Multidisciplinary Research in Computing Information Systems
Multidisciplinary Research in Computing Information Systems (MRCIS) is a peer-reviewed academic journal that publishes high-quality research on a wide range of topics within the fields of computing, information systems, and their interdisciplinary applications. The journal aims to provide a platform for the dissemination of innovative research, new methodologies, and advancements in computing technologies and their integration into various sectors. It covers a diverse array of topics including but not limited to: Software engineering Artificial intelligence and machine learning Data science and big data analytics Cloud computing and virtualization Information security and cryptography Human-computer interaction Network and communication systems Internet of Things (IoT) Computational models and simulations Business information systems Healthcare and e-health informatics Smart technologies and systems integration The journal encourages submissions from a variety of disciplines that explore the intersection of computing and information systems, fostering interdisciplinary research that drives technological advancements. It provides a comprehensive resource for researchers, practitioners, and academics interested in cutting-edge developments in the field of computing and its practical applications across different industries.
All articles published in the Multidisciplinary Research in Computing Information Systems are licensed under an open-access model. Authors retain full copyright and grant the journal the right of first publication. The content can be freely accessed, distributed, and reused for non-commercial purposes, provided proper citation is given to the original work.
Deep Learning-Based Optimization Techniques For Large-Scale Data Processing In Cloud Environments
The exponential growth of large-scale datasets in modern industries—ranging from social media and e-commerce to bioinformatics and IoT—has increased the demand for efficient cloud-based data processing platforms. Deep learning-powered optimization techniques offer powerful solutions for handling the computational complexity, latency challenges, and resource allocation demands of distributed cloud systems. This article provides an in-depth exploration of deep learning models used for optimizing data processing tasks, including neural scheduling algorithms, reinforcement learning-based resource allocation, auto-scaling prediction models, and deep neural network–driven workload balancing. Two graphs demonstrate the efficiency improvements and reduction in processing latency offered by deep learning integration. The article concludes with key challenges such as energy consumption, model transparency, and data security, while outlining future research directions involving federated optimization, edge–cloud collaboration, and quantum-assisted deep learning.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Digital Twin Technologies For Smart Manufacturing Systems
Digital Twin (DT) technology has emerged as a cornerstone of Industry 4.0, enabling real-time mapping of physical manufacturing systems into intelligent virtual representations. Through continuous data integration, simulation, and predictive analytics, digital twins improve system efficiency, reduce downtime, and enhance product quality. This article provides a comprehensive examination of DT frameworks, architectures, and applications within smart manufacturing environments. It explores key components—including IoT sensors, data pipelines, AI-driven predictive models, and cyber–physical integration. Two graphs illustrate the rising adoption of DT technologies and the productivity improvements achieved in manufacturing operations. The study concludes with challenges related to interoperability, cybersecurity risks, data standardization, and future opportunities, including autonomous factories and AI-enhanced DT ecosystems.
Neural Architecture Search For Efficient Model Deployment On Mobile Devices
Neural Architecture Search (NAS) has transformed the automation of deep learning model design, enabling the discovery of optimized architectures tailored for specific hardware constraints. With the rapid growth of mobile and edge-AI applications, deploying efficient deep learning models on resource-constrained devices has become a pressing challenge. NAS provides a systematic approach to generating lightweight, low-latency, and energy-efficient neural networks suitable for smartphones, IoT devices, drones, and wearables. This article examines modern NAS techniques—such as evolutionary search, reinforcement-learning-based search, differentiable NAS (DARTS), and hardware-aware NAS—alongside the challenges of memory limitations, power consumption, and inference speed on mobile devices. Two graphs illustrate latency improvements and accuracy–efficiency trade-offs across NAS-generated models. The article concludes with future research directions, including adaptive NAS, federated NAS, and device-specific optimization frameworks.
AI-Based Anomaly Detection In Financial Fraud Prevention Systems
Financial fraud continues to evolve with increasing digital transactions, global banking connectivity, and sophisticated cyber-attacks. Traditional rule-based systems are no longer adequate for detecting complex and emerging fraud patterns. Artificial Intelligence (AI)–driven anomaly detection models offer advanced capabilities for analyzing transactional behavior, identifying suspicious activities, and enhancing real-time decision-making. This article presents a comprehensive examination of AI-based anomaly detection techniques—including machine learning, deep learning, graph-based models, time-series analysis, and hybrid fraud detection frameworks. Two graphs illustrate the performance comparison of AI algorithms and the rising adoption of AI fraud detection across the financial sector. The article concludes with future challenges related to data imbalance, explainability, privacy, and adversarial attacks, while identifying future opportunities in federated learning, quantum-safe analytics, and real-time adaptive systems.
