AI and Data Science Journal
type: channel
description: AI and Data Science Journal (ADSJ) is an international peer-reviewed, open access journal.
publisher: UPub Science
language: en
publication name: AI and Data Science Journal
issn: 3078-7394
items: KNOWLEDGE GRAPH-DRIVEN DYNAMIC GENERATION TECHNOLOGY FOR FULL-SPECTRUM SCENARIO-BASED EMOTIONAL INTERVENTION PATHS: INNOVATION AND SOCIETAL VALUE
items: PHYSICS-INFORMED NEURAL NETWORKS FOR ARBITRAGE-FREE VOLATILITY SURFACE CONSTRUCTION IN INCOMPLETE MARKETS
items: ANALYSIS OF THE IMPACT OF SHOOTING SPORTS ON THE PSYCHOLOGICAL QUALITY OF TEENAGERS——BASED ON THE INVESTIGATION AND ANALYSIS OF SHOOTING CLUBS
items: HIERARCHICAL GNN FRAMEWORK FOR ENERGY-AWARE SCHEDULING IN GPU-ACCELERATED DISTRIBUTED SYSTEMS
items: https://upubscience.com/adsj/article/view/631
items: IMPROVING REAL-TIME LOW-RESOURCE LANGUAGE EUPHEMISM TRANSLATION WITH AGENT KNOWLEDGE BASE
items: DYNAMIC INDEX PRUNING WITH REINFORCEMENT LEARNING FOR EFFICIENT LONG-CONTEXT GENERATION
items: FEDERATED LEARNING-AWARE MULTI-OBJECTIVE SCHEDULING FOR DISTRIBUTED EDGE-CLOUD ENVIRONMENTS
items: AI ETHICS IN BUSINESS SCENARIOS: CORPORATE LEAP FORWARD, GOVERNANCE STRATEGIES, AND ACTIONABLE RISK FRAMEWORKS
items: CURRENT ADVANCED MEDICAL IMAGE PROCESSING METHOD—INTEGRATION AND INNOVATION OF DEEP LEARNING MODELS AND TRADITIONAL ALGORITHMS
items: CAUSAL INFERENCE-BASED DIGITAL PAYMENT FRAUD DETECTION: FROM FINANCIAL SECURITY TO ECONOMY-WIDE RESILIENCE
items: HIERARCHICAL DEEP REINFORCEMENT LEARNING FRAMEWORK FOR ADAPTIVE CPU SCHEDULING IN HYBRID TRANSACTIONAL-ANALYTICAL DATABASES
items: DECENTRALIZED TRAFFIC REGULATION IN ADVERTISING NETWORKS USING ENERGY-AWARE HIERARCHICAL DEEP REINFORCEMENT LEARNING
items: INTERPRETABLE TRANSFORMER MODELS FOR RELATIONSHIP ANALYSIS IN FINANCIAL DATA
items: A TRANSFER LEARNING FRAMEWORK FOR CLINICAL TEXT CLASSIFICATION USING PRETRAINED LANGUAGE MODELS
items: PREDICTION OF AIR QUALITY INDEX BASED ON ARIMA AND LSTM MODELS—TAKE GUILIN AS AN EXAMPLE
items: FROM "THE LIGHT OF THE COUNTRY" TO "THE PHENOMENAL EXPLOSION" —— THE AUDIENCE RELATIONSHIP AND THE WAY TO "BREAK THE CIRCLE" IN DECODING "NEZHA 2"
items: 5G ABNORMAL SIGNALING DETECTION BASED ON AUTO-ENCODER
items: AUTOMATED CYBERSECURITY INCIDENT RESPONSE: A REINFORCEMENT LEARNING APPROACH
items: LEGAL ATTRIBUTES OF COMMERCIAL DATA AND APPROACHES TO INTELLECTUAL PROPERTY PROTECTION
items: DATA-DRIVEN DECISION-MAKING SYSTEM FOR INJECTION MOLDING PRODUCTION AND MAINTENANCE
items: EEG-BASED EMOTION RECOGNITION USING ENSEMBLE LEARNING MODEL
items: 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
items: PUBLIC DEBT MANAGEMENT WITH ADVANCED DATA ANALYTICS
items: A COMPREHENSIVE FRAMEWORK FOR MODERN DATA CLEANING: INTEGRATING STATISTICAL AND MACHINE LEARNING APPROACHES WITH PERFORMANCE ANALYSIS
items: A TEMPORAL LOGICAL ATTENTION NETWORK APPROACH TO ANOMALY DETECTION IN DISTRIBUTED SYSTEMS LOGS
items: OPTIMIZED LITHOGRAPHIC HOTSPOT DETECTION WITH MULTI-TASK DEEP LEARNING
items: ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF HIGH-FREQUENCY TRADING STRATEGIES: ENHANCING PERFORMANCE AND MANAGING MARKET IMPACT
items: ADAPTIVE PHYSICAL SECURITY: REDEFINING ORGANIZATIONAL SAFETY IN DYNAMIC ENVIRONMENTS
items: EXAMINING THE MOTIVATION-PERFORMANCE LINK AMONG CONSTRUCTION EMPLOYEES IN WESTERN CAPE, SOUTH AFRICA

