AI and Data Science Journal (ADSJ) is an international peer-reviewed, open access journal.
XOR-EW: AN EXPLAINABLE AND COST-AWARE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR OPERATIONAL RISK EARLY WARNING IN FINANCIAL INSTITUTIONS
Operational-risk early-warning models in financial institutions must do more than rank rare events: they must remain stable under temporal change, express meaningful probabilities, respect finite investigation capacity, and provide auditable reasons. This paper presents XOR-EW (eXplainable Operational-Risk Early Warning), a decision-oriented framework that treats transaction as an observable proxy for a class of process- and control-related operational loss events. XOR-EW combines strict chronological controls, stability-aware champion selection, post-hoc probability calibration, an exposure-sensitive threshold under an alert-capacity constraint, ensemble-disagreement triage, and SHAP-based reason codes with explanation-stability checks. Experiments were executed on a publicly accessible mirror of the ULB/Worldline credit-card data. After exact deduplication, 227,102 transactions were split chronologically into training, calibration, policy, and test windows. A balanced random forest was selected without consulting the test set because it achieved the highest temporal-robustness score across the calibration and policy windows. On the 45,421-transaction test window, calibration reduced the Brier score from 0.000511 to 0.000421 and ten-bin expected calibration error from 0.003736 to 0.000304. Relative to the raw champion with an F1-selected threshold, the complete policy increased recall from 0.561 to 0.737, increased F1 from 0.719 to 0.785, and reduced normalized exposure cost from 0.731 to 0.358. An additional eight disagreement cases raised total capture to 0.772 while total workload remained 0.128% of transactions. The results support XOR-EW as a reproducible governance architecture, not as a claim of universal superiority or production readiness.
Copyright (c) 2024 YiZhen Lin; Ying Wang; MengLin Bian https://creativecommons.org/licenses/by/4.0
A DATA-DRIVEN MULTI-OBJECTIVE PRODUCTION SCHEDULING FRAMEWORK FOR BATTERY PACK MANUFACTURING UNDER MATERIAL ARRIVAL UNCERTAINTY
Material-arrival uncertainty can invalidate otherwise efficient battery-pack production schedules because cells, battery-management systems, enclosures, and cooling components become available at different assembly stages. This paper presents a reproducible data-to-decision framework that connects leakage-controlled arrival-delay learning with four-objective flexible-flow-shop scheduling. Median and 0.90-quantile histogram gradient-boosting models are trained on 180,519 public DataCo supply-chain records using a chronological 70/15/15 split. A split-conformal correction and stratified residual resampling generate scenario-dependent material availability without using post-arrival variables. The manufacturing layer is a transparent synthetic battery-pack benchmark with six assembly stages, two alternative machines per stage, three product families, sequence-dependent setups, due-date weights, and machine-energy tradeoffs. A calibrated quantile-residual NSGA-II (CQR-RNSGA-II) minimizes expected makespan, expected weighted tardiness, energy, and the 0.90 conditional value-at-risk of material-induced waiting. On the held-out logistics period, the learned 0.90 quantile reduces pinball loss by 32.1% relative to a global 90th-percentile buffer and attains 95.9% empirical upper coverage; the conformal correction is exactly zero because the raw discrete-target quantile is already conservative. Across nine independently seeded scheduling instances, the proposed method is best on the normalized four-objective compromise score for 20- and 30-job cases and essentially tied for 12 jobs. Relative to risk-neutral NSGA-II, it reduces material-wait CVaR by 12.1% at 20 jobs and 26.4% at 30 jobs, while revealing a nontrivial risk–tardiness tradeoff. All numerical claims are generated by the supplied code; no proprietary battery-factory data are asserted.
Copyright (c) 2024 ChangHao Zhang; YiNuo Wang https://creativecommons.org/licenses/by/4.0
CLARITY: A CLOSED-LOOP FRAMEWORK FOR DUPLICATE-AWARE FAILURE PREDICTION AND BUDGETED AUTOMATED REMEDIATION IN CLOUD INFRASTRUCTURE
Failure predictors for cloud infrastructure are often evaluated as isolated classifiers, although operators need calibrated risk, bounded actions, and feedback after remediation. This paper presents CLARITY, a closed-loop framework that couples duplicate-aware early prediction, probability calibration, a budget-constrained remediation policy, safety guardrails, and post-action feedback. The evaluation uses 575,061 labeled block-level event sequences from the public Loghub HDFS_v1 corpus. Exact duplicate sequences are grouped before splitting, and ten contradictory sequence patterns (46 occurrences) are removed rather than silently assigned. A prefix model combines normalized event frequencies and ordered transitions; XGBoost provides ranking and Platt scaling converts scores into operational probabilities. Five-fold grouped evaluation shows that observing 16 events yields an occurrence-weighted PR-AUC of 0.433±0.066 and a pattern-level PR-AUC of 0.751±0.139. Calibration reduces occurrence-weighted Brier score from 0.467 to 0.019 without changing ranking. In a trace-driven counterfactual policy, a 2% action budget captures 49.26% of anomalous remaining work and produces 11.10% normalized net savings under an explicitly stated 0.8 action efficacy and three-event action cost. Under controlled template-ID churn, feedback raises post-drift pattern PR-AUC from 0.190 to 0.470. The remediation results are policy simulations, not production intervention measurements. The study provides an implementable framework and a reproducible boundary between measured evidence and counterfactual assumptions.
