evaluation. Performance is measured through accuracy, F1-score, inference latency, and a proposed Dual-Purpose Efficiency Index under both high-performance server and resource-constrained ARM-based edge configurations. Results indicate that tree-based methods achieve the most favorable accuracy-to-latency ratio for edge deployment, while the LSTM-Autoencoder attains the highest detection quality at substantially greater computational cost. Cross-dataset generalization experiments reveal persistent domain shift challenges, particularly for low-frequency attack categories. These findings provide practical algorithm selection guidance for enterprise edge infrastructure scenarios where operational monitoring and security detection must coexist within constrained computational budgets.
subject: Articles
  topic: section
subject: edge computing
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: predictive maintenance
  topic: keywords
subject: intrusion detection
  topic: keywords
creator: Xiaoyi Long
creator: Jiacheng Hu
creator: Zhipeng Ling
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 79
ending page: 88
doi: 10.63575/CIA.2026.40106

Artificial Intelligence Applications Across Critical Domains: A Comprehensive Survey of Recent Advances in Healthcare, Finance, Security, and Beyond
type: item
description: Artificial intelligence (AI) has emerged as a transformative technology reshaping virtually every sector of modern society. From healthcare diagnostics and drug discovery to financial risk management, cybersecurity, autonomous transportation, and environmental sustainability, AI-driven methodologies have demonstrated remarkable capabilities in processing complex, heterogeneous data and generating actionable insights. This comprehensive survey examines 133 recent research contributions spanning the period from 2023 to 2026, systematically analyzing the state-of-the-art AI techniques employed across diverse application domains. We categorize these contributions into seven major thematic areas: (1) natural language processing and intelligent communication, (2) healthcare and biomedical sciences, (3) financial services and compliance, (4) cybersecurity and privacy preservation, (5) intelligent detection and pattern recognition, (6) explainable AI and cross-domain learning, and (7) document intelligence and sustainable development. For each domain, we discuss the key methodological innovations, evaluate reported performance metrics, identify common challenges, and highlight emerging research trends. Our analysis reveals that deep learning architectures, federated learning frameworks, and differential privacy mechanisms represent the most frequently adopted technical foundations, while multi-modal data fusion and graph-based learning are gaining increasing attention. We conclude by identifying critical open challenges including model interpretability, fairness-aware AI, and the need for robust evaluation benchmarks, and propose directions for future research that can bridge the gap between theoretical advances and practical deployment.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: deep learning
  topic: keywords
subject: natural language processing
  topic: keywords
subject: healthcare AI
  topic: keywords
subject: financial technology
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Grace Thompson
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-17
publication date: 2026-01-17
starting page: 61
ending page: 78
doi: 10.63575/CIA.2026.40105

A Comprehensive Survey on Artificial Intelligence Applications Across Critical Domains: Advances in Healthcare, Finance, Cybersecurity, and Emerging Fields
type: item
description: Artificial Intelligence (AI) has rapidly transformed numerous sectors including healthcare, finance, cybersecurity, transportation, and education. This comprehensive survey systematically reviews 120 recent publications spanning these domains, covering advances in natural language processing, deep learning, federated learning, reinforcement learning, differential privacy, and multi-modal data fusion. We organize the reviewed works in strict sequential order, with each reference cited exactly once, providing a coherent narrative that traces the evolution of AI methodologies across application areas. Tables and figures summarize the distribution of methods, domains, and privacy techniques. This survey serves as a unified reference for researchers and practitioners seeking to understand the breadth of contemporary AI applications.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: NLP
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: Financial AI
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Privacy-Preserving AI
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Olivia Bennett
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-14
publication date: 2026-01-14
starting page: 48
ending page: 60
doi: 10.63575/CIA.2026.40104

Uplift Modelling and Doubly Robust Causal Learning for Bank Marketing Targeting: Optimizing ROI with Coverage–Incremental Profit Curves
type: item
description: Marketing decisions should optimize incremental business value rather than predictive accuracy alone. A response classifier that ranks customers by subscription probability may inefficiently allocate budget by targeting individuals who would subscribe regardless of marketing intervention. Uplift modeling addresses this limitation by estimating the incremental causal impact of an action and enabling ROI-oriented targeting. This study formulates the “who to contact” problem as a causal decision task using the UCI Bank Marketing Dataset, specifically the bank-additional.csv subset (n = 4,119). Since the dataset includes only contacted clients, the treatment is operationalized as channel assignment, where cellular represents treatment and telephone represents control, as recorded in the contact variable. The leakage feature duration is removed following the dataset documentation. Two causal estimators are implemented: a two-model uplift estimator (T-learner) and a doubly robust learner that regresses an augmented inverse probability weighted (AIPW) pseudo-outcome with cross-fitting. Targeting policies are evaluated on a held-out test set using doubly robust policy evaluation with logistic-regression propensity scores clipped to [0.01, 0.99], and performance is assessed through AUUC, Qini, and coverage–incremental profit curves. On the test set (n = 1,236; seed = 42), the two-model uplift method achieves the best uplift ranking (AUUC = 0.0491, Qini = 0.0246). Outcome prediction AUC is 0.726 for a covariate-only model and 0.722 for an S-learner including the treatment indicator, demonstrating that predictive accuracy on outcomes does not identify incremental impact. Under a profit model with revenue (r = 100) and channel cost (c = 1), the two-model uplift generates 5,627.8 incremental profit at 10% coverage and peaks at 7,517.7 around 40% coverage. Sensitivity analysis further indicates that uplift-based targeting becomes increasingly advantageous as intervention costs ri…
subject: Articles
  topic: section
subject: uplift modeling
  topic: keywords
subject: causal inference
  topic: keywords
subject: heterogeneous treatment effects
  topic: keywords
subject: doubly robust estimation
  topic: keywords
subject: bank marketing
  topic: keywords
subject: ROI optimization
  topic: keywords
subject: AUUC
  topic: keywords
subject: Qini
  topic: keywords
subject: policy evaluation
  topic: keywords
creator: Nate Tham
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-11
publication date: 2026-01-11
starting page: 30
ending page: 47
doi: 10.63575/CIA.2026.40103

