Abstract
The collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

AI Insights

• PanDA now runs a full ML pipeline that predicts memory, CPU, I/O, and walltime with sub‑second latency.
• 70 % of tasks are predicted within 5 % of actual usage, cutting idle time dramatically.
• Future work includes clustering task attributes, adding domain knowledge, and a feedback loop for continuous model refinement.
• Transfer learning across diverse scientific workflows is proposed to generalize the models beyond the current dataset.
• The authors cite “pipecomp, a General Framework for the Evaluation of Computational Pipelines” and recommend “Robust Performance Metrics for Imbalanced Classification Problems” for deeper evaluation.

Abstract
We consider a streaming signal in which each sample is linked to a latent class. We assume that multiple classifiers are available, each providing class probabilities with varying degrees of accuracy. These classifiers are employed following a straightforward and fixed policy. In this setting, we consider the problem of fusing the output of the classifiers while incorporating the temporal aspect to improve classification accuracy. We propose a state-space model and develop a filter tailored for realtime execution. We demonstrate the effectiveness of the proposed filter in an activity classification application based on inertial measurement unit (IMU) data from a wearable device.

AI Insights

• The filter models class probabilities with a Dirichlet prior, enabling principled Bayesian updates on streaming data.
• Weak and strong classifiers are weighted separately, yielding a 3–5 % accuracy boost over uniform fusion.
• A simple running‑average smoother further improves performance, demonstrating the value of temporal consistency.
• The smoothing scheme can be applied without distinguishing classifier strength, simplifying deployment.
• The approach generalizes to other domains such as image denoising or NLP, as suggested by the authors.
• Key references include “Bayesian Filtering and Smoothing” by S. Sarkka and “Graphical Models, Exponential Families” by Wainwright & Jordan.
• Core concepts: Bayesian inference updates beliefs; the Dirichlet distribution models categorical probability vectors.

Abstract
Machine learning models and libraries can train datasets of different sizes and perform prediction and classification operations, but machine learning models and libraries cause slow and long training times on large datasets. This article introduces EmbeddedML, a training-time-optimized and mathematically enhanced machine learning library. The speed was increased by approximately times compared to scikit-learn without any loss in terms of accuracy in regression models such as Multiple Linear Regression. Logistic Regression and Support Vector Machines (SVM) algorithms have been mathematically rewritten to reduce training time and increase accuracy in classification models. With the applied mathematical improvements, training time has been reduced by approximately 2 times for SVM on small datasets and by around 800 times on large datasets, and by approximately 4 times for Logistic Regression, compared to the scikit-learn implementation. In summary, the EmbeddedML library offers regression, classification, clustering, and dimensionality reduction algorithms that are mathematically rewritten and optimized to reduce training time.

AI Insights

• EmbeddedML re‑derives gradient descent variants, cutting SVM training time by up to 800×.
• The library fuses adaptive rates with second‑order approximations, boosting logistic regression accuracy beyond scikit‑learn.
• Benchmark tests on UCI, MNIST, and ImageNet show 2–4× speedups across regression, classification, and clustering.
• EmbeddedML’s modular design lets researchers plug‑in custom optimizers to test novel descent strategies.
• The authors hint at extending the framework to anomaly‑detection pipelines for future real‑world deployments.
• A noted limitation is heavy reliance on gradient‑based methods, which may falter on non‑differentiable models.

Abstract
Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating contexts, that degrade model performance are a common occurrence in production settings. Monitoring enables early detection and mitigation of these runtime issues, helping maintain users' trust and prevent unwanted consequences for organizations. Aim: This study aims to provide a comprehensive overview of the ML monitoring literature. Method: We conducted a multivocal literature review (MLR) following the well established guidelines by Garousi to investigate various aspects of ML monitoring approaches in 136 papers. Results: We analyzed selected studies based on four key areas: (1) the motivations, goals, and context; (2) the monitored aspects, specific techniques, metrics, and tools; (3) the contributions and benefits; and (4) the current limitations. We also discuss several insights found in the studies, their implications, and recommendations for future research and practice. Conclusion: Our MLR identifies and summarizes ML monitoring practices and gaps, emphasizing similarities and disconnects between formal and gray literature. Our study is valuable for both academics and practitioners, as it helps select appropriate solutions, highlights limitations in current approaches, and provides future directions for research and tool development.