Copyright (c) 2024 Minjae Rhee; JiTong Zou; YuXuan Qin https://creativecommons.org/licenses/by/4.0
DAS-GNN: A SCALABLE DISAGREEMENT-AWARE GRAPH NEURAL FRAMEWORK FOR ANOMALY DETECTION IN LARGE-SCALE NETWORKS
Graph neural networks can exploit relational context for anomaly detection, but repeated neighborhood expansion and dense graph construction make many designs difficult to deploy on large network-flow collections. This paper presents DAS-GNN, a decoupled disagreement-aware framework that converts flow records into a deterministic, degree-capped locality graph and performs graph propagation only once. The graph is built without labels by combining categorical signatures with multiple random-projection orderings, avoiding an all-pairs nearest-neighbor search. A cosine-gated context channel estimates locally stable behavior, while an explicit deviation channel preserves the absolute disagreement between each record and its neighborhood together with similarity, entropy, and categorical-agreement statistics. The resulting graph views are trained with ordinary dense mini-batches, so each epoch is independent of edge traversal. Experiments use the official UNSW-NB15 partition with 257,673 labeled network flows. Across three seeds, DAS-GNN obtains 0.9721±0.0004 ROC-AUC, 0.9794±0.0003 PR-AUC, and 0.8771±0.0022 F1, improving the matched MLP by 0.0042, 0.0029, and 0.0083, respectively. A strong LightGBM baseline remains best at 0.9852 ROC-AUC, which bounds the claim: the contribution is scalable graph-context enhancement rather than universal dominance over tabular boosting. On the complete graph, construction plus propagation takes 1.59 s in a controlled CPU scaling run; a 500,000-node stress graph takes 4.46 s.
Copyright (c) 2024 BoYuan Wang; WenYu Zhao; ZiMeng Wang https://creativecommons.org/licenses/by/4.0
DATA-DRIVEN ASSESSMENT OF PAVEMENT PERFORMANCE AND RESILIENCE UNDER EXTREME WEATHER USING THE LONG-TERM PAVEMENT PERFORMANCE PROGRAM
Extreme weather driven by a changing climate is placing unprecedented stress on road infrastructure, yet the quantitative link between weather extremes and observed pavement deterioration—and the resilience of pavements to that stress—remains poorly characterized at scale. This paper presents an end-to-end, reproducible framework that couples the U.S. Federal Highway Administration Long-Term Pavement Performance (LTPP) database with interpretable machine learning and a novel pavement resilience formulation. From 376 asphalt-concrete test sections monitored between 1988 and 2016, we construct a panel of 2,656 section-year records enriched with engineered extreme-weather indicators (freeze–thaw cycles, extreme-heat and freezing days, intense-precipitation days, and thermal range). A variance decomposition shows that 48% of the variance in surface cracking is attributable to unobserved site-specific factors, explaining why absolute distress in unseen sections is intrinsically hard to predict from climate alone (transfer R² ≈ 0). A linear mixed-effects model that controls for site and pavement age isolates intense-precipitation days (standardized β = 1.10, p < 0.001) and extreme-heat days (β = 0.70, p = 0.043) as the statistically significant weather accelerators of cracking, whereas a condition-informed forecasting model reaches R² = 0.53 with prior condition dominating the SHAP attribution. We introduce a Climate Stress Index (CSI) and a two-dimensional stress–impact positioning that yields a Demonstrated Resilience Score, separating climate-driven vulnerability from intrinsic fragility. The framework offers actionable screening for climate-adaptive pavement asset management.
Copyright (c) 2026 ShuXin Zhang; Lei Qiu https://creativecommons.org/licenses/by/4.0
TEAS: TOKEN- AND ENERGY-AWARE AUTOSCALING FOR COST-EFFICIENT LLM SERVING
Production platforms that serve large language models (LLMs) must continuously decide how many GPU replicas to keep online. Conventional autoscalers inherited from microservice stacks scale on request rate or coarse utilization, but LLM requests differ by up to three orders of magnitude in the number of tokens they process, arrive in extreme bursts, and run on replicas whose cold start takes minutes and whose idle power remains a large fraction of peak. We present TEAS (Token- and Energy-Aware Scaling), a horizontal autoscaler for LLM serving that (i) measures load in phase-weighted effective tokens rather than requests, (ii) forecasts load over the replica cold-start horizon with an empirical-residual safety margin, (iii) adapts its utilization headroom to the measured burstiness of the arrival process, and (iv) applies an energy-aware asymmetric hysteresis that delays scale-in until the projected idle-energy waste exceeds the energy cost of a replica restart. We evaluate TEAS in a trace-driven cluster simulator with a calibrated power and cost model, replaying real production traces from Azure LLM inference services alongside synthetic diurnal and composition-drift workloads derived from the real token distributions. Across all four workloads TEAS is the only dynamic policy that sustains at least 96% SLO attainment; on a bursty production code-assistant trace it improves attainment by 32 percentage points over a Kubernetes-HPA-style baseline and by 11 points over static peak provisioning, and on a 24-hour diurnal workload it meets a 95% SLO target at 11.6% lower monetary cost than peak provisioning. We release the simulator, policies, and experiment scripts.