KNOWLEDGE GRAPH-DRIVEN DYNAMIC GENERATION TECHNOLOGY FOR FULL-SPECTRUM SCENARIO-BASED EMOTIONAL INTERVENTION PATHS: INNOVATION AND SOCIETAL VALUE
type: item
description: 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.
subject: Research Article
  topic: section
subject: Emotional intervention
  topic: keywords
subject: Knowledge graph
  topic: keywords
subject: dynamic path generation
  topic: keywords
subject: Full-spectrum coverage
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Social inclusiveness
  topic: keywords
subject: Child mental health
  topic: keywords
creator: XiaoQing Wang
rights: Copyright (c) 2026 XiaoQing Wang https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 4
ending page: 9
doi: 10.61784/adsj3033

PHYSICS-INFORMED NEURAL NETWORKS FOR ARBITRAGE-FREE VOLATILITY SURFACE CONSTRUCTION IN INCOMPLETE MARKETS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Physics-informed neural networks
  topic: keywords
subject: Arbitrage-free constraints
  topic: keywords
subject: Volatility surface
  topic: keywords
subject: Incomplete markets
  topic: keywords
subject: Volatility smile
  topic: keywords
subject: Heston model
  topic: keywords
creator: Hao Li
creator: Martin Keller
rights: Copyright (c) 2025 Hao Li; Martin Keller https://creativecommons.org/licenses/by/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 50
ending page: 60
doi: 10.61784/adsj3026

ANALYSIS OF THE IMPACT OF SHOOTING SPORTS ON THE PSYCHOLOGICAL QUALITY OF TEENAGERS——BASED ON THE INVESTIGATION AND ANALYSIS OF SHOOTING CLUBS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Shooting sports
  topic: keywords
subject: Psychological quality
  topic: keywords
subject: Sampling survey
  topic: keywords
creator: YunYi Wang
rights: Copyright (c) 2025 YunYi Wang https://creativecommons.org/licenses/by/4.0
date: 2025-10-27
publication date: 2025-10-27
starting page: 61
ending page: 68
doi: 10.61784/adsj3025

HIERARCHICAL GNN FRAMEWORK FOR ENERGY-AWARE SCHEDULING IN GPU-ACCELERATED DISTRIBUTED SYSTEMS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Graph neural networks
  topic: keywords
subject: Energy-aware scheduling
  topic: keywords
subject: GPU computing
  topic: keywords
subject: Distributed systems
  topic: keywords
subject: Hierarchical framework
  topic: keywords
subject: Resource management
  topic: keywords
subject: Deep learning
  topic: keywords
creator: Isabella Marino
creator: Lukas Hoffmann
rights: Copyright (c) 2025 Isabella Marino; Lukas Hoffmann https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 69
ending page: 79
doi: 10.61784/adsj3027

https://upubscience.com/adsj/article/view/631
type: item
title: 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
description: 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.
subject: Research Article
  topic: section
subject: New quality productivity
  topic: keywords
subject: Agricultural product consumption
  topic: keywords
subject: Consumer behavior path
  topic: keywords
subject: Moderating effect
  topic: keywords
creator: JiaYan Wen
rights: Copyright (c) 2025 JiaYan Wen https://creativecommons.org/licenses/by/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 1
ending page: 18
doi: 10.61784/adsj3028

IMPROVING REAL-TIME LOW-RESOURCE LANGUAGE EUPHEMISM TRANSLATION WITH AGENT KNOWLEDGE BASE
type: item
description: 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.
subject: Research Article
  topic: section
subject: Taarof
  topic: keywords
subject: Low-resource languages
  topic: keywords
subject: Knowledge base
  topic: keywords
subject: Large language models
  topic: keywords
creator: ZiQing Zhong
creator: XiaoMing Lu
creator: TianTian Gong
rights: Copyright (c) 2025 ZiQing Zhong; XiaoMing Lu; TianTian Gong https://creativecommons.org/licenses/by/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 19
ending page: 25
doi: 10.61784/adsj3029