The Future Of Quantum Cryptography In Secure Data Transmission
Quantum cryptography promises a revolutionary transformation in secure data transmission by leveraging fundamental principles of quantum mechanics such as superposition, entanglement, and no-cloning. As classical encryption methods face increasing vulnerabilities from powerful adversaries and emerging quantum computers, Quantum Key Distribution (QKD) and quantum-resistant algorithms offer unprecedented security advantages. This article explores the future landscape of quantum cryptography, analyzing QKD protocols, post-quantum cryptographic models, hardware requirements, communication architectures, and the evolving threat landscape. Two graphs illustrate the growth of global QKD deployment and the performance comparison of classical vs. quantum-resistant encryption schemes. Key challenges—including scalability, interoperability, cost, and standardization—are discussed alongside future opportunities in hybrid quantum-classical secure networks. The article concludes with a forward-looking assessment of how quantum cryptography will reshape secure communication in the coming decade.
Efficient Implementation of Improved Dragonfly Algorithm in Multi-Target Feature Selection
High-dimensional data often contain many features that do not help the model and make training slower. This study uses a multi-objective dragonfly method to choose smaller feature sets while keeping good accuracy. The method applies crowding distance and a changing inertia value to guide the search. Tests on 15 datasets show that the method removes about 58% of the features and increases accuracy by 3.1% over the basic dragonfly version. The Pareto fronts cover a wider range of choices and become stable in fewer generations. These results show that simple changes in the search steps can improve both feature use and accuracy. The method is useful when small models are needed, but future work should test more classifiers, larger datasets, and more objectives.
A comparative study of the effects of different types of dietary fiber on gut microbiota metabolites and insulin sensitivity in diabetic patients
This study examined how soluble and insoluble fibers affect blood sugar control, gut bacteria, and intestinal health in mice with type 2 diabetes. Thirty C57BL/6J mice were divided into three groups: diabetic control, inulin-fed, and wheat-bran-fed. After 12 weeks, fasting glucose dropped by 31% and HOMA-IR by 45% in the inulin group, while the wheat bran group showed smaller drops of 18% and 20%. Analysis of metabolites showed that inulin raised butyrate and lactate levels, while wheat bran increased propionate and valerate. The inulin group also had about 60% higher Occludin protein levels (P < 0.01), showing better gut barrier function. These results show that soluble fiber improves insulin response by supporting short-chain fatty acid formation and maintaining gut integrity, while insoluble fiber works mainly through slower fermentation. The study shows that fiber type plays an important role in managing blood sugar, offering evidence for choosing the right kind of fiber to support diabetes care.
Electronic Structure Control of Oxidizing Capacity in Birnessite Minerals
Birnessite, a layered manganese oxide, shows strong oxidation ability, but the main factor that controls its reactivity remains unclear. In this study, ten natural and synthetic birnessite samples with different interlayer cations and Mn(III)/Mn(IV) ratios were used to study the effect of electron affinity on oxidation behavior. The electron affinity measured by ultraviolet photoelectron spectroscopy ranged from 5.55 to 6.10 eV. Oxidation of Fe(II) and As(III) followed a pseudo-second-order model, with rate constants increasing from "1.1×1" "0" ^"-3" "to5.6×1" "0" ^"-3" g mg⁻¹ min⁻¹ for Fe(II) and from "6.8×1" "0" ^"-4" "to3.3×1" "0" ^"-3" g mg⁻¹ min⁻¹ for As(III). A clear positive relation was found between electron affinity and oxidation rate, showing that a higher electron affinity gives stronger oxidation ability. Surface analysis showed that Mn(IV) was partly reduced to Mn(III) and Mn(II), and hydroxyl groups formed after the reaction, which caused surface passivation and lower activity over time. Theoretical calculations supported these results and linked the conduction band position with electron affinity. The study proves that electron affinity directly affects oxidation ability and provides a useful guide for developing manganese oxide catalysts for pollutant control and environmental use.