Copyright (c) 2025 Dai Teng https://creativecommons.org/licenses/by/4.0
CLOUD-NATIVE DISTRIBUTED SYSTEMS FOR REAL-TIME PAYMENT INTELLIGENCE
Cloud-native payment platforms increasingly route every transaction through an online fraud-scoring microservice whose response time directly affects the end-to-end approval latency seen by merchants and cardholders. In this work we present a controlled empirical study of seven CPU-friendly classifiers for real-time payment fraud detection, evaluated on the public credit-card transaction dataset released by the ULB Machine Learning Group and Worldline. Models are trained on the first 60% of the transaction stream in chronological order and evaluated on the subsequent 40%, with thresholds tuned on an intermediate validation window under a precision-floor service-level constraint. For each detector we jointly report ranking quality (ROC-AUC, PR-AUC), operating-point precision and recall, single-row inference latency at the median, 95th and 99th percentiles, batch throughput, and serialized model footprint. We find that a regularized logistic-regression baseline attains a PR-AUC of 0.74 with a sub-millisecond p99 single-row latency and a 2 KB on-disk footprint, while a 50-tree random forest achieves the highest PR-AUC (0.81) at roughly an order of magnitude higher p99 latency and several hundred times the footprint. We further examine a sample-weighted incremental update of an online stochastic-gradient model over the validation stream and observe that, on this benchmark, simple incremental updates can shift the decision boundary in a way that increases false-positive load without improving recall, motivating more careful update strategies in production cloud-native pipelines. The implementation package can be released to support reproducibility.
Copyright (c) 2026 BingJie Zi https://creativecommons.org/licenses/by/4.0
AI-ASSISTED DISTRIBUTED RISK ASSESSMENT FOR ZERO-TRUST MICROSERVICES: A TAXONOMY AND CONCEPTUAL FRAMEWORK
Microservice and cloud-native architectures have enlarged the software attack surface faster than the methods used to assess its risk have adapted. Conventional vulnerability management remains centralised, static, focused on intrinsic severity, and disconnected from access control, none of which suits a distributed system in which risk depends on context and connectivity rather than on isolated flaws. This paper proposes a conceptual framework for distributed, AI-assisted risk assessment that addresses this mismatch. The framework is a synthesis of four research literatures, namely zero-trust architecture, microservice and cloud-native security, vulnerability analytics, and software risk management, and the paper first shows precisely where these literatures fail to meet. A six-dimensional taxonomy then organises the design space of distributed risk assessment, classifying approaches by signal source, granularity, analytical technique, risk model, zero-trust integration point, and temporality, and exposing the combination that no current approach occupies. To fill that gap, the paper proposes a four-layer framework in which lightweight per-service agents collect multi-source evidence, AI-assisted analytics estimate exploitation likelihood and detect anomalies, a graph-aware aggregation layer propagates risk across the service dependency graph, and an integration layer feeds a continuous per-service risk score into the zero-trust policy engine. An illustrative scenario shows how a contextually significant but low-severity vulnerability would be surfaced and contained. The contribution is conceptual rather than experimentally validated; the paper closes by setting out the open challenges of data, robustness, performance, consistency, explainability, and evaluation that lie between the framework and a deployable system.
Copyright (c) 2026 DaoQuan Zhou; XiongSheng Yi https://creativecommons.org/licenses/by/4.0
KNOWLEDGE GRAPH-DRIVEN DYNAMIC GENERATION TECHNOLOGY FOR FULL-SPECTRUM SCENARIO-BASED EMOTIONAL INTERVENTION PATHS: INNOVATION AND SOCIETAL VALUE
The field of child emotional intervention faces core pain points such as insufficient full-spectrum coverage, rigid intervention paths, and poor scenario adaptability. Traditional intervention models struggle to meet the personalized needs of different groups with common emotional distress, mild emotional deficits, and severe emotional disorders associated with Autism Spectrum Disorder (ASD). To address this issue, this study, based on the invention patent "A Knowledge Graph-Based Dynamic Generation Device for Scenario-Based Emotional Intervention Paths", constructs an emotional intervention technology system integrating knowledge graphs, reinforcement learning, and intelligent optimization algorithms. The technology innovatively designs a knowledge graph architecture with five core ontologies, achieves precise classification of full-spectrum groups through K-means clustering, dynamically generates initial intervention paths using Q-learning reinforcement learning, and completes real-time optimization adjustments based on gradient descent algorithms. Meanwhile, it integrates poetic cultural elements and multi-scenario adaptation mechanisms, balancing the scientificity, interestingness, and safety of interventions. Breaking the "one-size-fits-all" limitation of traditional interventions, the technology realizes the whole-process intelligence from path generation and real-time adjustment to scenario migration. Its wide application will significantly improve the accuracy and accessibility of emotional interventions, promote the inclusive development of child emotional health services, and possess important technological innovation value and far-reaching social significance.
Copyright (c) 2026 XiaoQing Wang https://creativecommons.org/licenses/by/4.0
THE INFLUENCE PATH AND CONSUMPTION TREND ANALYSIS OF NEW PRODUCTIVITY ON GUANGXI AGRICULTURAL PRODUCTS CONSUMER BEHAVIOR-AN EMPIRICAL STUDY BASED ON MARKET SURVEY TAKING BAISE MANGO IN GUANGXI AS AN EXAMPLE
This study investigates how New Quality Productivity (NQP) influences consumer behavior in the agricultural sector, using mango consumption in Baise, Guangxi as a case study. Based on 500 valid questionnaires and employing multiple regression, structural equation modeling (SEM), and ridge regression, the research identifies key impact pathways.The findings reveal, first, that direct factors—including improved product quality, optimized pricing, enhanced accessibility, and product diversification—all significantly drive consumer behavior, with price and accessibility being the most influential. Second, the analysis confirms significant mediating roles of information transparency, brand value, and consumption scenario reconstruction. However, not all paths are effective; for instance, quality upgrades do not affect behavior through transparency. Third, digital literacy and consumption concepts are crucial moderators that strengthen the NQP effect, while regional economic level and policy support show more limited moderating impacts.Accordingly, recommendations are proposed: optimize pricing and accessibility, enhance brand storytelling, and improve consumers' digital literacy to better harness NQP for agricultural development.