DYNAMIC INDEX PRUNING WITH REINFORCEMENT LEARNING FOR EFFICIENT LONG-CONTEXT GENERATION
type: item
description: 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.
subject: Research Article
  topic: section
subject: Dynamic pruning
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Long-context generation
  topic: keywords
subject: Large language models
  topic: keywords
subject: Attention optimization
  topic: keywords
subject: Computational efficiency
  topic: keywords
creator: JianYu Huang
creator: PeiLin Xu
creator: Andrew Collins
rights: Copyright (c) 2025 JianYu Huang; PeiLin Xu; Andrew Collins https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 26
ending page: 34
doi: 10.61784/adsj3030

FEDERATED LEARNING-AWARE MULTI-OBJECTIVE SCHEDULING FOR DISTRIBUTED EDGE-CLOUD ENVIRONMENTS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Federated learning
  topic: keywords
subject: Multi-objective optimization
  topic: keywords
subject: Task scheduling
  topic: keywords
subject: Edge computing
  topic: keywords
subject: Cloud computing
  topic: keywords
subject: Mobile edge computing
  topic: keywords
subject: Adaptive genetic algorithm
  topic: keywords
creator: Arjun Malhotra
creator: Felix Baumann
rights: Copyright (c) 2025 Arjun Malhotra; Felix Baumann https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 35
ending page: 46
doi: 10.61784/adsj3031

AI ETHICS IN BUSINESS SCENARIOS: CORPORATE LEAP FORWARD, GOVERNANCE STRATEGIES, AND ACTIONABLE RISK FRAMEWORKS
type: item
description: 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.
subject: Research Article
  topic: section
subject: AI ethics
  topic: keywords
subject: Business applications
  topic: keywords
subject: Governance
  topic: keywords
subject: Risk management
  topic: keywords
subject: Trustworthy AI
  topic: keywords
creator: JunWen Han
rights: Copyright (c) 2026 JunWen Han https://creativecommons.org/licenses/by/4.0
date: 2026-02-12
publication date: 2026-02-12
starting page: 1
ending page: 3
doi: 10.61784/adsj3032

CURRENT ADVANCED MEDICAL IMAGE PROCESSING METHOD—INTEGRATION AND INNOVATION OF DEEP LEARNING MODELS AND TRADITIONAL ALGORITHMS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Algorithms
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Image processing
  topic: keywords
subject: Medical image segmentation
  topic: keywords
creator: ShouTong Huang
rights: Copyright (c) 2025 ShouTong Huang https://creativecommons.org/licenses/by/4.0
date: 2025-06-25
publication date: 2025-06-25
starting page: 7
ending page: 12
doi: 10.61784/adsj3019

CAUSAL INFERENCE-BASED DIGITAL PAYMENT FRAUD DETECTION: FROM FINANCIAL SECURITY TO ECONOMY-WIDE RESILIENCE
type: item
description: 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.
subject: Research Article
  topic: section
subject: Causal inference
  topic: keywords
subject: Digital payment
  topic: keywords
subject: Fraud detection
  topic: keywords
subject: Financial security
  topic: keywords
subject: Risk prevention
  topic: keywords
creator: LuQing Ren
rights: Copyright (c) 2025 LuQing Ren https://creativecommons.org/licenses/by/4.0
date: 2025-07-17
publication date: 2025-07-17
starting page: 13
ending page: 19
doi: 10.61784/adsj3020

HIERARCHICAL DEEP REINFORCEMENT LEARNING FRAMEWORK FOR ADAPTIVE CPU SCHEDULING IN HYBRID TRANSACTIONAL-ANALYTICAL DATABASES
type: item
description: 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.
subject: Research Article
  topic: section
subject: Hierarchical reinforcement learning
  topic: keywords
subject: CPU scheduling
  topic: keywords
subject: HTAP satabases
  topic: keywords
subject: Deep Q-Networks
  topic: keywords
subject: Adaptive resource management
  topic: keywords
subject: OLTP-OLAP optimization
  topic: keywords
subject: Database performance
  topic: keywords
subject: Workload balancing
  topic: keywords
creator: Nur Aisyah
creator: Mehdi Benali
rights: Copyright (c) 2025 Nur Aisyah; Mehdi Benali https://creativecommons.org/licenses/by/4.0
date: 2025-08-14
publication date: 2025-08-14
starting page: 20
ending page: 27
doi: 10.61784/adsj3021

