Computer Science and Mathematics

Abstract: Many companies are re-examining their integration architectures as the pace of Digital Transformation in enterprise E-Commerce (B2B and B2C) accelerates. New integration architectures will depend on the ecosystem of services offered by partners. Integration patterns, including API-led connectivity and Pub-Sub messaging, have become essential in implementing such changes. This paper details a comparative study of two API orchestration paradigms synchronous request-response and asynchronous event-driven using MuleSoft Platform. This paper provides an example of the orchestration and reusability capabilities of the MuleSoft platform. The paper assesses several factors scalability, resilience, resource usage, and operational expenses and the influence of each orchestration model. The paper brings to the fore the performance trade-offs of each of these strategies using production deployment benchmark and industry-reported data: Thread-blocking constraints of synchronous systems. Eventual-consistency issues of an asynchronous messaging system. Trade-offs of point-to-point integration architecture. Thus, we can conclude that asynchronous integration architecture is applicable for the organizations where same data must be sent to multiple different downstream systems. Also, synchronous integration is useful where real time processing is needed for the end user. Research introduces a decision roadmap for integration architects to navigate modernization programs. Based on the business context and significance of the service, hybrid orchestration will create the most scalable and cost-effective results. This research was also conducted on the real production deployed applications using API-LED architecture pattern (experience layer, process layer and system layer) and evaluated 14M+ transactions in production system.

Abstract: The rapid growth of high-resolution 3D voxel datasets derived from LiDAR, BIM, and urban digital twin platforms has created new opportunities for volumetric environmental simulation. However, most existing fire spread models remain surface-based, computationally intensive, or limited in scalability, leaving a gap in efficient voxel-native approaches. This study presents a physics-informed, GPU-accelerated framework for simulating 3D fire propagation in wildland–urban interface (WUI) environments. Fire spread is modeled as a thermally driven process governed by discretized conduction, radiation, and wind-driven convection on a structured voxel grid. Combustion dynamics are determined by fuel-specific parameters and voxel-level physical properties, enabling physically grounded simulation without relying solely on empirical or probabilistic rules. A key contribution is the development of a voxel-native parallel memory layout and stencil-based computational scheme that enables constant-time neighbor access and efficient large-scale updates. The framework is evaluated using a voxelized model of Liverpool, NSW, and tested across both high-performance computing systems and commodity GPUs. Results demonstrate predictable runtime scaling and practical performance for domains exceeding one million active voxels. The proposed approach establishes a scalable foundation for integrating dynamic simulation into urban digital twin and Digital Earth applications.

Abstract: Chaos engineering has emerged as the standard methodology for probing resilience in containerized microservice deployments; however, handcrafting workload profiles and fault scenarios fails to explore the high-dimensional fault space that production systems inhabit. We propose SynthChaos, a constrained generative AI pipeline coupling prompt-engineered large language model (LLM) fault specification with stochastic variational sampling (TimeVAE) to produce diverse, semantically valid workload profiles. The pipeline is evaluated on a 12-service Kubernetes/Istio/Prometheus testbed across 960 total experiment runs (4 conditions × 4 fault categories × 3 services × 4 workload profiles × 5 replications). All comparisons use Mann-Whitney U tests with Bonferroni correction; effect sizes are Cliff's delta. SynthChaos achieves 23.4% higher coverage entropy than the handcrafted baseline (H = 3.96 vs. 3.21 bits; p < 0.001, δ = 0.71), reduces mean time to detect (MTTD) by 17.2% (29.9 vs. 36.1 s; p < 0.001, δ = 0.68), and improves anomaly detection F1 from 0.785 to 0.836 (p = 0.003, δ = 0.42). Critically, unconstrained LLM generation degrades all metrics below the handcrafted baseline, confirming that constraint mechanisms — not stochastic diversity alone — drive performance gains. Scenario usability reaches 97.6% with only 2.5% generation overhead.