Copyright (c) 2025 JiaYan Wen https://creativecommons.org/licenses/by/4.0
AI ETHICS IN BUSINESS SCENARIOS: CORPORATE LEAP FORWARD, GOVERNANCE STRATEGIES, AND ACTIONABLE RISK FRAMEWORKS
As generative AI is integrated into core business processes such as marketing, risk control, recruitment, and customer service, enterprises face ethical and compliance risks, including bias and discrimination, privacy breaches, insufficient explainability, and unclear attribution of responsibility, while experiencing leaps in efficiency. This paper takes "AI Ethics in Business Applications" as its research object, drawing on the empirical paradigm of "time structure alignment + interval summary comparison" from the appendix paper. It selects publicly available reports on enterprise AI adoption rates and policy governance signals as samples to construct a reproducible descriptive empirical framework. On the one hand, it depicts the phased leaps in enterprise AI adoption rates from 2017 to 2025; on the other hand, it proposes actionable control points for business management based on AI governance frameworks (OECD AI Principles, NIST AI RMF). The study finds that enterprise AI adoption accelerated significantly around 2024, with generative AI penetration increasing even faster. Simultaneously, discussions on AI-related legislation and governance on the policy side are also on the rise, suggesting that enterprises need to upgrade "ethical governance" from a passive task of compliance departments to an integral part of their business strategy and risk management system. Finally, this article presents actionable governance guidelines to help companies reduce ethical risks and enhance trust while pursuing growth and innovation.
Copyright (c) 2026 JunWen Han https://creativecommons.org/licenses/by/4.0
FEDERATED LEARNING-AWARE MULTI-OBJECTIVE SCHEDULING FOR DISTRIBUTED EDGE-CLOUD ENVIRONMENTS
The proliferation of Internet of Things applications and latency-sensitive computing tasks has necessitated novel approaches to resource management across distributed edge-cloud architectures. Federated Learning has emerged as a compelling paradigm for collaborative model training without centralizing data, yet integrating Federated Learning into multi-objective scheduling frameworks presents significant challenges. This paper proposes a comprehensive scheduling strategy that accounts for Federated Learning-specific requirements including model convergence time, communication overhead, and data heterogeneity while optimizing multiple conflicting objectives such as makespan, energy consumption, and resource utilization. We develop a hierarchical scheduling architecture that coordinates tasks between mobile edge computing servers, base stations, and cloud data centers while maintaining Federated Learning protocol integrity. The proposed approach employs an adaptive multi-objective optimization algorithm with dynamic crossover and mutation probabilities that adjusts resource allocation based on Federated Learning training progress and system state. Experimental evaluation demonstrates that our Federated Learning-aware scheduling strategy with adaptive genetic algorithm parameters achieves superior performance compared to fixed-parameter approaches, reducing total system cost by approximately 28 percent while improving convergence speed by 35 percent. The framework effectively balances computation offloading decisions with Federated Learning communication patterns across the hierarchical mobile edge computing infrastructure, resulting in enhanced system efficiency for distributed edge-cloud environments. This work establishes foundations for integrating Federated Learning workflows into production-scale distributed computing infrastructures while addressing the unique challenges posed by privacy-preserving machine learning paradigms.
Copyright (c) 2025 Arjun Malhotra; Felix Baumann https://creativecommons.org/licenses/by/4.0
DYNAMIC INDEX PRUNING WITH REINFORCEMENT LEARNING FOR EFFICIENT LONG-CONTEXT GENERATION
The exponential growth of context lengths in large language models (LLMs) has introduced significant computational challenges, particularly in memory consumption and inference latency. This paper proposes a novel dynamic index pruning framework leveraging reinforcement learning to optimize long-context generation efficiency. By selectively retaining informative tokens while discarding redundant information, our approach reduces computational overhead without compromising generation quality. We formulate the pruning decision as a sequential decision-making problem and employ a policy gradient method to learn optimal pruning strategies. The framework draws inspiration from attention-based neural architectures, where alignment mechanisms dynamically focus on relevant context portions. Experimental results demonstrate that our method achieves up to 40% reduction in memory footprint and 35% improvement in inference speed while maintaining comparable performance on benchmark tasks. The proposed framework addresses the critical bottleneck of attention mechanism scaling and provides a practical solution for deploying LLMs in resource-constrained environments.
Copyright (c) 2025 JianYu Huang; PeiLin Xu; Andrew Collins https://creativecommons.org/licenses/by/4.0
IMPROVING REAL-TIME LOW-RESOURCE LANGUAGE EUPHEMISM TRANSLATION WITH AGENT KNOWLEDGE BASE
Translating euphemisms and politeness expressions between languages with distinct cultural norms presents significant challenges for automated translation systems, particularly for low-resource language (LRL) pairs. This study examines the effectiveness of integrating a custom-built knowledge base (KB) into large language models (LLMs) to enhance the translation of euphemistic expressions and politeness markers between Persian, Chinese, and English, with a focus on the Persian cultural schema of taarof. Two qualitative translation assessments were conducted: a Persian ceremonial speech translated into Chinese, and English audiovisual subtitles translated into Chinese and subsequently into Persian. Five translation versions were produced using LLMs with and without a domain-specific knowledge base, and evaluated by one Chinese lecturer and two native Persian speakers. Findings reveal that LLMs integrated with the knowledge base consistently outperform standalone models in terms of semantic accuracy and politeness transfer. However, divergent evaluations between evaluators highlight that politeness perception remains culturally contingent. This study demonstrates the value of knowledge base integration for LRL translation while underscoring the continued necessity of human’s machine translation post editing efforts.