An Empirical Evaluation of Prompt Injection Detection and Refusal-Usefulness Tradeoffs Using the deepset/prompt-injections Dataset
type: item
description: Prompt injection is a leading security risk for large language model (LLM) applications because adversaries can embed instructions that override system intent, exfiltrate hidden prompts, or trigger unsafe tool use. This paper presents a fully empirical evaluation of prompt-injection defenses on the public deepset/prompt-injections dataset (662 labeled prompts; 399 benign, 263 injection/attack) using the official train/test split. We compare lightweight detectors that can be deployed as an input gate: a keyword-based rule system, word-level TF-IDF with Logistic Regression (LR), character-level TF-IDF with LR, calibrated linear Support Vector Machines (SVMs), and Complement Naive Bayes. We report attack success rate (ASR), detection F1, and a refusal rate–usefulness tradeoff. On the test split, the best detector is a character TF-IDF + calibrated linear SVM with F1=0.901 and ROC-AUC=0.977, substantially outperforming keyword rules (F1=0.125). When used as a refusal gate, the same family of character models reduces ASR from 1.000 (no gate) to 0.117 at 92.9% usefulness (1 - benign refusal) under a low-false-positive operating point derived from benign-score quantiles on the training split. Error analysis shows that most remaining bypasses are short, multilingual, or typo-heavy injections, indicating that robust defenses require character-level generalization and abstention tuning. Overall, our results quantify the operational tradeoffs between security and usability and provide reproducible baselines for prompt injection detection research.
subject: Articles
  topic: section
subject: prompt injection
  topic: keywords
subject: jailbreaks
  topic: keywords
subject: large language models
  topic: keywords
subject: text classification
  topic: keywords
subject: abstention
  topic: keywords
subject: security evaluation
  topic: keywords
creator: Daniel Brooks
creator: Samuel Turner
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-07-17
publication date: 2025-07-17
starting page: 66
ending page: 84
doi: 10.63575/CIA.2025.30205

Temporal Feature Learning Framework for Multi-dimensional Behavior Anomaly Detection in Digital Transaction Platforms
type: item
description: Digital transaction platforms face escalating challenges from sophisticated fraudulent activities that exploit multi-dimensional behavioral patterns across temporal sequences. This research presents a comprehensive temporal feature learning framework designed specifically for detecting anomalous behaviors in complex digital transaction environments. The proposed framework integrates adaptive threshold mechanisms with multi-scale temporal feature extraction, enabling real-time identification of suspicious activities while maintaining low false-positive rates. Through systematic analysis of transaction sequences, user interaction patterns, and network-level behavioral signatures, the framework achieves enhanced detection accuracy across diverse attack vectors including coordinated fraud campaigns and automated malicious account operations. Experimental validation demonstrates superior performance compared to conventional rule-based approaches, with detection precision reaching 94.7% and recall maintaining 91.3% across heterogeneous transaction datasets. The adaptive nature of the framework allows dynamic adjustment to evolving threat landscapes without requiring extensive retraining cycles. , , ,
subject: Articles
  topic: section
subject: temporal feature extraction
  topic: keywords
subject: behaviour anomaly detection
  topic: keywords
subject: transaction security
  topic: keywords
subject: adaptive threshold optimization
  topic: keywords
creator: Zhaoyang Luo
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-08
publication date: 2026-01-08
starting page: 12
ending page: 29
doi: 10.63575/CIA.2026.40102

Accelerating Clinical Trial Recruitment Through Automated Eligibility Screening with Multi-Modal Deep Learning
type: item
description: Clinical trial recruitment remains a critical bottleneck in medical research, with approximately 80% of trials experiencing significant delays due to inadequate patient enrollment. Traditional manual screening approaches require substantial time and resources while yielding suboptimal accuracy in matching patients to appropriate trials. This paper presents a novel multi-modal deep learning framework that integrates structured electronic health record data with unstructured clinical narratives to automate eligibility screening processes. The proposed architecture employs transformer-based encoders for clinical text processing, coupled with specialized neural networks for structured data analysis, unified through an attention-based fusion mechanism. Experimental validation demonstrates substantial improvements over existing methods, achieving 92.3% accuracy in eligibility prediction while reducing screening time by 73%. The framework successfully processes heterogeneous medical data sources, including diagnosis codes, laboratory results, medication histories, and physician notes, enabling rapid identification of suitable trial candidates. Performance analysis across multiple clinical domains confirms the generalizability and robustness of the approach.
subject: Articles
  topic: section
subject: Clinical trial recruitment
  topic: keywords
subject: eligibility screening
  topic: keywords
subject: multi-modal deep learning
  topic: keywords
subject: electronic health records
  topic: keywords
creator: Chuanli Wei
creator: Zhenghao Pan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-01-05
publication date: 2026-01-05
starting page: 1
ending page: 11
doi: 10.63575/CIA.2026.40101