detection

This study addresses sequential multi-stream detection under the constraint that only one data stream can be observed at each time step, with the goal of simultaneously controlling global false alarm and missed detection probabilities while minimizing detection delay. To this end, the work introduces a novel optimality criterion based on the expected order statistics of detection times and proposes an active sampling strategy—dubbed “follow-the-leader”—that integrates exploration and exploitation mechanisms. Theoretical analysis demonstrates that the proposed strategy achieves asymptotic optimality for all such criteria as error probabilities vanish. Numerical experiments further confirm its superior finite-sample performance compared to existing methods and show that it closely approaches the performance of an ideal oracle policy that has full knowledge of the anomalous streams.

This work addresses the challenges of high early-stage uncertainty in manufacturing monitoring system development—leading to redundant modeling and substantial training costs—and the limited transferability of filtering pipelines in cross-domain image segmentation tasks. To tackle these issues, the authors propose a problem-centric design paradigm that constructs an abstract system model to continuously accumulate and retrieve historical segmentation tasks along with their associated filtering pipelines, enabling solution reuse and incremental optimization. The approach integrates similarity-based problem retrieval, abstract modeling, pipeline reuse, and a retrieval-augmented evolutionary learning mechanism. Experimental results demonstrate that the method significantly reduces training costs and late-stage revision risks, provides the first systematic validation of filtering pipeline transferability across similar segmentation tasks, and achieves a favorable balance among complexity, technical requirements, and reliability under lightweight model constraints.

Complex backgrounds and multi-scale anomalies in manufacturing images cause modeling bias and anomaly contamination, undermining the reliability of unsupervised defect detection. Method: This paper proposes a two-stage suspicious patch iterative identification framework: first, suspicious patches are filtered and removed via dual-reconstruction residual analysis and statistical consistency verification to construct a clean normal sample set; subsequently, this purified set drives adaptive patch-wise reconstruction and precise anomaly localization. Contribution/Results: The method decouples anomaly interference from model learning, eliminating reliance on prior assumptions about anomaly characteristics—unlike conventional matrix decomposition approaches—and supports detection of arbitrarily sized defects. Evaluated on both synthetic and real-world production-line datasets, it achieves an average 12.6% improvement in F1-score, significantly enhancing detection sensitivity for small and irregular anomalies. Moreover, it demonstrates superior robustness against background clutter and stronger cross-scenario generalization compared to state-of-the-art unsupervised methods.

This study addresses interpretive discrepancies between monitored individuals and supervising authorities in electronic monitoring systems, where divergent standpoints lead to misjudgments of behavior and imbalanced interactions. Drawing on China’s community correction system, the research employs semi-structured interviews (with 26 supervisees and 12 supervisors), situational analysis, and a CSCW theoretical framework to uncover structural misalignments in data interpretation. Introducing the concept of “interpretive misalignment,” the work reconceptualizes continuous sensing as distributed interpretive labor and identifies five categories of behavioral responses stemming from asymmetries in data, context, and inference. Building on these findings, the study proposes design directions that enhance transparency and mutual negotiability in data-driven decision-making, offering novel perspectives on intelligibility, contestability, and accountability across system boundaries.

This work addresses the challenge scientists face in efficiently transforming raw sensor data streams into actionable insights across edge-cloud infrastructures, hindered by the need for cross-domain expertise to manage heterogeneous systems and emerging platforms such as DPUs, which impedes rapid prototyping. To overcome this barrier, the authors propose a novel paradigm that integrates pattern-based workflow engineering with AI-assisted development. Implemented on the FABRIC testbed using the Pegasus workflow system and exemplified by the Orcasound hydrophone workflow, this approach enables swift construction of applications for air quality, seismic, and soil moisture monitoring. The framework supports modular extensibility and edge deployment, substantially lowering the barrier for non-expert users to iteratively develop distributed applications. Empirical validation across multiple use cases demonstrates its effectiveness in enhancing development efficiency, accelerating prototyping cycles, and accumulating practical deployment experience.

This study addresses the challenge of rapid change-point detection in high-dimensional multisensor systems under structural constraints and limited sensing resources. By integrating sparse modeling, heterogeneous data fusion, and a resource-adaptive sequential sampling strategy, the work extends classical change-point detection theory to large-scale, resource-constrained sensing scenarios and incorporates machine learning to handle cases with unknown system models. The proposed approach unifies sparse signal processing, multi-stream statistical decision-making, and resource-constrained optimization to enable simultaneous detection of multiple change points. This framework significantly enhances both applicability and scalability in high-dimensional, heterogeneous, and resource-limited environments while maintaining high detection efficiency.