Copyright (c) 2025 ZiQing Zhong; XiaoMing Lu; TianTian Gong https://creativecommons.org/licenses/by/4.0
HIERARCHICAL GNN FRAMEWORK FOR ENERGY-AWARE SCHEDULING IN GPU-ACCELERATED DISTRIBUTED SYSTEMS
Graphics Processing Units have become indispensable computational accelerators in modern distributed computing systems, powering applications ranging from deep learning to scientific simulations. However, the increasing computational demands and energy consumption of GPU-accelerated systems pose significant challenges for resource management and scheduling. Traditional scheduling algorithms often fail to capture the complex hierarchical structure and dynamic dependencies inherent in distributed GPU environments, leading to suboptimal energy efficiency and performance degradation. This paper proposes a novel hierarchical Graph Neural Network framework for energy-aware scheduling in GPU-accelerated distributed systems. The framework leverages the representational power of GNNs to model the complex interactions between computational tasks, GPU resources, and energy constraints at multiple hierarchical levels. By incorporating graph-structured representations of workload dependencies, resource topologies, and energy profiles, the proposed framework enables adaptive scheduling decisions that jointly optimize task completion time and energy consumption. Experimental results demonstrate that the proposed approach achieves up to 36 percent reduction in energy consumption while maintaining quality of service requirements compared to state-of-the-art scheduling methods. The hierarchical architecture effectively captures both fine-grained GPU-level characteristics and coarse-grained cluster-level dynamics, enabling scalable and efficient scheduling for large-scale distributed systems.
Copyright (c) 2025 Isabella Marino; Lukas Hoffmann https://creativecommons.org/licenses/by/4.0
ANALYSIS OF THE IMPACT OF SHOOTING SPORTS ON THE PSYCHOLOGICAL QUALITY OF TEENAGERS——BASED ON THE INVESTIGATION AND ANALYSIS OF SHOOTING CLUBS
The requirements for teenagers' psychological resilience are getting higher in modern society, shooting sports are beneficial to teenagers' mental health. This paper studys the impact of shooting on teenagers' psychological factors such as concentration, emotional control ability, adjustment ability, endurance and stress adaptation ability. By means of the cluster sampling, a questionnaire survey is conducted in shooting clubs, and the results of the questionnaire survey are systematically analyzed. The analysis shows that through shooting training, the three comprehensive indicators of self-control, concentration and psychological stability of the trainees have been significantly improved, and the effect is more obvious for trainees who have trained for more than one year. Moreover, the surveyed trainees all have a positive willingness to promote shooting sports.
Copyright (c) 2025 YunYi Wang https://creativecommons.org/licenses/by/4.0
PHYSICS-INFORMED NEURAL NETWORKS FOR ARBITRAGE-FREE VOLATILITY SURFACE CONSTRUCTION IN INCOMPLETE MARKETS
The construction of arbitrage-free volatility surfaces represents a fundamental challenge in quantitative finance, particularly in incomplete markets where hedging portfolios cannot perfectly replicate option payoffs. Traditional parametric models often fail to capture the complex dynamics of market-observed implied volatility structures, including the characteristic smile and skew patterns, while simultaneously satisfying no-arbitrage constraints. This paper introduces a novel framework that leverages Physics-Informed Neural Networks (PINNs) to construct arbitrage-free volatility surfaces in incomplete market settings. The proposed methodology integrates partial differential equation constraints derived from arbitrage-free conditions directly into the neural network training process through automatic differentiation and soft constraint penalties. By incorporating Dupire's local volatility equation and calendar-butterfly arbitrage constraints into a multi-objective loss function, our approach generates smooth, arbitrage-free implied volatility surfaces that accurately fit market data across different strikes and maturities. Numerical experiments using both synthetic data and real market observations from S&P 500 and VIX options demonstrate that the PINN-based framework substantially reduces calibration errors while maintaining theoretical consistency. The method exhibits particular strength in handling incomplete market scenarios where traditional parametric approaches produce inconsistent surfaces or violate no-arbitrage conditions.
Copyright (c) 2025 Hao Li; Martin Keller https://creativecommons.org/licenses/by/4.0
INTERPRETABLE TRANSFORMER MODELS FOR RELATIONSHIP ANALYSIS IN FINANCIAL DATA
The increasing complexity of financial transaction networks has necessitated the development of sophisticated analytical tools capable of uncovering intricate relationships within heterogeneous financial data while maintaining interpretability for regulatory compliance and fraud detection purposes. This paper presents a novel framework for interpretable transformer models specifically designed for relationship analysis in financial transaction networks. Our approach builds upon the foundational attention mechanisms developed for sequence-to-sequence tasks and extends them through graph attention networks to handle complex multi-entity financial relationships. The framework demonstrates how attention-based architectures can effectively analyze heterogeneous networks comprising card numbers, transaction identifiers, email domains, and card types to identify suspicious patterns and fraudulent activities. We develop specialized visualization techniques that reveal temporal dependencies in transaction sequences and cross-entity correlations in financial networks. Experimental evaluation on real-world financial transaction datasets demonstrates that our interpretable transformer models achieve superior performance in fraud detection while providing actionable insights for financial analysts. The framework successfully identifies complex fraud patterns including coordinated attacks across multiple entity types, suspicious email-card associations, and abnormal transaction behaviors, with interpretability metrics showing high alignment with expert fraud analyst assessments.