DECENTRALIZED TRAFFIC REGULATION IN ADVERTISING NETWORKS USING ENERGY-AWARE HIERARCHICAL DEEP REINFORCEMENT LEARNING
type: item
description: 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.
subject: Research Article
  topic: section
subject: Decentralized traffic regulation
  topic: keywords
subject: Advertising networks
  topic: keywords
subject: Energy-aware computing
  topic: keywords
subject: Hierarchical deep reinforcement learning
  topic: keywords
subject: Deep Q-Networks
  topic: keywords
subject: Network optimization
  topic: keywords
subject: Quality of service
  topic: keywords
subject: Energy efficiency
  topic: keywords
creator: JingYi Cao
rights: Copyright (c) 2025 JingYi Cao https://creativecommons.org/licenses/by/4.0
date: 2025-08-14
publication date: 2025-08-14
starting page: 28
ending page: 34
doi: 10.61784/adsj3022

INTERPRETABLE TRANSFORMER MODELS FOR RELATIONSHIP ANALYSIS IN FINANCIAL DATA
type: item
description: 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.
subject: Research Article
  topic: section
subject: Interpretable machine learning
  topic: keywords
subject: Transformer architecture
  topic: keywords
subject: Financial relationship analysis
  topic: keywords
subject: Attention mechanisms
  topic: keywords
subject: Fraud detection
  topic: keywords
subject: Heterogeneous networks
  topic: keywords
creator: Laura Chen
creator: Robert Murphy
rights: Copyright (c) 2025 Laura Chen; Robert Murphy https://creativecommons.org/licenses/by/4.0
date: 2025-09-20
publication date: 2025-09-20
starting page: 35
ending page: 41
doi: 10.61784/adsj3023

A TRANSFER LEARNING FRAMEWORK FOR CLINICAL TEXT CLASSIFICATION USING PRETRAINED LANGUAGE MODELS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Clinical text classification
  topic: keywords
subject: Transfer learning
  topic: keywords
subject: Pretrained language models
  topic: keywords
subject: BioBERT
  topic: keywords
subject: ClinicalBERT
  topic: keywords
subject: Natural language processing
  topic: keywords
subject: Electronic health records
  topic: keywords
creator: MeiLin Cheng
rights: Copyright (c) 2025 MeiLin Cheng https://creativecommons.org/licenses/by/4.0
date: 2025-06-13
publication date: 2025-06-13
starting page: 1
ending page: 6
doi: 10.61784/adsj3018

PREDICTION OF AIR QUALITY INDEX BASED ON ARIMA AND LSTM MODELS—TAKE GUILIN AS AN EXAMPLE
type: item
description: With the acceleration of urbanization and the improvement of industrialization, air quality has become an important factor affecting the quality of life and health of urban residents. Under this background, the air quality problem has become increasingly prominent and has become a key factor affecting the quality of life and health of urban residents. Therefore, how to accurately predict the air quality index (AQI) in order to take timely and effective measures to protect the health of residents has become a major challenge we face. This study takes Guilin city as the object, and constructs an air quality prediction model by integrating big data and artificial intelligence technology. Based on the historical monitoring data such as PM2.5 and NO2, a training set/test set division method is adopted, and the ARIMA time series model and the LSTM depth learning network are comprehensively applied: the former captures the trend and seasonal characteristics of the data, and the latter handles the complex sequence relationship. By introducing the rolling prediction mechanism to update the model parameters in real time, the dynamic adaptability of the prediction system is effectively improved. Experiments show that the hybrid model significantly improves the prediction accuracy and stability of AQI, and provides a scalable technical scheme for urban air quality management.
subject: Research Article
  topic: section
subject: LSTM model
  topic: keywords
subject: ARIMA model
  topic: keywords
subject: Rolling forecast
  topic: keywords
subject: Air quality index forecast
  topic: keywords
creator: Dan Chen
rights: Copyright (c) 2025 Dan Chen https://creativecommons.org/licenses/by/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 106
ending page: 115
doi: 10.61784/adsj3017