Joint Optical–Thermal Optimization of Laser Processing Parameters Using Bayesian Optimization and Neural Emulators
The precise control of laser material processing, particularly in regimes such as laser welding, cutting, and additive manufacturing, necessitates a rigorous understanding of the complex interplay between optical beam propagation and thermal material response. Traditional methods for parameter optimization, which rely heavily on trial-and-error experimentation or computationally expensive Finite Element Method simulations, often fail to converge efficiently within the high-dimensional parameter space inherent to modern laser systems. This paper proposes a novel framework for the Joint Optical–Thermal Optimization of processing parameters by leveraging a hybrid architecture comprising Bayesian Optimization and deep Neural Emulators. We introduce a data-driven methodology where a high-fidelity physics-based model, coupling ray-tracing optical solvers with transient heat transfer equations, generates a training corpus. This data trains a deep neural network surrogate—the Neural Emulator—capable of predicting melt pool geometries and thermal gradients with near-instantaneous inference times. Subsequently, a Bayesian Optimization engine utilizes this emulator to navigate the parameter space, balancing exploration and exploitation to identify optimal process windows. Our results demonstrate that this approach reduces the computational cost of optimization by three orders of magnitude compared to direct numerical simulation while maintaining predictive accuracy within two percent of experimental baselines. This work bridges the gap between optical setup and thermal history, offering a scalable pathway for autonomous laser process manufacturing.
Self-Supervised Learning for Raman Spectra Denoising and Peak Deconvolution Under Low SNR
Raman spectroscopy is a pivotal analytical technique in chemical physics, molecular biology, and material science, offering a non-destructive fingerprinting capability for molecular identification. However, the practical utility of Raman scattering is frequently impeded by its inherently weak signal intensity, which results in a low Signal-to-Noise Ratio (SNR) when acquisition times are limited or when samples are sensitive to photo-degradation. Traditional computational methods for spectral restoration, such as Savitzky-Golay filtering or wavelet transforms, often necessitate manual parameter tuning and risk distorting peak fidelity, particularly in the preservation of Full Width at Half Maximum (FWHM) values essential for deconvolution. Furthermore, while supervised Deep Learning (DL) models have shown promise, they suffer from a reliance on paired clean-noisy datasets, which are experimentally prohibitive to obtain for complex biological mixtures. This paper presents a novel Self-Supervised Learning (SSL) framework, the Masked Spectral Reconstruction Network (MSR-Net), designed to denoise Raman spectra and facilitate peak deconvolution without requiring ground-truth clean references. By leveraging a masked autoencoding pretext task adapted for 1D correlated signals, the model learns the underlying morphological semantics of Lorentzian and Gaussian peaks while treating stochastic noise as non-reconstructible high-frequency artifacts. We evaluate the approach on both synthetic datasets and real-world mineralogical spectra from the RRUFF database. Experimental results demonstrate that MSR-Net achieves superior SNR improvement and peak position accuracy compared to classical baselines and supervised counterparts trained on limited data.
Real-Time Arrhythmia Detection on Wearables Using Quantized Transformers and Noise-Robust Training
The proliferation of wearable health monitoring devices has created an unprecedented opportunity for continuous, non-invasive cardiac surveillance. However, deploying sophisticated deep learning models for arrhythmia detection on resource-constrained edge devices remains a significant challenge due to limited computational power, memory restrictions, and the high susceptibility of ambulatory signals to motion artifacts. This paper introduces a novel framework for real-time arrhythmia classification that leverages Quantized Transformers combined with a noise-robust training regimen. We propose a lightweight Transformer architecture optimized for time-series physiological data, utilizing Quantization-Aware Training (QAT) to reduce model precision from 32-bit floating-point to 8-bit integers without substantial degradation in predictive performance. Furthermore, we address the pervasive issue of signal contamination by introducing a dynamic noise injection strategy during the training phase, which simulates realistic baseline wander, muscle artifacts, and electrode motion. Experimental results on the MIT-BIH Arrhythmia Database demonstrate that our approach achieves an F1-score of 98.2 percent while reducing memory footprint by a factor of 3.8 and inference latency by 45 percent compared to full-precision counterparts. These findings suggest that quantized attention mechanisms can effectively capture long-range dependencies in electrocardiogram (ECG) signals within the tight power envelopes of modern wearable hardware.
Geometric Deep Learning for Protein Function Prediction: Integrating 3D Structural Inductive Bias with Interaction Graphs
The accurate prediction of protein function from raw amino acid sequences and structural data remains a central challenge in computational biology, essential for advancing drug discovery and understanding cellular mechanisms. While sequence-based methods have historically dominated the field due to data abundance, they often fail to capture the functional implications of distant homology where sequence similarity is low but structural conservation is high. With the advent of highly accurate structure prediction systems, the availability of 3D protein structures has exploded, necessitating novel architectures capable of leveraging this geometric data. This paper introduces GeoProtNet, a comprehensive framework that utilizes Geometric Deep Learning (GDL) to predict protein function, specifically Gene Ontology (GO) terms. We propose a hybrid architecture that processes the protein as a geodesic graph on a Riemannian manifold to capture local chemical environments, while simultaneously integrating global context through a higher-order Protein-Protein Interaction (PPI) graph. By enforcing SE(3)-equivariance within the message-passing mechanism, our model ensures robustness against rotational and translational variations inherent in coordinate data. Experimental results demonstrate that GeoProtNet significantly outperforms state-of-the-art sequence-based and structure-based baselines, particularly in the twilight zone of low sequence identity.