Copyright (c) 2025 Laura Chen; Robert Murphy https://creativecommons.org/licenses/by/4.0
AUTOMATED CYBERSECURITY INCIDENT RESPONSE: A REINFORCEMENT LEARNING APPROACH
Cybersecurity incident response is critical for defending digital infrastructures from evolving cyber threats. Traditional manual systems and rule-based automation methods cannot efficiently cope with dynamic and sophisticated attacks. This paper explores the application of reinforcement learning (RL) to automate cybersecurity incident response. By modeling the response process as an RL problem, where an agent learns from interactions with the environment, the proposed system aims to enhance detection accuracy, minimize response times, and reduce false positives. Experimental results demonstrate the system's ability to mitigate threats effectively, showing that RL can significantly improve the efficiency and scalability of cybersecurity defenses. This approach leverages machine learning to automate decisions in real-time, adapting to evolving threats and optimizing incident response strategies. The integration of RL in incident response has the potential to dramatically reduce human error, improve system adaptability, and scale efficiently in complex, high-volume environments.
Copyright (c) 2025 MingJie Zhao; Rui Chen https://creativecommons.org/licenses/by/4.0
DECENTRALIZED TRAFFIC REGULATION IN ADVERTISING NETWORKS USING ENERGY-AWARE HIERARCHICAL DEEP REINFORCEMENT LEARNING
Online advertising networks face increasing challenges in traffic regulation due to the decentralized nature of ad serving, fluctuating demand patterns, and growing energy consumption concerns. Traditional centralized traffic management approaches fail to scale effectively across distributed advertising infrastructures while struggling to balance Quality of Service (QoS) requirements with energy efficiency constraints. The heterogeneous nature of advertising traffic, including display ads, video content, and real-time bidding requests, requires sophisticated regulation mechanisms that can adapt to varying workload characteristics and network conditions. This study proposes an Energy-Aware Hierarchical Deep Reinforcement Learning (EA-HDRL) framework for decentralized traffic regulation in advertising networks. The framework employs a multi-tier architecture where regional controllers manage local traffic optimization while a global coordinator ensures network-wide efficiency and energy conservation. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) algorithms enable adaptive traffic regulation policies that simultaneously optimize throughput, latency, and energy consumption across distributed advertising infrastructure.Experimental evaluation using real-world advertising network traces demonstrates that the proposed framework achieves 52% improvement in traffic throughput while reducing energy consumption by 41% compared to traditional centralized regulation methods. The hierarchical approach successfully balances local optimization autonomy with global coordination requirements, resulting in 36% better QoS compliance and 28% reduction in network congestion incidents.
Hierarchical deep reinforcement learning
Copyright (c) 2025 JingYi Cao https://creativecommons.org/licenses/by/4.0
HIERARCHICAL DEEP REINFORCEMENT LEARNING FRAMEWORK FOR ADAPTIVE CPU SCHEDULING IN HYBRID TRANSACTIONAL-ANALYTICAL DATABASES
Hybrid Transactional-Analytical Processing (HTAP) databases face significant challenges in CPU resource allocation due to the conflicting requirements of Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads. Traditional static scheduling approaches fail to adapt to dynamic workload patterns, leading to suboptimal performance and resource utilization inefficiencies. The diverse characteristics of transactional and analytical queries require sophisticated scheduling strategies that can balance latency-sensitive transaction processing with throughput-oriented analytical operations.This study proposes a Hierarchical Deep Reinforcement Learning (HDRL) framework for adaptive CPU scheduling in HTAP database systems. The framework employs a two-level architecture where a high-level agent manages workload prioritization between OLTP and OLAP components, while low-level agents optimize resource allocation within each processing type. Deep Q-Networks (DQN) and Actor-Critic algorithms enable dynamic adaptation to changing workload patterns and system conditions.Experimental evaluation using industry-standard benchmarks demonstrates that the proposed framework achieves 34% improvement in overall system throughput while reducing OLTP query latency by 28% compared to traditional scheduling methods. The hierarchical approach successfully balances competing workload demands and adapts to varying system conditions, resulting in enhanced resource utilization efficiency and improved Quality of Service (QoS) guarantees across both transactional and analytical processing requirements.
Copyright (c) 2025 Nur Aisyah; Mehdi Benali https://creativecommons.org/licenses/by/4.0
LEGAL ATTRIBUTES OF COMMERCIAL DATA AND APPROACHES TO INTELLECTUAL PROPERTY PROTECTION
As a core production factor in the digital economy, the legal attributes and protection pathways of commercial data have become a research hotspot in the field of legal studies. This paper begins by examining the legal nature of commercial data and its attributes under intellectual property rights, establishing the legitimacy foundation for intellectual property protection of commercial data through labor property theory, utilitarian theory, and transaction cost theory. Besides, it proposes a “limited rights + fair use” governance model to address current legal dilemmas in data rights allocation. The study further recommends approaches such as strengthening technical governance and collaborative regulation, promoting international rule harmonization, to balance innovation incentives with public interests. Ultimately, these measures aim to achieve efficient circulation and value realization of commercial data.