FROM "THE LIGHT OF THE COUNTRY" TO "THE PHENOMENAL EXPLOSION" —— THE AUDIENCE RELATIONSHIP AND THE WAY TO "BREAK THE CIRCLE" IN DECODING "NEZHA 2"
type: item
description: "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 t…
subject: Research Article
  topic: section
subject: Population from four regions in Guangxi
  topic: keywords
subject: “Ne Zha 2”
  topic: keywords
subject: “Breakthrough” strategies
  topic: keywords
subject: Differential analysis
  topic: keywords
subject: Multiple response
  topic: keywords
subject: Structural equation model.
  topic: keywords
creator: Baoshuo Wang
rights: Copyright (c) 2025 Baoshuo Wang https://creativecommons.org/licenses/by/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 76
ending page: 105
doi: 10.61784/adsj3016

5G ABNORMAL SIGNALING DETECTION BASED ON AUTO-ENCODER
type: item
description: 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.
subject: Research Article
  topic: section
subject: 5G security
  topic: keywords
subject: Signaling detection
  topic: keywords
subject: N4 interface
  topic: keywords
subject: PFCP
  topic: keywords
creator: YaDi Fu
rights: Copyright (c) 2025 YaDi Fu https://creativecommons.org/licenses/by/4.0
date: 2025-02-26
publication date: 2025-02-26
starting page: 18
ending page: 22
doi: 10.61784/adsj3009

AUTOMATED CYBERSECURITY INCIDENT RESPONSE: A REINFORCEMENT LEARNING APPROACH
type: item
description: 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.
subject: Research Article
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Incident response
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Automation
  topic: keywords
subject: Cyber threats
  topic: keywords
subject: Machine learning
  topic: keywords
subject: AI
  topic: keywords
creator: MingJie Zhao
creator: Rui Chen
rights: Copyright (c) 2025 MingJie Zhao; Rui Chen https://creativecommons.org/licenses/by/4.0
date: 2025-03-13
publication date: 2025-03-13
starting page: 23
ending page: 28
doi: 10.61784/adsj3010

LEGAL ATTRIBUTES OF COMMERCIAL DATA AND APPROACHES TO INTELLECTUAL PROPERTY PROTECTION
type: item
description: 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.
subject: Research Article
  topic: section
subject: Commercial data
  topic: keywords
subject: Legal attributes
  topic: keywords
subject: Intellectual property protection
  topic: keywords
subject: Fair use
  topic: keywords
subject: Technical governance
  topic: keywords
creator: Zhaoxia Deng
rights: Copyright (c) 2025 Zhaoxia Deng https://creativecommons.org/licenses/by/4.0
date: 2025-04-23
publication date: 2025-04-23
starting page: 29
ending page: 34
doi: 10.61784/adsj3012

DATA-DRIVEN DECISION-MAKING SYSTEM FOR INJECTION MOLDING PRODUCTION AND MAINTENANCE
type: item
description: 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.
subject: Research Article
  topic: section
subject: Injection molding production
  topic: keywords
subject: Edge computing
  topic: keywords
subject: Equipment health
  topic: keywords
subject: Maintenance decision-making
  topic: keywords
creator: DuAng Chen
creator: XinJie Zhou
creator: Fan Xin
creator: ShiWei Xu
rights: Copyright (c) 2025 DuAng Chen; XinJie Zhou; Fan Xin; ShiWei Xu https://creativecommons.org/licenses/by/4.0
date: 2025-04-25
publication date: 2025-04-25
starting page: 35
ending page: 39
doi: 10.61784/adsj3013

EEG-BASED EMOTION RECOGNITION USING ENSEMBLE LEARNING MODEL
type: item
description: 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.
subject: Research Article
  topic: section
subject: EEG
  topic: keywords
subject: Emotion recognition
  topic: keywords
subject: Ensemble model
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Long-Short Term Memory
  topic: keywords
creator: YunYi Wang
rights: Copyright (c) 2025 YunYi Wang https://creativecommons.org/licenses/by/4.0
date: 2025-05-20
publication date: 2025-05-20
starting page: 40
ending page: 47
doi: 10.61784/adsj3014

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
type: item
description: 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 exper…
subject: Research Article
  topic: section
subject: University students
  topic: keywords
subject: Lexical poverty
  topic: keywords
subject: Differential analysis
  topic: keywords
subject: Multiple response
  topic: keywords
subject: Structural equation
  topic: keywords
creator: Xing Zeng
rights: Copyright (c) 2025 Xing Zeng https://creativecommons.org/licenses/by/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 48
ending page: 75
doi: 10.61784/adsj3015