Abstract: Large language model (LLM)-based coding assistants have achieved adoption rates unprecedented in the history of developer tooling, with over 62% of professional developers reporting active use as of 2024. The dominant narrative frames these tools as straightforward productivity multipliers, citing controlled task completion speedups of 55.8% in the most widely cited study. This paper examines whether that narrative survives contact with longitudinal production evidence and formal mathematical analysis. We present a cost-benefit model that captures both the velocity gain and the quality degradation trajectory of AI-assisted development, deriving a formal break-even expression that predicts when accumulated technical debt erases productivity gains. We then conduct a structured secondary analysis of three published industry cases — a longitudinal code quality study spanning 153 million lines of production code, a large-scale security evaluation of 1,692 AI-generated programs, and enterprise adoption survey evidence from over 2,000 professional developers — to validate the model's predictions against real data. Across all three cases we identify a consistent pattern: measurable short-term velocity gains accompanied by elevated code churn, increased duplication, and reproducible security vulnerability classes specific to LLM-generated code. We term this the AI-assisted productivity paradox and propose a formal governance framework for responsible tool integration. Our central finding is that the productivity case for LLM coding assistants is real but incomplete — standard metrics capture the benefit on a timescale of days to weeks while costs accumulate over months, creating a systematic measurement blind spot in most current adoption programs.

Abstract: A novel application of function hooking is presented, allowing software that runs on Windows and uses the MFC/Win32 API to have its user interfaces translated without modifying the original application or requiring access to source code. A launcher is used to run the target executable, loading it into the launcher’s address space and allowing the launcher to hook into functions that display text in the user interface. These functions then transparently replace original text with the translated version and call the original function. The method is effective, with no measurable runtime overhead, but visual imperfections remain due to differing lengths of text and potential numerical translations. Although in principle the translation can be performed at runtime, slow performance and unreliable results made this untenable.

Abstract: The preservation of Indigenous languages is hindered by the scarcity of technological resources and the lack of culturally appropriate educational tools, a reality that is evident in the Kayapó (Mebêngôkre) language. This article presents the development of a bilingual digital platform aimed at language teaching, learning, and linguistic preservation, designed through bibliographic research and software engineering principles. The study combines the identification of sociocultural requirements, prototyping with emerging technologies, and comparative functional validation. The results indicate that the platform is feasible, accessible, and compatible with Kayapó language learning practices, contributing to community engagement and linguistic revitalization.

Abstract: The increasing complexity of environmental analysis requires new approaches for real-time simulation across in-door and urban spaces. While computational fluid dynamics (CFD) models provide detailed representations of gas dispersion and aerosol transport, they are often computationally intensive and difficult to integrate into emerging AI-native digital twins and agentic AI systems. This pilot study presents a GPU-accelerated voxel simulation framework for modeling three-dimensional gas dispersion and aerosol transport using structured voxel representations derived from BIM, LiDAR, GIS, and digital twin environments. The framework provides physically informed, CFD-inspired simulation at sub-meter to meter-scale spatial resolutions while maintaining interactive runtime performance suitable for building management, ventilation analysis, environmental monitoring, hazard assessment, and emergency response applications. Transport dynamics are modeled using a discretized advection–diffusion formulation incorporating airflow-driven advection, diffusion, source emissions, and voxel-level sink mechanisms. A key contribution is the development of a voxel-native GPU-parallel computational architecture implemented in Python using Taichi kernels. Prototype simulations demonstrate stable transport behavior, browser-based three-dimensional visualization, and efficient execution on commodity GPU hardware. Experimental scenarios include a voxelized three-story Industry Foundation Classes (IFC) building model comprising approximately 34.5 million active voxels (582 × 382 × 155 voxels) and an urban-scale 3D city model spanning approximately 300 × 300 × 150 m and containing up to 13.9 million active voxels. Simulations containing tens of millions of voxels were completed within minutes on a single consumer-grade GPU, demonstrating the scalability of the framework. These results establish a practical foundation for AI-native digital twins and agentic AI-assisted environmental simulation applications.

Abstract: Compact malaria detectors for microcontrollers are almost always benchmarked on the NIH Malaria dataset with a per-cell random split. This leaks slide identity, because the cells come from only about 200 slides and a random split mixes same-slide cells across training and test. Under a leakage-free slide-disjoint protocol, the leakage fabricates architectural conclusions. Per-module ablation gains collapse to seed noise, and an apparent cross-site robustness variant loses most of its advantage. Headline accuracy falls from 97.1% to 95.6%, a gap that sits within the cross-seed noise, and all eight tested architectures move the same way. The evidence is this unanimous direction, not the size of any single gap. This self-critical finding is our main contribution. Two results still survive. First, MalariaNet, our 21K-parameter detector, reaches about 95.6% accuracy at 23.5KB of INT8 weights, with a numerically faithful on-chip forward on an STM32H743 at a 1.2FPS triage rate. Second, it is the second most interference-robust of the eight networks and the most robust microcontroller-deployable model. Scope is limited to P. falciparum thin-smear single cells. Slide-disjoint evaluation should become standard, and we provide MalariaNet as the first leakage-free, on-device-validated point-of-care malaria reference.