Copyright (c) 2025 Zhaoxia Deng https://creativecommons.org/licenses/by/4.0
DATA-DRIVEN DECISION-MAKING SYSTEM FOR INJECTION MOLDING PRODUCTION AND MAINTENANCE
A data-driven injection molding production and maintenance decision-making system is designed to address the issues of low efficiency and poor real-time performance in traditional data collection models, meeting the modern industrial needs for high reliability and intelligence. The system adopts a three-layer architecture, including data collection, edge computing, and maintenance decision-making layers. It achieves real-time collection and processing of multi-source heterogeneous data to assess equipment health status dynamically and predict failures. The data collection layer integrates sensor, PLC, and visual device data; the edge computing layer processes key parameters through lightweight models to reduce cloud-side pressure; the maintenance decision-making layer predicts the remaining life of the equipment using the Weibull distribution model and optimizes maintenance strategies. The system proposes a quantitative evaluation index for the health of the injection molding machine and utilizes a weighted fusion algorithm for accurate maintenance decisions, significantly reducing operational costs and improving production efficiency, providing a feasible technical solution for intelligent manufacturing.
Copyright (c) 2025 DuAng Chen; XinJie Zhou; Fan Xin; ShiWei Xu https://creativecommons.org/licenses/by/4.0
EEG-BASED EMOTION RECOGNITION USING ENSEMBLE LEARNING MODEL
Throughout history, emotion recognition has played a pivotal role across various fields. Due to technology limitation, emotions were traditionally evaluated through interviews or questionnaires, methods prone to subjectivity even among psychologists. Hence, development of automated emotion recognition system is necessary. In recent years, prodigious process has been made in this area, with EEG-based emotion recognition becoming increasingly popular. However, the models that used to classify the EEG-based emotion data are still not powerful enough. In this paper, we propose an efficient model for emotion classification, utilizing EEG data from DEAP dataset. The neural signals are first decomposed into the Gamma, Alpha, Beta, and Theta bands according to their respective frequencies, and preprocessed using Welch method. A hybrid model, combining Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA), is employed for classification. This hybrid model distinguishes among three emotional states: positive, negative, and neutral. Compared to existing work, our ensemble learning model achieves a higher accuracy, performs with 95.99% and 95.68% for arousal and valence. Furthermore, our method successfully mitigates the weaknesses of these individual base models, bringing in a more robust emotion recognition framework.
BREAKING THE "WORD POVERTY" AND MAKING THE LANGUAGE LIVELY - INVESTIGATION AND ANALYSIS ON THE CURRENT SITUATION OF COLLEGE STUDENTS’ WORD POVERTY AND ITS INFLUENCING FACTORS
In contemporary society, the phenomenon of lexical poverty is becoming increasingly prominent, as reported by the Chinese Youth Daily, which highlighted the lexical poverty issue among young people. Survey data indicates that over half of the interviewed youth feel their language expr ession abilities are gradually declining. Additionally, 47.1% of the interviewed youth believe they have a limited vocabulary and exhibit repetitive expressions. They often experience cognitive difficulties during speech, such as mental blockages, forgetting words while writing, and experiencing linguistic lapses. For university students, proficient language expression is one of their essential competencies. However, the research on lexical poverty among university students in China is currently limited, necessitating a deeper exploration of this phenomenon. This study employed a questionnaire survey method to investigate the lexical poverty status and influencing factors among university students in four r egions of Guangxi. A total of 925 questionnaire responses were collected, of which 772 were deemed valid after removing invalid responses. Basic descriptive analysis of the data provided insights into its distribution. Subse quently, differential analysis and multiple response analysis were conducted to explore the association between respondent characteristics and research questio ns. Finally, a structural equation model was constructed to explore the complex relationship between university students' understanding of lexical poverty and their behaviors. The research findings reveal that lexical poverty is widespread among university students, posing a significant challenge to their language expression abilities. The main reasons include inadequate reading habits, internet usage patterns, and limited face-to-face communication. Additionally, educational background and academic disciplines also influence the occurrence of lexical poverty. Students with higher educational levels experience r elatively fewer instances of lexical poverty compared to their counterparts, with students in STEM fields being more susceptible than those in humanities. Moreover, the frequency of lexical poverty increases significantly with the growing reliance on the internet, declining reading habits, intensified fragmented reading patterns, reduced face-to-face communication, and decreased participation in organized communication activities at universities.In light of these findings, it is recommended to strengthen guidance and regulation on internet information, conduct vocabulary education and language training activities, and actively encourage and support students to engage in debate, public speaking, writing, and other practical activities to alleviate lexical poverty among university students.