PUBLIC DEBT MANAGEMENT WITH ADVANCED DATA ANALYTICS
type: item
description: Public debt management is a cornerstone of economic stability, yet traditional methods often struggle to address the complexities of contemporary economic environments characterized by volatility, interconnected markets, and rapid technological advancements. This research explores the transformative role of advanced data analytics in modernizing public debt management practices. By integrating predictive analytics, risk assessment tools, and portfolio optimization techniques, advanced analytics offers innovative solutions to enhance forecasting accuracy, manage risks proactively, and optimize debt portfolios. Through an analysis of case studies from both developing and developed economies, the study highlights the global applicability and scalability of these tools. Key findings reveal that advanced analytics significantly improve fiscal resilience, reduces borrowing costs, and fosters long-term sustainability. However, challenges such as the need for robust data infrastructure, skilled personnel, and high initial investments remain barriers to widespread adoption. This paper underscores the critical need for continued research, innovation, and collaboration to fully leverage the potential of data analytics in public debt management, ensuring sustainable economic growth in an increasingly dynamic global landscape.
subject: Research Article
  topic: section
subject: Public debt management
  topic: keywords
subject: Advanced data analytics
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Risk assessment
  topic: keywords
subject: Portfolio optimization
  topic: keywords
subject: Debt sustainability
  topic: keywords
subject: Economic stability
  topic: keywords
subject: Fiscal resilience
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Big data analytics
  topic: keywords
creator: Rakibul Hasan Chowdhury
rights: Copyright (c) 2024 Rakibul Hasan Chowdhury https://creativecommons.org/licenses/by/4.0
date: 2024-12-24
publication date: 2024-12-24
starting page: 6
ending page: 19
doi: 10.61784/adsj3001

A COMPREHENSIVE FRAMEWORK FOR MODERN DATA CLEANING: INTEGRATING STATISTICAL AND MACHINE LEARNING APPROACHES WITH PERFORMANCE ANALYSIS
type: item
description: Data cleansing is an important prerequisite for reliable data evaluation and system-gaining knowledge of programs, directly impacting the exceptional insights and model overall performance. This paper provides a comprehensive examination of modern information-cleaning methodologies, focusing on their sensible packages and effectiveness across diverse datasets. We analyze six primary categories of information cleansing techniques: missing statistics management, outlier detection, information standardization, reproduction removal, consistency validation, and data type transformations. Our systematic assessment exhibits that automated information cleansing pipelines, at the same time efficient, require careful configuration primarily based on area context. Key findings imply that hybrid procedures—combining statistical techniques with area-precise policies—attain advanced consequences compared to standalone strategies, showing a 23% improvement in statistically satisfactory metrics. We also perceive that early-stage records validation significantly reduces downstream processing mistakes by 45%. The implications of this research suggest that companies ought to implement iterative facts-cleaning workflows, incorporating continuous validation and area expert remarks. Furthermore, our findings emphasize the significance of documenting cleansing decisions to ensure reproducibility and keep information lineage. This work offers a structured framework for practitioners to select and implement suitable facts-cleaning techniques based totally on their particular use cases and facts characteristics.
subject: Research Article
  topic: section
subject: Data cleaning
  topic: keywords
subject: Data quality
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Statistical analysis
  topic: keywords
subject: Hybrid methods
  topic: keywords
creator: Nagwa Elmobark
rights: Copyright (c) 2024 Nagwa Elmobark https://creativecommons.org/licenses/by/4.0
date: 2024-12-24
publication date: 2024-12-24
starting page: 20
ending page: 28
doi: 10.61784/adsj3003

A TEMPORAL LOGICAL ATTENTION NETWORK APPROACH TO ANOMALY DETECTION IN DISTRIBUTED SYSTEMS LOGS
type: item
description: This paper presents the Temporal Logical Attention Network, a novel approach to anomaly detection in distributed systems logs. As distributed systems become increasingly integral to modern applications, the complexity and volume of log data generated pose significant challenges for effective monitoring and analysis. Traditional methods for anomaly detection, such as rule-based and statistical techniques, often fall short in addressing the dynamic nature of log data, resulting in high false positive rates and inadequate detection of subtle anomalies. TLAN leverages deep learning, specifically attention mechanisms, to capture temporal dependencies and logical relationships within log data. By embedding log entries into a dense vector space and applying temporal encoding, TLAN identifies significant patterns over time, enhancing the accuracy of anomaly detection. The model focuses on relevant log entries, allowing it to prioritize critical information while minimizing the influence of less significant data. Through rigorous experimentation on multiple datasets, TLAN demonstrated superior performance compared to traditional and state-of-the-art models, achieving high precision, recall, and F1-scores. The findings underscore TLAN's effectiveness in identifying anomalies that may indicate underlying issues, such as security breaches or system failures. This research contributes to the evolving landscape of anomaly detection techniques, highlighting the importance of integrating advanced machine learning approaches in managing distributed systems logs. Ultimately, TLAN represents a significant advancement in the field, offering organizations robust tools for enhancing the security and reliability of their distributed environments.
subject: Research Article
  topic: section
subject: Anomaly detection
  topic: keywords
subject: Distributed systems
  topic: keywords
subject: Temporal logical attention network
  topic: keywords
creator: Maria Gonzalez
creator: Elena Ruiz
creator: Antonio Perez
rights: Copyright (c) 2024 Maria Gonzalez; Elena Ruiz; Antonio Perez https://creativecommons.org/licenses/by/4.0
date: 2024-12-26
publication date: 2024-12-26
starting page: 29
ending page: 36
doi: 10.61784/adsj3004