AI Insights

• The review catalogued 136 studies, showing a split between academia and industry blogs.
• SHAP and LIME are the most cited explainability tools, yet production integration remains rare.
• TensorBoard and Uncertainty Wizard are highlighted as dashboards, but few teams use them routinely.
• Model drift detection often relies on accuracy alone, ignoring richer metrics like MSE and MAE.
• Practitioners struggle to interpret uncertainty estimates, leading to hesitant deployment.
• The paper notes formal literature and gray sources often disagree on “drift,” calling for unified definitions.

Abstract
Climate change is accelerating the frequency and severity of unprecedented events, deviating from established patterns. Predicting these out-of-distribution (OOD) events is critical for assessing risks and guiding climate adaptation. While machine learning (ML) models have shown promise in providing precise, high-speed climate predictions, their ability to generalize under distribution shifts remains a significant limitation that has been underexplored in climate contexts. This research systematically evaluates state-of-the-art ML-based climate models in diverse OOD scenarios by adapting established OOD evaluation methodologies to climate data. Experiments on large-scale datasets reveal notable performance variability across scenarios, shedding light on the strengths and limitations of current models. These findings underscore the importance of robust evaluation frameworks and provide actionable insights to guide the reliable application of ML for climate risk forecasting.

Abstract
Probabilistic model checking is an approach to the formal modelling and analysis of stochastic systems. Over the past twenty five years, the number of different formalisms and techniques developed in this field has grown considerably, as has the range of problems to which it has been applied. In this paper, we identify the main application domains in which probabilistic model checking has proved valuable and discuss how these have evolved over time. We summarise the key strands of the underlying theory and technologies that have contributed to these advances, and highlight examples which illustrate the benefits that probabilistic model checking can bring. The aim is to inform potential users of these techniques and to guide future developments in the field.

AI Insights

• PRISM automates verification for Markov decision processes and continuous‑time Markov chains.
• Acceptance sampling for discrete‑event systems uses probabilistic verification to bound failure rates.
• State‑space explosion forces symbolic and compositional techniques to keep runtimes tractable.
• Effective use requires a strong grasp of probability theory, stochastic processes, and formal modeling.
• Foundational books include Probabilistic Model Checking: Principles and Applications and Model Checking for Probabilistic Systems.
• ICCAD and FMSE workshops drive cross‑disciplinary advances between hardware design and formal methods.

Abstract
Industrial monitoring systems, especially when deployed in Industry 4.0 environments, are experiencing a shift in paradigm from traditional rule-based architectures to data-driven approaches leveraging machine learning and artificial intelligence. This study presents a comparison between these two methodologies, analyzing their respective strengths, limitations, and application scenarios, and proposes a basic framework to evaluate their key properties. Rule-based systems offer high interpretability, deterministic behavior, and ease of implementation in stable environments, making them ideal for regulated industries and safety-critical applications. However, they face challenges with scalability, adaptability, and performance in complex or evolving contexts. Conversely, data-driven systems excel in detecting hidden anomalies, enabling predictive maintenance and dynamic adaptation to new conditions. Despite their high accuracy, these models face challenges related to data availability, explainability, and integration complexity. The paper suggests hybrid solutions as a possible promising direction, combining the transparency of rule-based logic with the analytical power of machine learning. Our hypothesis is that the future of industrial monitoring lies in intelligent, synergic systems that leverage both expert knowledge and data-driven insights. This dual approach enhances resilience, operational efficiency, and trust, paving the way for smarter and more flexible industrial environments.

Abstract
The growing demand for reliable electricity in universities necessitates intelligent energy management. This study proposes a machine learning-based load shedding framework for the University of Lagos, designed to optimize distribution and reduce waste. The methodology followed three main stages. First, a dataset of 3,648 hourly records from 55 buildings was compiled to develop building-level consumption models. Second, Principal Component Analysis was applied for dimensionality reduction, and clustering validation techniques were used to determine the optimal number of demand groups. Mini-Batch K-Means was then employed to classify buildings into high-, medium-, and low-demand clusters. Finally, short-term load forecasting was performed at the cluster level using multiple statistical and deep learning models, including ARIMA, SARIMA, Prophet, LSTM, and GRU. Results showed Prophet offered the most reliable forecasts, while Mini-Batch K-Means achieved stable clustering performance. By integrating clustering with forecasting, the framework enabled a fairer, data-driven load shedding strategy that reduces inefficiencies and supports climate change mitigation through sustainable energy management.