Copyright (c) 2025 Xing Zeng https://creativecommons.org/licenses/by/4.0
FROM "THE LIGHT OF THE COUNTRY" TO "THE PHENOMENAL EXPLOSION" —— THE AUDIENCE RELATIONSHIP AND THE WAY TO "BREAK THE CIRCLE" IN DECODING "NEZHA 2"
"China’s ‘Fourteenth Five-Year Plan’ explicitly proposes to promote the development of the digital culture industry. Animation, as an important carrier for ‘telling Chinese stories well, ’ has received special funding support. The government advocates for the ‘creative transformation of fine traditional Chinese culture, ’ promoting the exploration of themes such as mythology, history, and intangible cultural heritage in national animation, which aids in cultural exports and national identity. As a sequel to the domestic animated film ‘Ne Zha: The Devil Child Makes His Debut, ’ ‘Ne Zha 2’ continues the industry topic of‘the rise of national animation’with the accumulated popularity of the national IP and audience expectation s from the predecessor. This study uses a questionnaire survey method, focusing on the population from four regions in Guangxi, aiming to analyze how ‘Ne Zha 2’ breaks through the audience layer limitations of animated films to become a universally popular hit and to summarize its ‘breakthrough’ market strategies, providing a reference for the national animation industry. A total of 528 questionnaires were collected, and after removing invalid questionnaires, 430 were left. Through basic descriptive analysis of the data, we gained an understanding of the general distribution. Then, differential analysis and multiple response analysis were conducted to explore the correlation between the respondents’ basic information and the rese arch questions. Finally, a structural equation model was constructed to explore the complex relationship between the subjects’ understanding of ‘Ne Zha 2’ and its development strategies. The study found that the audience’s understanding of ‘Ne Zha 2’ is generally high, and ‘Ne Zha 2’ has also had a certain impact on the future development of national animation. The reasons for ‘Ne Zha 2’ being able to truly ‘break th rough’ include content and emotional, technical, and marketing aspects. By delving into the reasons for the success of ‘Ne Zha 2’, we can further promote the development of the domestic animated film industry and build a more prosperous and innovative cultural industry structure. Only by fully understanding and drawing on the successful experience of ‘Ne Zha 2’ can we better meet a more competitive film and television market and achieve the long-term development goals of the national cultural industry.
Copyright (c) 2025 Baoshuo Wang https://creativecommons.org/licenses/by/4.0
5G ABNORMAL SIGNALING DETECTION BASED ON AUTO-ENCODER
With the proliferation of 5G networks, increasing attention has been directed toward associated security risks. The N4 interface, serving as the interface between the user plane and control plane in 5G architecture, encompasses functionalities including session management and policy enforcement, and is susceptible to risks such as session hijacking. PFCP, as the application layer protocol of the N4 interface, can be effectively monitored through anomaly detection to identify abnormal behaviors within the N4 interface. Consequently, this paper proposes an autoencoder algorithm model implemented with Transformer neural networks. During the training process, the model learns the sequential characteristics of normal PFCP bidirectional flows. In the detection phase, data is processed through the encoder and decoder, and the model computes the Euclidean distance between the reconstructed data and the original data to derive an anomaly score. Additionally, this paper employs publicly available datasets to experimentally validate the efficacy of the algorithmic model in detecting PFCP traffic anomalies.
Copyright (c) 2025 YaDi Fu https://creativecommons.org/licenses/by/4.0
A TRANSFER LEARNING FRAMEWORK FOR CLINICAL TEXT CLASSIFICATION USING PRETRAINED LANGUAGE MODELS
Clinical text classification is a critical task in medical informatics, enabling applications such as automated diagnosis coding, patient outcome prediction, and adverse event detection. However, the scarcity of labeled medical data and the domain-specific language used in clinical records pose significant challenges. This paper proposes a transfer learning framework that leverages pretrained language models—specifically BioBERT and ClinicalBERT—for clinical text classification tasks. The framework incorporates a domain-adaptive fine-tuning strategy and task-specific adaptation layer to bridge the gap between general language understanding and specialized medical text. Experimental results on benchmark clinical datasets demonstrate substantial improvements in classification accuracy, F1-score, and robustness compared to traditional supervised learning approaches.
Copyright (c) 2025 MeiLin Cheng https://creativecommons.org/licenses/by/4.0
CURRENT ADVANCED MEDICAL IMAGE PROCESSING METHOD—INTEGRATION AND INNOVATION OF DEEP LEARNING MODELS AND TRADITIONAL ALGORITHMS
Medical image processing has witnessed remarkable progress in recent years, driven by the integration of advanced algorithms and artificial intelligence techniques. This review comprehensively examines the state-of-the-art methods in medical image processing, both domestically and internationally, with a focus on deep learning models and other prominent algorithms. We delve into their applications across various medical imaging modalities, analyze their strengths and limitations, and discuss future development trends, aiming to provide valuable insights for researchers and practitioners in this field.
Copyright (c) 2025 ShouTong Huang https://creativecommons.org/licenses/by/4.0
CAUSAL INFERENCE-BASED DIGITAL PAYMENT FRAUD DETECTION: FROM FINANCIAL SECURITY TO ECONOMY-WIDE RESILIENCE
With the explosive expansion of digital payment systems, financial fraud has now become one of the most serious threats facing economic stability in many sectors. This paper presents a critical analysis of how methods for causal inference could contribute to improvement of fraud detection by revealing underlying patterns rather than correlations. The paper presents a theoretical model that integrates machine learning and causal analysis methods to improve the differentiation between legitimate and fraudulent transactions. Through the detection of interaction networks and behavioural patterns, the methodology attains a higher level of accuracy in identifying sophisticated fraud schemes than a traditional rule-based system. The results propose that causality methods do not just mitigate false positives in financial industries but they also present actionable risk controls for the application domain of e-commerce, healthcare and digital transaction processing. The research serves to enhance financial security efforts by designing a more thorough methodology which is also flexible enough to evolve in response to advances in fraud techniques. Next steps include generalizing causal models to cover new threats in decentralized finance and cross-border payments.
Copyright (c) 2025 LuQing Ren https://creativecommons.org/licenses/by/4.0