OPTIMIZED LITHOGRAPHIC HOTSPOT DETECTION WITH MULTI-TASK DEEP LEARNING
type: item
description: The semiconductor manufacturing industry faces increasing challenges due to the growing complexity of integrated circuit designs and the limitations of traditional hotspot detection methods. Lithographic hotspots—areas on chip layouts susceptible to manufacturing defects—pose significant risks to yield and performance. Traditional detection techniques, primarily rule-based and statistical methods, often fail to accurately identify these hotspots, leading to high rates of false positives and negatives. In response to these challenges, this paper proposes a multi-task deep learning framework designed to optimize lithographic hotspot detection. By leveraging the capabilities of convolutional neural networks, the framework simultaneously addresses multiple related tasks, including hotspot detection, design rule violation prediction, and critical area estimation. This multi-task learning approach enhances the model's ability to capture intricate relationships within IC layouts, resulting in improved accuracy and efficiency compared to conventional methods. The proposed framework was trained on a comprehensive dataset, ensuring robust performance across diverse IC designs. Experimental results indicate that the model achieves an impressive accuracy of 92%, significantly outperforming traditional detection systems. Furthermore, the integration of multi-task learning facilitates the sharing of representations across tasks, leading to enhanced generalization and reduced overfitting. The findings underscore the potential of deep learning techniques to revolutionize hotspot detection in semiconductor manufacturing, ultimately contributing to higher yields and better-performing devices. This research not only highlights the advantages of adopting advanced machine learning methodologies but also sets the stage for future explorations into hybrid models that incorporate domain-specific knowledge and advanced architectures.
subject: Research Article
  topic: section
subject: Lithographic hotspot detection
  topic: keywords
subject: Multi-Task learning
  topic: keywords
subject: Deep learning
  topic: keywords
creator: Arjun Mehta
creator: Neha Sharma
creator: Ramesh Kumar
rights: Copyright (c) 2024 Arjun Mehta; Neha Sharma; Ramesh Kumar https://creativecommons.org/licenses/by/4.0
date: 2024-12-26
publication date: 2024-12-26
starting page: 37
ending page: 45
doi: 10.61784/adsj3005

ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF HIGH-FREQUENCY TRADING STRATEGIES: ENHANCING PERFORMANCE AND MANAGING MARKET IMPACT
type: item
description: The rapid advancement of artificial intelligence (AI) has revolutionized high-frequency trading (HFT), offering unprecedented opportunities for strategy optimization and market impact management. This paper explores the transformative role of AI technologies—including machine learning, deep learning, and reinforcement learning—in refining HFT strategies to achieve superior returns and enhanced adaptability in dynamic financial markets. We begin by examining the foundational principles of HFT and its current applications, followed by an in-depth analysis of how AI-driven algorithms can improve trading efficiency, reduce latency, and mitigate risks. Additionally, the study investigates the impact of HFT on market microstructure, developing a comprehensive market impact model that incorporates liquidity dynamics and price volatility. Through empirical analysis, we evaluate the performance of various AI-enhanced trading strategies across diverse market conditions, highlighting their effectiveness as well as potential risks. This research not only advances the understanding of AI's role in HFT strategy optimization but also provides actionable insights for market participants to better navigate and manage the complexities of modern financial markets.
subject: Research Article
  topic: section
subject: High-frequency trading
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Market impact
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Liquidity
  topic: keywords
subject: Algorithmic trading
  topic: keywords
creator: Gang Min
rights: Copyright (c) 2024 Gang Min https://creativecommons.org/licenses/by/4.0
date: 2024-12-29
publication date: 2024-12-29
starting page: 46
ending page: 50
doi: 10.61784/adsj3011