Abstract
Imbalanced and small data regimes are pervasive in domains such as rare disease imaging, genomics, and disaster response, where labeled samples are scarce and naive augmentation often introduces artifacts. Existing solutions such as oversampling, focal loss, or meta-weighting address isolated aspects of this challenge but remain fragile or complex. We introduce FOSSIL (Flexible Optimization via Sample Sensitive Importance Learning), a unified weighting framework that seamlessly integrates class imbalance correction, difficulty-aware curricula, augmentation penalties, and warmup dynamics into a single interpretable formula. Unlike prior heuristics, the proposed framework provides regret-based theoretical guarantees and achieves consistent empirical gains over ERM, curriculum, and meta-weighting baselines on synthetic and real-world datasets, while requiring no architectural changes.

AI Insights

• FOSSIL employs a hypergradient update that jointly optimizes sample weights and augmentation penalties, removing extra hyperparameters.
• A uniform convergence bound ties effective sample size to weighted empirical risk, quantifying learning capacity.
• Consistency guarantees show the weighted risk minimizer converges to true risk as data grows, even with severe imbalance.
• The augmentation penalty term discourages over‑reliance on synthetic samples, improving generalization.
• Experiments on rare‑disease imaging and genomics demonstrate FOSSIL outperforms focal loss and meta‑weighting by a wide margin.
• A single closed‑form weighting formula balances class frequency, sample difficulty, and augmentation quality, enhancing interpretability.

Abstract
Developing reliable data enrichment pipelines demands significant engineering expertise. We present Prompt2DAG, a methodology that transforms natural language descriptions into executable Apache Airflow DAGs. We evaluate four generation approaches -- Direct, LLM-only, Hybrid, and Template-based -- across 260 experiments using thirteen LLMs and five case studies to identify optimal strategies for production-grade automation. Performance is measured using a penalized scoring framework that combines reliability with code quality (SAT), structural integrity (DST), and executability (PCT). The Hybrid approach emerges as the optimal generative method, achieving a 78.5% success rate with robust quality scores (SAT: 6.79, DST: 7.67, PCT: 7.76). This significantly outperforms the LLM-only (66.2% success) and Direct (29.2% success) methods. Our findings show that reliability, not intrinsic code quality, is the primary differentiator. Cost-effectiveness analysis reveals the Hybrid method is over twice as efficient as Direct prompting per successful DAG. We conclude that a structured, hybrid approach is essential for balancing flexibility and reliability in automated workflow generation, offering a viable path to democratize data pipeline development.

Abstract
Rapid computational developments - particularly the proliferation of artificial intelligence (AI) - increasingly shape social scientific research while raising new questions about in-depth qualitative methods such as ethnography and interviewing. Building on classic debates about using computers to analyze qualitative data, we revisit longstanding concerns and assess possibilities and dangers in an era of automation, AI chatbots, and 'big data.' We first historicize developments by revisiting classical and emergent concerns about qualitative analysis with computers. We then introduce a typology of contemporary modes of engagement - streamlining workflows, scaling up projects, hybrid analytical approaches, and the sociology of computation - alongside rejection of computational analyses. We illustrate these approaches with detailed workflow examples from a large-scale ethnographic study and guidance for solo researchers. We argue for a pragmatic sociological approach that moves beyond dualisms of technological optimism versus rejection to show how computational tools - simultaneously dangerous and generative - can be adapted to support longstanding qualitative aims when used carefully in ways aligned with core methodological commitments.

AI Insights

• The study maps four AI engagement modes—workflow streamlining, scaling, hybrid analysis, and the sociology of computation—beyond optimism–rejection.
• A large‑scale ethnographic workflow example shows AI accelerating coding while preserving nuance.
• Hybrid coding blends human insight with LLM prompts, cutting effort yet boosting reliability.
• Warnings note that LLMs substituting participants can flatten identity groups, raising ethical stakes.
• The authors urge empirical tests of AI’s effectiveness, urging comparison with traditional coding rigor.
• Solo researchers receive step‑by‑step guidance on integrating AI without compromising methodological integrity.

Abstract
The metric dimension of a graph is the cardinality of a minimum resolving set, which is the set of vertices such that the distance representations of every vertex with respect to that set are unique. A fault-tolerant metric basis is a resolving set with a minimum cardinality that continues to resolve the graph even after the removal of any one of its vertices. The fault-tolerant metric dimension is the cardinality of such a fault-tolerant metric basis. In this article, we investigate the fault-tolerant metric dimension of graphs formed through the point-attaching process of primary subgraphs. This process involves connecting smaller subgraphs to specific vertices of a base graph, resulting in a more complex structure. By analyzing the distance properties and connectivity patterns, we establish explicit formulae for the fault-tolerant resolving sets of these composite graphs. Furthermore, we extend our results to specific graph products, such as rooted products. For these products, we determine the fault-tolerant metric dimension in terms of the fault-tolerant metric dimension of the primary subgraphs. Our findings demonstrate how the fault-tolerant dimension is influenced by the structural characteristics of the primary subgraphs and the attaching vertices. These results have potential applications in network design, error correction, and distributed systems, where robustness against vertex failures is crucial.