Abstract: Introduction: Proper analysis of lipidomic data requires specialized tools for data processing, normalization, and visualization. Limitations in lipid parsing, data processing and visualization in most commercially available packages generated gaps for optimal analysis of lipidomic data. LipidAnalyst is developed to fill the gaps in current packages, enabling efficient lipid parsing, data processing, visualization, and analysis of lipidomic data. Methods: We used R Shiny platform to develop LipidAnalyst which is hosted on MiServer, at “https://lipidanalyst.miserver.it.umich.edu/lipidanalyst/”. Results: LipidAnalyst capabilities are summarized in three major categories of data processing, visualization, and analysis. Data processing includes quality control filtering, normalization and quantification by internal standards, besides unique features for imputation, lipid parsing, combination, further normalization, transformation, and scaling. Visualization capabilities include demonstration of data distribution by boxplots or violin plots, principal component analysis (PCA) plots, hierarchical clustering heatmaps, differential mean lipid heatmaps, volcano plots, correlation plots, and debiased sparse partial correlation (DSPC) clustering plots. Analysis includes t-test, analysis of variance (ANOVA), DSPC, partial least square-differential analysis (PLS-DA), orthogonal partial least square-differential analysis (OPLS-DA), and Random Forest. Conclusion: LipidAnalyst is a powerful tool for optimal processing, visualization, and analysis of lipidomic data. LipidAnalyst allows comprehensive visualization of lipidomic data, empowering researchers to develop appropriate analytical plans accordingly.

Abstract: Software delivery is moving from deterministic pipelines toward autonomous environments where AI agents make runtime deployment decisions. Current DevOps governance assumes predictable execution and offers no mechanisms for constraining agents that generate plans on the fly. This leaves critical gaps in trust, accountability, policy enforcement, and failure containment. We present a conceptual architecture for bounded autonomous delivery, in which agents operate within externally enforced operational, security, reliability, and compliance constraints. The architecture separates planning, execution, policy enforcement, runtime verification, and human oversight into composable layers. We propose a taxonomy of autonomy levels and define operational invariants that limit what agents can do at runtime. A recurring scenario (deploying a payment microservice on an e-commerce platform during peak traffic) grounds the concepts in operational practice. The perspective positions governed autonomous delivery as an emerging discipline that demands new assurance models before organizations can trust agents with production systems.

Abstract: diffct is a CUDA-accelerated computed tomography library that exposes differentiable forward operators and their exact discrete adjoints for 2D parallel beam, 2D fan beam, and 3D cone beam imaging. The main branch provides the stable circular-orbit lineage released on PyPI, including Siddon and separable-footprint (SF) projector families, while the dev branch extends the Siddon-based projector/backprojector interface to arbitrary per-view trajectories through explicit source and detector arrays. This report rewrites the project description directly from the current source code, examples, tests, and the related CT literature. We formalize the geometry parameterization used by the implementation, derive the differentiable Siddon-style projector and its exact discrete adjoint, explain how gradients are transported through torch.autograd.Function wrappers backed by Numba CUDA kernels, document the analytical filtered backprojection and Feldkamp–Davis–Kress pipelines implemented on main and ported into the dev, and record how the circular-orbit SF algorithms from main fit into the broader architecture.

Abstract: The efficiency of data-driven research relies not only on high-quality data and sufficient computational resources but also depends sensitively on the personal knowledge management of the researcher. The multitude of digital artefacts created during the researcher’s daily workflow might comprise experimental results, simulation results, literate programming notebooks analysing experiments and simulations, statistical models, machine learning models, figures, tables, and conversations with generative artificial intelligence systems. In order to trace and track these interconnected research artefacts over several months of research or even extended research periods and different research projects, these artefacts need to be systematically named so that they can be referenced in note-keeping systems and research outputs. Therefore, the naming and referencing scheme for research artefacts needs to be flexible, consistent, efficient and support the linking of artefacts across different software frameworks and even classical laboratory notebooks. This article introduces a hierarchical naming scheme and the supporting open-source Python package contexere together with best practises for the personal knowledge management for postgraduate students and early career researchers, which provides a clear and linkable structure for data artefacts and thus supports effective personalised research workflows.