A Multimodal Foundation Model for EHR Time Series and Clinical Notes with Outcome Calibration
The digitization of healthcare has resulted in the proliferation of Electronic Health Records (EHRs), which contain a rich yet heterogeneous mix of data modalities, primarily structured time-series data and unstructured clinical notes. While deep learning has demonstrated remarkable potential in predictive healthcare, existing approaches often process these modalities in isolation or rely on naive fusion mechanisms that fail to capture the complex, asynchronous interplay between physiological measurements and clinical narratives. Furthermore, modern neural networks, particularly large foundation models, suffer from significant miscalibration, frequently yielding overconfident predictions that are detrimental to clinical decision-making. In this paper, we introduce MedCali-FM, a novel multimodal foundation model designed to integrate sparse, irregular EHR time series with clinical text through a calibrated cross-attention mechanism. We propose a joint pre-training objective that combines masked forecasting, masked language modeling, and a novel contrastive alignment loss to learn unified patient representations. Crucially, we integrate a differentiable calibration objective directly into the fine-tuning phase, ensuring that the model's confidence scores align with true empirical probabilities. Our extensive experiments on the MIMIC-IV dataset demonstrate that MedCali-FM not only achieves state-of-the-art performance in mortality prediction and phenotype classification but also significantly reduces Expected Calibration Error (ECE) compared to existing ensemble and deep fusion methods.
Robust Cell Type Annotation in Single-Cell RNA-Seq via Contrastive Domain Adaptation
Single-cell RNA sequencing (scRNA-seq) has revolutionized the understanding of cellular heterogeneity by allowing transcriptional profiling at single-cell resolution. However, the automated annotation of cell types across different datasets remains a formidable challenge due to substantial batch effects—systematic technical variations arising from differences in sequencing protocols, capture platforms, and donor variability. These non-biological variances often confound standard supervised learning algorithms, leading to poor generalization on unseen target data. This paper introduces a novel framework, Contrastive Domain Adaptation for Single-Cell (CDA-SC), designed to robustly transfer knowledge from labeled source atlases to unlabeled target datasets. By integrating domain-adversarial training with supervised contrastive learning, CDA-SC aligns the feature distributions of source and target domains while explicitly enforcing intra-class compactness and inter-class separability in the latent space. We demonstrate that this dual-objective approach effectively mitigates batch effects and prevents the misalignment of biologically distinct but transcriptionally similar cell types. Extensive experiments on multiple cross-protocol benchmarks indicate that CDA-SC outperforms state-of-the-art baselines in classification accuracy and macro F1-scores, providing a scalable and reliable solution for automated cell type identification in large-scale integrative studies.
Domain-Adapted Remote Sensing for Urban Change Detection Using Weak Supervision from Maps
The proliferation of high-resolution Earth observation data has created unprecedented opportunities for monitoring urban dynamics. However, the efficacy of supervised deep learning models for change detection is frequently curtailed by the scarcity of pixel-level ground truth annotations and the statistical heterogeneity inherent in multi-temporal or cross-sensor imagery. This paper presents a novel framework for Domain-Adapted Remote Sensing for Urban Change Detection (DARS-UCD), which leverages weak supervision from openly available cartographic data, specifically OpenStreetMap (OSM). We propose a dual-stream architecture that harmonizes feature representations between a labeled source domain and an unlabeled target domain, where the target domain supervision is derived solely from noisy, outdated, or incomplete map rasters. To mitigate the domain shift and label noise, we introduce a Map-Guided Uncertainty Weighting (MGUW) mechanism coupled with an adversarial domain adaptation module. Extensive experiments demonstrate that our approach significantly outperforms standard unsupervised methods and achieves competitive performance relative to fully supervised baselines. The results validate the utility of integrating semantic map priors into remote sensing pipelines, offering a scalable solution for global-scale urban monitoring.
