Handbook of Sociotechnical Systems

2026-02-26

SKIRP: an efficient inference strategy for video camouflaged object detection

2025-09-16

Camouflaged object detection (COD) in video sequences poses significant challenges due to the visual ambiguity between objects and backgrounds. Although existing approaches often rely on large-scale backbone networks for fine-grained detection, their dense full-frame inference strategy leads to considerable computational overhead. To address this, we introduce SKIRP (Sparse Keyframe Inference and Region-of-Interest Propagation), an efficient inference mechanism tailored for video-based COD tasks. SKIRP performs full-frame inference on a sparse set of keyframes to identify high-quality regions of interest (ROIs), which are then propagated to intermediate non-keyframes via interpolation. On non-keyframes, inference is conducted solely within the propagated ROIs with a significantly reduced input size and computation. SKIRP enables efficient localized segmentation without dense per-frame processing by combining sparse inference and region-level propagation. The experimental results demonstrate around a 30% reduction in the computation compared to the base detector while maintaining a comparable segmentation quality. This design balances accuracy and efficiency, making it well-suited for scalable COD in long videos and edge-deployable scenarios.

Green Video Camouflaged Object Detection

ArXiv.org · 2025-01-19

Camouflaged object detection (COD) aims to distinguish hidden objects embedded in an environment highly similar to the object. Conventional video-based COD (VCOD) methods explicitly extract motion cues or employ complex deep learning networks to handle the temporal information, which is limited by high complexity and unstable performance. In this work, we propose a green VCOD method named GreenVCOD. Built upon a green ICOD method, GreenVCOD uses long- and short-term temporal neighborhoods (TN) to capture joint spatial/temporal context information for decision refinement. Experimental results show that GreenVCOD offers competitive performance compared to state-of-the-art VCOD benchmarks.

Extending Formal Modeling for Resilient Systems Design

Insight · 2025-04-01

ABSTRACT Resilience is a much‐needed characteristic in systems that are expected to operate in uncertain environments for extended periods with a high likelihood of disruptive events. Resilience approaches today employ ad hoc methods and piece‐meal solutions that are difficult to verify and test, and do not scale. Furthermore, it is difficult to assess the long‐term impact of such ad hoc “resilience solutions.” This paper presents a flexible contract‐based approach that employs a combination of formal methods for verification and testing and flexible assertions and probabilistic modelling to handle uncertainty during mission execution. A flexible contract (FC) is a hybrid modelling construct that facilitates system verification and testing while offering the requisite flexibility to cope with non‐determinism. This paper illustrates the use of FCs for multi‐UAV swarm control in, partially observable, dynamic environments. However, the approach is sufficiently general for use in other domains such as self‐driving vehicle and adaptive power/energy grids.

Grand challenges in industrial and systems engineering

International Journal of Production Research · 2025-01-17 · 31 citations

Contemporary society faces a growing set of complex issues representing significant socioeconomic, health and well-being, environmental, and sustainability challenges. The discipline of industrial and systems engineering (ISE) can play an important role in addressing these issues. This paper identifies and discusses eight grand challenges for ISE. These grand challenges are (1) Artificial Intelligence (AI) For Business and Personal Use: Decision-Making and System Design and Operations, (2) Cybersecurity and Resilience, (3) Sustainability: Environment, Energy and Infrastructure, (4) Health Issues, (5) Social Issues, (6) Logistics and Supply Chain, (7) System Integration and Operations: Humans, Automation, and AI, and (8) Industrial and Systems Engineering Education. The discussed grand challenges were derived by accomplished ISE professionals who are the authors of this paper. The implications of the ISE grand challenges for education, training, research, and implementation of ISE principles and methodologies for the benefit of global society are discussed.

Masthead

IEEE Systems Man and Cybernetics Magazine · 2024-04-01

Green Video Camouflaged Object Detection

2024-12-03 · 1 citations

Camouflaged object detection (COD) aims to distinguish hidden objects embedded in an environment highly similar to the object. Conventional video-based COD (VCOD) methods explicitly extract motion cues or employ complex deep learning networks to handle the temporal information, which is limited by high complexity and unstable performance. In this work, we propose a green VCOD method named GreenVCOD. Built upon a green ICOD method, GreenVCOD uses long- and short-term temporal neighborhoods (TN) to capture joint spatial/temporal context information for decision refinement. Experimental results show that GreenVCOD offers competitive performance compared to state-of-the-art VCOD benchmarks.

Five Perspectives on Transdisciplinary Systems Engineering

Insight · 2024-04-01 · 4 citations

ABSTRACT This article offers insights from five INCOSE Fellows on the evolution and significance of transdisciplinarity in systems engineering. Michael Pennotti reviews the origins of systems engineering, emphasizing its inherent transdisciplinary nature and the need for continuous evolution. Azad Madni considers transdisciplinarity as systems engineering's true calling, crucial for the 21st century, and highlights his TRASEE™ education paradigm that underpins the Systems Architecting and Engineering program that he directs at the University of Southern California as pivotal for systems engineering's advancement. Hillary Sillitto sees the climate crisis as systems engineering's most critical and complex challenge, asserting transdisciplinarity's crucial role in addressing it. David Rousseau examines the cultural and scientific underpinnings of transdisciplinarity, presenting systems engineering as a prime example. Peter Brook envisions the joint evolution of systems sciences and systems engineering to confront future challenges, advocating for transdisciplinarity as an essential role in systems engineering leadership for addressing global challenges.

GreenCOD: A Green Camouflaged Object Detection Method

APSIPA Transactions on Signal and Information Processing · 2024-12-02 · 4 citations

We introduce GreenCOD, a green method for detecting camouflaged ob jects distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks. Traditional camouflaged object detection approaches rely on complex deep neural networks, seeking performance improvements by backpropagation-based finetuning. However, such methods are typically computationally demanding and exhibit only marginal performance variations across different models. It raises the question of whether effective training can be achieved without backpropagation. In this direction, our work proposes a new paradigm that utilizes gradient boosting for COD. This approach significantly simplifies the model design, resulting in a system that requires fewer parameters and operations and maintains high performance compared to state-of-the-art deep learning models. Remarkably, our models are trained without backpropagation and achieve the best performance with fewer than 20G Multiply-Accumulate Operations. This new, more efficient paradigm opens avenues for further exploration in green, backpropagation-free model training. We make GreenCOD source code and on-device demo available at https://greencod.ai/ for futher research.

GreenCOD: A Green Camouflaged Object Detection Method

arXiv (Cornell University) · 2024-05-25

We introduce GreenCOD, a green method for detecting camouflaged objects, distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks (DNNs). Traditional camouflaged object detection (COD) approaches often rely on complex deep neural network architectures, seeking performance improvements through backpropagation-based fine-tuning. However, such methods are typically computationally demanding and exhibit only marginal performance variations across different models. This raises the question of whether effective training can be achieved without backpropagation. Addressing this, our work proposes a new paradigm that utilizes gradient boosting for COD. This approach significantly simplifies the model design, resulting in a system that requires fewer parameters and operations and maintains high performance compared to state-of-the-art deep learning models. Remarkably, our models are trained without backpropagation and achieve the best performance with fewer than 20G Multiply-Accumulate Operations (MACs). This new, more efficient paradigm opens avenues for further exploration in green, backpropagation-free model training.