ADAPTIVE PHYSICAL SECURITY: REDEFINING ORGANIZATIONAL SAFETY IN DYNAMIC ENVIRONMENTS
type: item
description: Organizations today face an unprecedented array of security challenges, driven by rapidly changing environments and increasingly sophisticated threats. Traditional static physical security systems, while effective in addressing known risks, are ill-equipped to handle the complexity and unpredictability of modern organizational landscapes. This paper introduces and explores the concept of adaptive physical security, a dynamic approach that leverages advanced technologies such as artificial intelligence (AI), machine learning, real-time data analytics, and Internet of Things (IoT)-enabled devices to create flexible and resilient security frameworks. Adaptive physical security systems are designed to respond to evolving threats in real time, enabling organizations to detect, assess, and mitigate risks before they escalate. Central to this approach are predictive threat modeling, scalable access control mechanisms, and automated incident response strategies, all of which work in tandem to ensure comprehensive protection. Through a review of recent advancements, case studies, and practical applications, this study demonstrates the potential of adaptive security systems to significantly enhance organizational resilience. Key challenges in implementation, such as integration with existing systems, data privacy concerns, and cost implications, are also discussed. Furthermore, the paper highlights the importance of a proactive security culture, emphasizing that technology must be complemented by human vigilance and strategic planning. The findings underscore the need for organizations to shift from reactive to adaptive security paradigms, redefining safety standards to address the demands of today’s dynamic environments. This research provides a foundation for future studies and offers actionable insights for organizations aiming to strengthen their physical security measures.
subject: Research Article
  topic: section
subject: Adaptive physical security
  topic: keywords
subject: Organizational safety
  topic: keywords
subject: Dynamic environments
  topic: keywords
subject: Real-time threat detection
  topic: keywords
creator: Putri Ayu Nur Rizki
creator: Sasha Havisa Samosir
creator: Iskandar Muda
rights: Copyright (c) 2025 Putri Ayu Nur Rizki; Sasha Havisa Samosir; Iskandar Muda https://creativecommons.org/licenses/by/4.0
date: 2025-01-11
publication date: 2025-01-11
starting page: 1
ending page: 7
doi: 10.61784/adsj3006

EXAMINING THE MOTIVATION-PERFORMANCE LINK AMONG CONSTRUCTION EMPLOYEES IN WESTERN CAPE, SOUTH AFRICA
type: item
description: The construction industry, serving as a cornerstone in economic development, necessitates a comprehensive understanding of the determinants influencing the productivity of construction workers. This study delves into the motivating drivers and challenging factors impacting construction workers' productivity in Western Cape, South Africa. The research context underscores the pivotal role of motivation in augmenting worker productivity while acknowledging the adverse effects of challenging elements on job satisfaction and overall project success. Employing a survey research design, the study focused on 200 construction professionals in the Western Cape Province, South Africa. A stratified proportional random sampling technique was employed to ensure a representative sample of 200 participants and only 146 were retrieved. The data collection process encompassed a structured questionnaire, covering demographic characteristics, motivation, and factors influencing productivity. Rigorous analyses, including mean and standard deviation and Pearson Product Moment Correlation, were conducted using the Statistical Package for Social Sciences (SPSS) software, facilitating a systematic examination of the collected data. The noteworthy findings highlight the significance of factors such as recognition by authority, responsibility, provision of healthcare services, working conditions, transportation facilities, promotion opportunities, error tolerance, salary increments, adherence to company policies, and participation in decision-making processes as significant motivators. Conversely, challenges such as lack of cooperation among fellow workers, unfair reward practices, lack of appreciation for job performance, poor supervision, irregular salary payments, inadequate safety measures, lack of respect by supervisors, presence of incompetent crew members, and repetitive tasks were identified. This research highlights the drive relationship between motivation and productivity in the co…
subject: Research Article
  topic: section
subject: Construction employees
  topic: keywords
subject: Motivation
  topic: keywords
subject: Performance
  topic: keywords
subject: Western cape
  topic: keywords
creator: Ndukuba Samuel Nnadoziem
creator: Uwa Chukwunonso Aghaegbulam
rights: Copyright (c) 2025 Ndukuba Samuel Nnadoziem; Uwa Chukwunonso Aghaegbulam https://creativecommons.org/licenses/by/4.0
date: 2025-01-22
publication date: 2025-01-22
starting page: 8
ending page: 17
doi: 10.61784/adsj3007