Robust Ground Settlement Forecasting with Spatiotemporal Transformers and Geotechnical Priors
The rapid expansion of urban underground infrastructure, particularly metro systems and utility tunnels, necessitates precise monitoring and forecasting of ground settlement to mitigate risks to existing surface structures. Traditional empirical methods and finite element analyses often struggle to balance computational efficiency with the complex, non-linear dynamics of soil-structure interactions in heterogeneous geological environments. While deep learning has emerged as a viable alternative, purely data-driven models frequently violate physical laws and generalize poorly in data-sparse regimes. This paper presents a novel framework, the Geotechnical Spatiotemporal Transformer (Geo-STT), which integrates geotechnical priors directly into the attention mechanism of a transformer architecture. By embedding static soil parameters—specifically Atterberg limits, void ratios, and shear strength—alongside dynamic time-series data from sensor arrays, the model learns a physics-aware representation of ground deformation. We introduce a novel governing equation-based loss function that penalizes predictions diverging from established settlement trough profiles. Extensive experiments on real-world shield tunneling datasets demonstrate that Geo-STT significantly outperforms state-of-the-art baselines in long-term forecasting accuracy and robustness against sensor noise.
Climate and ESG Signal Extraction for Asset Pricing Using Large-Scale Text and Graph Models
The integration of Environmental, Social, and Governance (ESG) criteria into quantitative asset pricing has transitioned from a niche investment style to a central pillar of modern financial engineering. However, the extraction of reliable ESG signals remains plagued by the unstructured nature of corporate disclosures, the prevalence of greenwashing, and the complex, non-linear dependencies inherent in global supply chains. This paper presents a novel, dual-stream neural architecture that synergizes large-scale Natural Language Processing (NLP) with Graph Neural Networks (GNNs) to distill robust pricing signals. We propose a methodology that first employs a domain-specific transformer model to extract semantic sentiment and latent risk embeddings from diverse textual corpora, including regulatory filings and news media. Concurrently, we construct a dynamic industry-relation graph that propagates these idiosyncratic signals across supply chain and ownership linkages using a graph attention mechanism. This approach addresses the limitation of independent and identically distributed (i.i.d.) assumptions in traditional factor models by explicitly modeling the spillover effects of climate risks. Our empirical analysis, conducted on a universe of global equities over a ten-year period, demonstrates that the proposed Text-Graph fusion model significantly outperforms both traditional linear factor models and standalone deep learning baselines. The resulting alpha signals exhibit low correlation with standard risk factors, offering diversification benefits and enhanced Sharpe ratios for ESG-integrated portfolios.
Multimodal News–Price Fusion for Event-Driven Return Prediction with Causal Debiasing
The prediction of financial asset returns has long been a central challenge in computational finance, characterized by non-stationarity, high volatility, and a low signal-to-noise ratio. While recent advancements in deep learning have enabled the fusion of quantitative market data with qualitative textual streams—such as news articles and earnings reports—most existing multimodal architectures rely on associative correlations rather than causal mechanisms. This reliance renders models susceptible to spurious correlations driven by confounders, such as global market sentiment or macroeconomic shocks, which simultaneously influence both news content and asset prices. This paper introduces the Multimodal News–Price Fusion with Causal Debiasing (MNPF-CD) framework, a novel architecture that integrates structural causal models into deep neural networks to mitigate confounding bias. We propose a backdoor adjustment mechanism implemented via a variational intervention layer, allowing the model to learn invariant representations of asset dynamics. By mathematically separating the causal effect of idiosyncratic news events from systemic market noise, MNPF-CD achieves superior generalization capabilities. Our extensive experimental evaluation on S&P 500 constituents demonstrates that the proposed method significantly outperforms state-of-the-art baselines in terms of Information Coefficient (IC) and Sharpe Ratio, particularly during periods of market regime shifts.
Diffusion-Based Data Augmentation for Imbalanced Multivariate Time Series Classification
The proliferation of sensor networks and Internet of Things (IoT) devices has led to an exponential increase in the availability of multivariate time series (MTS) data. However, in critical domains such as healthcare monitoring, industrial fault detection, and financial anomaly prediction, datasets are intrinsically imbalanced; the events of interest are rare compared to normal operational states. This class imbalance severely degrades the performance of deep learning classifiers, which tend to bias towards the majority class. Traditional oversampling techniques, such as SMOTE, often fail to capture the complex temporal dependencies and inter-variable correlations inherent in MTS data. While Generative Adversarial Networks (GANs) have been proposed as a solution, they suffer from training instability and mode collapse. This paper presents a novel framework for diffusion-based data augmentation specifically tailored for imbalanced multivariate time series classification. We leverage a Conditional Denoising Diffusion Probabilistic Model (CDDPM) effectively conditioned on class labels to generate high-fidelity synthetic samples of the minority class. By modeling the data distribution through a gradual denoising process, our approach preserves the intricate temporal dynamics and cross-channel correlations better than adversarial counterparts. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly improves classification performance, particularly in terms of F1-score and Geometric Mean, compared to state-of-the-art augmentation techniques.