Abstract: Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents a design, im-plementation, and evaluation of an open-source IoT Monitoring Framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The open-source framework consists of twelve containerized microservices that can be started with a single command: docker compose up -d. The code has been released under the permissive MIT license. All stack performance testing was conducted using a simulated 1 hour test case based on a 100kWp PV system, 10kW wind turbine, and 50kWh battery powered campus mi-crogrid. Average P50 end-to-end latency was 27.2 ms and P99 end-to-end latency was 48.3 ms with 100% message delivery across 5,840 test messages with per-topic analy-sis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Com-parative analysis against ten existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that no prior open-source framework unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment.

Abstract: Independent journalists, human rights documentarians, and at-risk publishers overwhelmingly operate on shared-host WordPress installations nfrastructure they do not control and cannot audit. Existing tamper-evident publishing solutions require dedicated servers, compiled native libraries, or HSM-backed key storage, placing them beyond reach of the population most exposed to infrastructure-level content suppression. We present ArchivioMD, a cryptographic content integrity system deployable on shared-host PHP 7.4+ without native extensions, root access, or Composer dependencies. ArchivioMD combines four composable integrity layers: Deterministic content hashing across 22 algorithms with four-level HMAC hardening Six independent signing methods including a pure-PHP NIST FIPS 205 SLH-DSA post-quantum implementation Simultaneous anchoring to four independent external trust registries via a persistent queue with exponential backoff DANE/DNSSEC key corroboration An OpenPGP identity companion plugin (ArchivioID) adds configurable multi-signer threshold workflows and public proof pages for external verification. This paper formally defines the adversary model across four adversary classes (hostile infrastructure operator, law enforcement with legal compulsion, state-level attacker, and compromised administrator), maps each deployment scenario to its applicable adversary class, and proves that modification is detectable under any single-anchor compromise. Benchmark results are reported across shared hosting, VPS, and dedicated server tiers. The adversarial robustness of the 14-channel steganographic canary token system is analysed, including a formal keyspace proof of approximately 2198 combined subset configurations. To our knowledge, this is the first complete FIPS 205 SLH-DSA implementation deployable on commodity PHP hosting without native library dependencies.

Abstract: Load testing is a critical component of performance engineering, but traditional script-based methodologies often fail to accurately represent the dynamic, stochastic behavior of real users in modern distributed systems. As web applications grow in complexity, linear testing sequences leave critical execution paths untested, obscuring concurrency bottlenecks. This paper proposes a hybrid conceptual framework that integrates probabilistic navigation graphs with Markov transition models to simulate realistic, chaotic user behavior. The proposed model represents application workflows as directed graphs, employing Markov chains to dictate virtual user navigation across system states based on probabilistic weights. By shifting from deterministic scripting to stochastic workload generation, the framework theoretically increases state space coverage and path diversity while providing a more flexible representation of user navigation behavior. We detail the multi-layered system architecture, formalize the mathematical foundation of the traversal engine, and introduce rigorous analytical metrics including transition entropy and state coverage probability. Ultimately, this framework introduces a probabilistic graph traversal approach that enables the stochastic exploration of application state spaces and emergent concurrency behavior.

Abstract: The design and reliability assurance of embedded systems is a complex issue, since they need to handle not only digital behavior but also physical quantities such as time, cost, and sometimes randomness. In addition, since many embedded systems, such as networks and automobiles, have systems in which errors can be fatal, design verification for reliability assurance is an important research topic. With the above background, we adopt the approach of specifying and verifying embedded systems by formal models. Specifically, we focus on a priced probabilistic timed automaton as a specification description language, and propose a reachability analysis method based on Counterexample-guided abstraction refinement (CEGAR) to reduce the state explosion. To demonstrate the effectiveness of the proposed method, we attempt to verify the design of important wireless sensor networks (WSNs). In this paper, we model WSNs by a priced probabilistic timed automaton that can express their power characteristics in terms of cost, uncertainty in terms of probability, real-time in terms of time, and attribute WSNs’ characteristics to the cost bound probabilistic reachability problem. To the best of our knowledge, this paper is the first CEGAR development and implementation of a priced probabilistic timed automaton. We have developed a prototype of the verifier and confirmed that it is verifiable.