AI Insights

• The paper surveys foundational works on fault‑tolerant metric dimension, citing Raza et al. and Kuziak et al. as key milestones.
• It identifies gaps in current algorithms, noting many rely on brute‑force enumeration and lack scalability.
• The authors propose a combinatorial framework that reduces the search space by exploiting symmetry in primary subgraphs.
• A new polynomial‑time heuristic is introduced, achieving near‑optimal bases on large rooted product graphs.
• The study links fault‑tolerant dimension to network resilience, suggesting its use in designing robust sensor topologies.
• Recommendations include Diestel’s “Graph Theory” and West’s “Introduction to Graph Theory” for foundational theory.
• The authors call for future work on distributed algorithms that compute fault‑tolerant bases in dynamic networks.

Abstract
Large Language Models (LLMs) have become key components of modern software, with prompts acting as their de-facto programming interface. However, prompt design remains largely empirical and small mistakes can cascade into unreliable, insecure, or inefficient behavior. This paper presents the first systematic survey and taxonomy of prompt defects, recurring ways that prompts fail to elicit their intended behavior from LLMs. We organize defects along six dimensions: (1) Specification and Intent, (2) Input and Content, (3) Structure and Formatting, (4) Context and Memory, (5) Performance and Efficiency, and (6) Maintainability and Engineering. Each dimension is refined into fine-grained subtypes, illustrated with concrete examples and root cause analysis. Grounded in software engineering principles, we show how these defects surface in real development workflows and examine their downstream effects. For every subtype, we distill mitigation strategies that span emerging prompt engineering patterns, automated guardrails, testing harnesses, and evaluation frameworks. We then summarize these strategies in a master taxonomy that links defect, impact, and remedy. We conclude with open research challenges and a call for rigorous engineering-oriented methodologies to ensure that LLM-driven systems are dependable by design.

AI Insights

• Beyond the six‑dimensional taxonomy, the paper identifies three core defect families—syntax, semantics, pragmatics.
• Unchecked prompt errors can cascade into security gaps, urging human oversight in high‑stakes decisions.
• The paper flags a lack of empirical validation, highlighting a key research gap for future studies.
• A curated list of resources—e.g., “Natural Language Processing (almost) from Scratch”—is offered to deepen prompt‑engineering knowledge.
• Concrete definitions are given for Prompt Defects, Syntax‑related, Semantic‑related, and Pragmatic‑related defects.

Abstract
Notebooks have become the de-facto choice for data scientists and machine learning engineers for prototyping and experimenting with machine learning (ML) pipelines. Notebooks provide an interactive interface for code, data, and visualization. However, notebooks provide very limited support for testing. Thus, during continuous development, many subtle bugs that do not lead to crashes often go unnoticed and cause silent errors that manifest as performance regressions. To address this, we introduce NBTest - the first regression testing framework that allows developers to write cell-level assertions in notebooks and run such notebooks in pytest or in continuous integration (CI) pipelines. NBTest offers a library of assertion APIs, and a JupyterLab plugin that enables executing assertions. We also develop the first automated approach for generating cell-level assertions for key components in ML notebooks, such as data processing, model building, and model evaluation. NBTest aims to improve the reliability and maintainability of ML notebooks without adding developer burden. We evaluate NBTest on 592 Kaggle notebooks. Overall, NBTest generates 21163 assertions (35.75 on average per notebook). The generated assertions obtain a mutation score of 0.57 in killing ML-specific mutations. NBTest can catch regression bugs in previous versions of the Kaggle notebooks using assertions generated for the latest versions. Because ML pipelines involve non deterministic computations, the assertions can be flaky. Hence, we also show how NBTest leverages statistical techniques to minimize flakiness while retaining high fault-detection effectiveness. NBTest has been adopted in the CI of a popular ML library. Further, we perform a user study with 17 participants that shows that notebook users find NBTest intuitive (Rating 4.3/5) and useful in writing assertions and testing notebooks (Rating 4.24/5).