Denoising Diffusion Probabilistic Models
Bayesian Deep Ensembles for Reliable Remaining Useful Life Estimation Under Domain Shift
Predictive Maintenance (PdM) has emerged as a cornerstone of Industry 4.0, leveraging sensor data to forecast the Remaining Useful Life (RUL) of critical machinery. While deep learning models have achieved state-of-the-art accuracy in RUL estimation, they predominantly function as deterministic point estimators. These models often fail to generalize effectively when subjected to domain shift—changes in operating conditions or fault modes that deviate from the training distribution. Furthermore, standard deep learning approaches typically exhibit overconfidence in their predictions on out-of-distribution data, posing significant safety risks in high-stakes industrial environments. This paper proposes a Bayesian Deep Ensemble (BDE) framework designed to enhance reliability and quantify uncertainty in RUL estimation under domain shift. By aggregating predictions from multiple probabilistic neural networks, the proposed method captures both aleatoric uncertainty (inherent data noise) and epistemic uncertainty (model ignorance). We demonstrate that BDEs not only improve predictive accuracy on the NASA C-MAPSS dataset under transfer learning scenarios but also provide calibrated uncertainty estimates that can serve as reliable indicators for manual intervention. The results suggest that ensemble-based Bayesian methods offer a robust alternative to single-model architectures for safety-critical PdM applications.
Semi-Supervised Change-Point Detection via Consistency Training on Augmented Temporal Views
The detection of abrupt changes in the generative parameters of time series data, known as Change-Point Detection (CPD), is a fundamental challenge in signal processing, data mining, and machine learning. While supervised deep learning methods have achieved remarkable success in this domain, they rely heavily on large-scale, accurately annotated datasets. In many real-world applications, such as industrial anomaly detection and physiological signal monitoring, obtaining frame-level labels is prohibitively expensive and requires expert domain knowledge. Conversely, unsupervised methods often suffer from high false-positive rates due to their inability to distinguish between noise and varying semantic states. To bridge this gap, this paper proposes a novel Semi-Supervised Change-Point Detection framework based on Consistency Training. We introduce a dual-branch architecture that enforces predictive consistency between original temporal sequences and their stochastically augmented views. By leveraging a rigorous set of temporal augmentations—including magnitude scaling, permutation, and time-warping—we enable the model to learn robust feature representations from unlabeled data while refining decision boundaries using a limited set of labeled examples. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach significantly outperforms state-of-the-art unsupervised methods and achieves competitive performance against fully supervised baselines using only 10% of the labeled data.
A Dual-Domain Denoising Network for Nonstationary Signals with Learned Wavelet Bases
The restoration of nonstationary signals contaminated by complex noise distributions remains a fundamental challenge in signal processing, particularly within biomedical engineering, seismic analysis, and structural health monitoring. Traditional denoising methodologies, which predominantly rely on fixed basis transformations or purely time-domain filtering, often fail to preserve high-frequency transient features while effectively suppressing noise. This paper introduces the Dual-Domain Denoising Network (D3N), a novel deep learning architecture that integrates a learnable wavelet transform with a dual-path attention mechanism. Unlike standard Convolutional Neural Networks (CNNs) that operate solely in the spatial or temporal domain, the D3N explicitly decomposes signals into time-frequency representations using trainable lifting schemes, allowing the network to adapt the basis functions to the specific spectral characteristics of the input data. We propose a parallel architecture that processes global temporal dependencies via a recurrent attention branch and local spectral features via a sparse wavelet coding branch. Extensive experiments on synthetic and real-world datasets, including ECG and seismic time-series, demonstrate that the proposed method significantly outperforms state-of-the-art baselines in terms of Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE).