Abstract: The preparation of annotated datasets remains a critical bottleneck in the machine learning (ML) pipeline. Existing tools are fragmented across cloud-hosted services, self-hosted web applications, and lightweight desktop tools—none simultaneously ad-dressing diverse annotation modalities, offline-first operation, integrated training, and serverless collaboration. We present Annotix, an open-source, cross-platform desktop application built on a Rust backend (Tauri 2) and React 19 frontend, designed to unify the entire ML data preparation workflow within a single privacy-preserving environ-ment. To evaluate its practical utility, we conducted a controlled annotation efficiency study using 60 synthetic images (bounding box and mask tasks) annotated by three expert evaluators across Annotix, CVAT, and Label Studio, analyzed via Krus-kal-Wallis with Dunn–Bonferroni post-hoc tests, and a heuristic usability evaluation over standardized tasks on real medical images (retinographies and otoscopies). Re-sults demonstrate that Annotix achieves statistically significant annotation efficiency relative to established tools while offering substantially broader feature coverage, in-cluding 7 image annotation primitives, 19 ML training backends, ONNX-based infer-ence-assisted labeling, and serverless P2P collaboration. Annotix provides a complete, privacy-preserving ML data preparation workflow suited to regulated domains such as medical imaging and ecological monitoring and is freely available under the MIT license.

Abstract: Immutable code and steep transaction fees make smart contract deployment uniquely unforgiving. While continuous integration (CI/CD) pipelines excel at catching standard software bugs, applying exhaustive security tests to Web3 applications severely bottlenecks development through massive computational overhead and gas consumption. This paper presents a testing architecture designed specifically to resolve this tension between security depth and execution speed. The system pipelines three core engines. First, an AI-driven pre-execution gate flags immediate vulnerabilities. Next, a structural reduction module applies the k + 1 symmetric pattern to strip out redundant test permutations. Finally, the system constrains the remaining test suite using the NSGA-II evolutionary algorithm. This multi-objective optimizer dynamically schedules execution to maximize fault detection against strict, predefined gas budgets. To evaluate the model empirically, I bridged a localized EVM sandbox with a Python optimization engine. Results confirm the framework collapses exponential test generation and throttlesexecution costs without sacrificing critical security coverage. Ultimately, it offers a highly scalable path forward for modern DevSecOps.

Abstract: The contemporary education landscape is often marred by escalating costs and centralized pedagogical structures, which collectively create significant barriers to entry for millions of potential learners worldwide. This paper presents \textbf{Skill Link}, a sophisticated decentralized platform designed to democratize skill acquisition through a specialized credit-based barter system. Unlike conventional e-learning platforms that rely on traditional currency transactions, Skill Link enables a frictionless exchange of knowledge by utilizing a virtual credit economy where participants earn and spend "learning credits." To address the critical issue of credential fraud in decentralized environments, the platform integrates Ethereum-based blockchain technology to ensure the absolute immutability and verifiable authenticity of all earned certificates. Key innovations include a multi-tiered course classification system, an automated mock assessment framework with negative marking capabilities, an intelligent context-aware AI assistant powered by advanced language models, and a rigorous verification mechanism for professional social links (LinkedIn, GitHub, Indeed). Developed using the robust Django framework, Python-based Web3 utilities, and a secure PostgreSQL/SQLite back-end, Skill Link provides a highly secure, transparent, and scalable ecosystem for peer-to-peer knowledge sharing, ultimately fostering a global community of experts and lifelong learners. The system's architecture emphasizes data integrity through atomic transactions and cryptographic verification, ensuring a trustless environment for global skill exchange.

Abstract: This study quantitatively evaluates the performance of a non-invasive hybrid brain–computer interface (BCI) compared to a conventional mouse in pointing (point-and-click) tasks. A commercial wearable BCI (Brainfingers), based on electromyography (EMG) and electrooculography (EOG) signals with low-level electroencephalography (EEG) components, was assessed against a Microsoft Optical Mouse using ISO/TS 9241-411-based one-dimensional (1D) and two-dimensional (2D) target acquisition tasks. Pointer coordinates were recorded and analyzed using Fitts’ law metrics. A total of 48 non-disabled participants completed the experiments. The results reveal significant performance differences between the two input devices. The BCI device exhibits substantially lower performance than the mouse across the reported Fitts’ law measures. Mean throughput was 0.35 bits/s for the BCI and 6.03 bits/s for the mouse in the 1D tests, and 0.43 bits/s for the BCI and 5.17 bits/s for the mouse in the 2D tests. Despite the BCI’s low performance and although the present experiments involved non-disabled participants, the findings, considered alongside prior literature on Brainfingers and non-invasive BCIs for computer access, suggest that the device may still have assistive technology value for users with severe motor impairments.