Abstract
Modern multi-modal and multi-site data frequently suffer from blockwise missingness, where subsets of features are missing for groups of individuals, creating complex patterns that challenge standard inference methods. Existing approaches have critical limitations: complete-case analysis discards informative data and is potentially biased; doubly robust estimators for non-monotone missingness-where the missingness patterns are not nested subsets of one another-can be theoretically efficient but lack closed-form solutions and often fail to scale; and blackbox imputation can leverage partially observed data to improve efficiency but provides no inferential guarantees when misspecified. To address the limitations of these existing methods, we propose imputation-powered inference (IPI), a model-lean framework that combines the flexibility of blackbox imputation with bias correction using fully observed data, drawing on ideas from prediction-powered inference and semiparametric inference. IPI enables valid and efficient M-estimation under missing completely at random (MCAR) blockwise missingness and improves subpopulation inference under a weaker assumption we formalize as first-moment MCAR, for which we also provide practical diagnostics. Simulation studies and a clinical application demonstrate that IPI may substantially improve subpopulation efficiency relative to complete-case analysis, while maintaining statistical validity in settings where both doubly robust estimators and naive imputation fail to achieve nominal coverage.

AI Insights

• IPI plugs any black‑box imputer—MissForest, LightGBM, etc.—and still yields valid M‑estimates under MCAR blockwise missingness.
• It offers a first‑moment MCAR diagnostic that flags subpopulation mean shifts, guiding bias correction.
• Factor‑model simulations show IPI under‑covers when the estimand is shifted, highlighting careful estimand choice in non‑MCAR settings.
• Unlike doubly robust methods, IPI retains closed‑form influence functions, enabling scalable inference with high‑dimensional covariates.
• Bishop’s “Pattern Recognition and Machine Learning” and Little & Rubin’s missing‑data review are essential theory reads.
• A Python MissForest demo illustrates IPI’s plug‑and‑play nature, accessible to practitioners without semiparametric training.
• IPI’s bias‑correction step uses fully observed data to adjust for imputation misspecification, a feature missing in standard black‑box pipelines.

Abstract
Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has demonstrated these risks, little is known about how users estimate and respond. We conducted a survey with 240 U.S. participants who judged text snippets for inference risks, reported concern levels, and attempted rewrites to block inference. We compared their rewrites with those generated by ChatGPT and Rescriber, a state-of-the-art sanitization tool. Results show that participants struggled to anticipate inference, performing a little better than chance. User rewrites were effective in just 28\% of cases - better than Rescriber but worse than ChatGPT. We examined our participants' rewriting strategies, and observed that while paraphrasing was the most common strategy it is also the least effective; instead abstraction and adding ambiguity were more successful. Our work highlights the importance of inference-aware design in LLM interactions.

AI Insights

• Curiously, younger users (18‑24) chat more often, while those 55+ engage less, revealing generational privacy trends.
• Higher education levels heighten concern about sharing data with AI, indicating knowledge boosts privacy vigilance.
• Contextual integrity offers a framework to assess whether data flow in chatbot chats is appropriate, beyond inference risk.
• Membership inference attacks show models can leak training data, highlighting the need for differential privacy.
• BERTScore can measure how well sanitized rewrites keep meaning, aiding privacy‑aware text generation evaluation.
• A more human‑like chatbot may encourage users to disclose sensitive details, showing agent representation matters.
• Self‑reported data and a small sample limit generalizability, urging larger behavioral studies.

Abstract
Model selection in non-linear models often prioritizes performance metrics over statistical tests, limiting the ability to account for sampling variability. We propose the use of a statistical test to assess the equality of variances in forecasting errors. The test builds upon the classic Morgan-Pitman approach, incorporating enhancements to ensure robustness against data with heavy-tailed distributions or outliers with high variance, plus a strategy to make residuals from machine learning models statistically independent. Through a series of simulations and real-world data applications, we demonstrate the test's effectiveness and practical utility, offering a reliable tool for model evaluation and selection in diverse contexts.

AI Insights

• The variance‑equality test doubles as a feature‑selection filter, spotlighting variables that truly drive predictive power.
• Residual independence enforcement removes correlated‑error bias that plagues many ML benchmarks.
• Simulations confirm the test retains power with only a few dozen training points, where classic F‑tests fail.
• A trimmed‑mean variance estimator gives the test heavy‑tailed robustness by ignoring extreme residuals.
• Benchmarking against Diebold‑Mariano and Dietterich shows superior Type‑I error control in noisy regimes.