Active Learning for Rare Event Detection with Cost-Sensitive Acquisition on Streaming Logs
The exponential growth of modern distributed systems has led to a corresponding explosion in system logs, which serve as the primary source of information for system observability and failure diagnosis. However, detecting rare events—such as critical security breaches or catastrophic system failures—within these massive, high-velocity data streams presents a formidable challenge. Traditional supervised learning approaches fail due to the scarcity of labeled anomalies, while unsupervised methods often suffer from high false-positive rates that induce alert fatigue among operators. Active Learning (AL) offers a promising avenue by querying human experts to label only the most informative instances; yet, existing AL frameworks typically assume a uniform cost of annotation and often neglect the temporal drift inherent in streaming data. This paper proposes a novel framework: Cost-Sensitive Streaming Active Learning (CS-StreamAL). Our approach integrates a dynamic budget management system with a hybrid query strategy that balances uncertainty, diversity, and annotation cost. We introduce a utility function that weighs the information gain of a query against the operational cost of interrupting a human analyst. Extensive experiments on large-scale public log datasets (HDFS, BGL, and Thunderbird) demonstrate that CS-StreamAL achieves superior detection performance for rare events while reducing the labeling budget by approximately 40% compared to state-of-the-art uncertainty sampling methods.
Compositional Reasoning over Structured and Unstructured Data Using Hybrid Indexing Frameworks
The exponential growth of heterogeneous data sources has created unprecedented challenges for information retrieval and knowledge extraction systems. Modern enterprises and research institutions routinely manage vast repositories containing both structured databases and unstructured text collections, yet traditional indexing approaches remain siloed in their treatment of these distinct data modalities. This research investigates compositional reasoning mechanisms that enable unified query processing across structured and unstructured data through hybrid indexing frameworks. We propose a novel architecture that integrates semantic embeddings with relational schema representations, employing gating mechanisms to dynamically balance contributions from both modalities. Our methodology combines graph-based knowledge structures with dense vector retrieval systems, implementing attention mechanisms and modular reasoning components that enable flexible query decomposition and execution. Through extensive experiments on enterprise datasets containing financial records, technical documentation, and operational logs, we demonstrate that hybrid indexing frameworks achieve superior performance in multi-hop reasoning tasks compared to single-modality approaches. The proposed system reduces query response time by 34% while improving answer accuracy by 28% on compositional queries requiring integration across database tables and document collections. These findings suggest that unified indexing strategies with compositional reasoning represent a critical enabler for next-generation question answering systems, business intelligence platforms, and knowledge management applications operating in complex data environments.
Adaptive Workflow Scheduling in Heterogeneous GPU Clusters via Deep Reinforcement Learning
The proliferation of heterogeneous Graphics Processing Unit (GPU) clusters has introduced unprecedented computational capabilities for workflow execution across diverse scientific and industrial domains. However, the inherent heterogeneity of GPU resources, coupled with dynamic workload characteristics and complex workflow dependencies, presents substantial challenges for efficient scheduling. Traditional heuristic-based scheduling algorithms such as Heterogeneous Earliest Finish Time (HEFT) and First-In-First-Out with Duplication and Earliest Finish Time (FIFO-DEFT) often fail to adapt to rapidly changing cluster states and evolving workload patterns. This paper proposes an adaptive workflow scheduling framework leveraging Deep Reinforcement Learning (DRL) to intelligently allocate workflow tasks to heterogeneous GPU resources. The proposed approach employs a Deep Q-Network (DQN) architecture integrated with prioritized experience replay to learn optimal scheduling policies through continuous interaction with the cluster environment. The framework models workflow scheduling as a Markov Decision Process (MDP) where the agent learns to minimize makespan, maximize resource utilization, and maintain quality-of-service guarantees. Extensive experimental evaluations demonstrate that the DRL-based scheduler achieves significant performance improvements compared to baseline algorithms including HEFT, FIFO-DEFT, and other state-of-the-art schedulers. The proposed method exhibits superior adaptability to varying cluster configurations and workflow characteristics, maintaining robust performance across diverse execution scenarios while reducing average makespan and improving scheduling length ratio metrics.
Graph QL and AEM: Streamlining Content Delivery Across Platforms
The digital experience platform has been radically reshaped where organizations are moving away with monolithic architecture to composable architecture based on MACH (Microservices, API-first, Cloud-native, Headless). This research paper gives a thorough study on composable digital experience architectures and how the Adobe Experience Manager has evolved in this paradigm shift and how it has been integrated with MACH ecosystems. This research will examine how conventional DXPs are evolving to comply with the needs of increased agility, scalability, and speed of innovation in delivering digital experiences. A combination of architectural analysis, a case study analysis, and future trend analysis is used in the research to determine the best patterns to be used in composable DXP implementation. Results have shown that companies that deploy composable architectures using AEM and MACH principles realize 40-65x faster time to market of new digital experience, 35-55x lower total cost of ownership and 50-70x higher productivity of the development team than those using conventional monolithic designs. The analysis has shown that the development of AEM to become headless and having a configuration to deploy as a cloud-native architecture makes it a strategic element of composable architectures, whereas MACH technologies are the best-suited to deliver best-of-breed experiences. Moreover, the study also determines that the implementation of composable features necessitates novel organizational functions, such as API management, microservice orchestration, and cloud operations proficiency, which are an obstacle and a chance to digital transformation. The paper offers a systematic model of composable digital experience architecture planning, implementation, and optimization aligning the content management capabilities of AEM and the agility benefits of MACH principles. The conclusions provide strategic advice on how enterprise architects, digital leaders, and technology practitioners can succeed in the process of moving away monolithic to composable DXP architectures and ensuring the most value to the business and future preparation.
Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-nd/4.0
Composable Digital Experience Architectures: AEM, MACH, and the Future of DXPs
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
Automating AEM Content Lifecycle Management Using Python and Adobe APIs
The explosive nature of digital content among enterprise organizations has posed unprecedented challenges in the management of content lifecycles in an efficient manner within Adobe Experience Manager (AEM). The current research paper is a detailed study of automation of the AEM content lifecycle management via Python scripting and Adobe APIs, with the focus on patterns of implementation, automation system models, and business results. This paper explores the ways that organizations can use programmatic solutions to automate content operations without sacrificing the standards of governance or quality, through the systematic analysis of API integration strategies, Python automation libraries, and content workflow optimization strategies. The study is based on a multi-methodology that includes the technical implementation analysis, performance benchmarking, and case study analysis to discover the best patterns of automating AEM content. Results indicate that organizations using Python-based automation of Adobe APIs have experienced 55-75% less manual content management effort, 40-60% improved content lifecycle processing and 35-50% content governance compliance. The paper has shown that Python has a large library ecosystem and AEM has full REST APIs which can be used to automate complex content operations such as version control, publishing processes, archiving processes, and content auditing. Moreover, the study notes that the most flexible solution to automating enterprise-scale content operations without compromising security and performance levels is to use custom Python frameworks that connect to the Content Services APIs of AEM. This paper offers an organized approach to designing, developing and optimizing python-based automation systems to support the entire content lifecycle, encompassing creation and archival. The conclusions provide a practical advice to the AEM administrators, content operations specialists, and automated engineers operating in the contents management environments of enterprises to improve the efficiency of operations at the enterprises and minimize the number of manual errors and risk of compliance in the content management settings.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
Multi-Agent Reinforcement Learning for Dynamic Resource Allocation in Real-Time Bidding Platforms
Real-time bidding platforms have revolutionized digital advertising by enabling advertisers to dynamically bid for ad impressions in milliseconds. However, efficient resource allocation in such platforms remains challenging due to the highly competitive and dynamic nature of auction environments, where multiple advertisers simultaneously compete for limited advertising opportunities. This paper proposes a Multi-Agent Reinforcement Learning framework for dynamic resource allocation in RTB platforms, where autonomous agents represent individual advertisers optimizing their bidding strategies while considering budget constraints and market competition. The framework employs a distributed coordination mechanism that balances competition and cooperation among agents, enabling them to learn optimal policies through interaction with the auction environment. We formulate the resource allocation problem as a multi-agent Markov Decision Process and develop a novel coordination algorithm that combines Deep Deterministic Policy Gradient with attention-based communication protocols. Our experimental evaluation demonstrates that the proposed approach achieves superior performance compared to traditional single-agent methods, improving click-through rates by an average of 23.5% while maintaining budget constraints and reducing cost-per-click by 18.7%. The results indicate that multi-agent coordination significantly enhances resource utilization efficiency in dynamic RTB environments, providing a scalable solution for complex advertising platforms.
Hierarchical Multi-Agent Reinforcement Learning for Dynamic Inventory Allocation with Demand Uncertainty
The complexity of modern supply chain networks requires sophisticated approaches to inventory management that can effectively handle demand uncertainty and coordinate decisions across multiple organizational levels. This paper proposes a novel hierarchical multi-agent reinforcement learning framework for dynamic inventory allocation in multi-echelon supply chains facing stochastic demand patterns. The hierarchical architecture decomposes the inventory control problem into strategic and operational decision layers, where high-level agents coordinate allocation policies across distribution networks while low-level agents optimize local replenishment decisions. The framework integrates Centralized Training with Decentralized Execution paradigm, enabling autonomous agents to learn coordinated policies through shared experience while maintaining operational independence during deployment. Experimental results demonstrate that the proposed approach achieves significant reductions in total system costs compared to traditional base-stock policies and single-agent reinforcement learning methods, while effectively mitigating the bullwhip effect in supply chains with high demand variability.