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17th Asian Conference on Computer Vision, ACCV 2024

作者： Sargeant, James Wei Teng, Shyh Murshed, Manzur Paul, Manoranjan Brennan, David Institute of Innovation Science and Sustainability Federation University Ballarat Australia School of Information Technology Deakin University Burwood Australia School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia Wimmera Catchment Management Authority Horsham Australia

ISBN: (纸本)9789819609628

Understanding the amount of Soil Organic Carbon (SOC) at farm and field scale is a necessary precursor to effective management, important for both agricultural productivity and to reduce CO2 emissions. To avoid the prohibitive cost of measurement, SOC can be estimated by using multispectral images. In this study, we propose a novel Physics-Informed Convolutional Neural Network (CNN) to model well-known but noisy relationship between a soil index and SOC using the network’s loss function. This study is also conducted by resampling the European Land Use/Classification Area Survey (LUCAS) dataset to Sentinel-2 bands. Our experimental results show that our proposed network converges more quickly, has a lower root mean squared error (RMSE) and is more robust (as measured by the standard deviation of RMSE over multiple trials) than a compatible standard CNN. The operation of the novel Physics-Informed CNN is explained in terms of the components of the loss function. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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Quantitative Cybersecurity Analysis Framework for Cyber Physical Systems: A Conceptual Approach

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the Computer Society 2025年第1期6卷 613-626页

作者： Abdulhamid, Alhassan Kabir, Sohag Ghafir, Ibrahim Lei, Ci El Hindi, Khalil Hammoudeh, Mohammad University of Bradford Faculty of Engineering and Digital Technologies BradfordBD7 1DP United Kingdom King Saudi University College of Computer and Information Sciences Riyadh11543 Saudi Arabia Manchester Metropolitan University School of Computing and Mathematics ManchesterM15 6BH United Kingdom

Cyber-physical systems (CPS) are indispensable in various sectors, enabling convenient and efficient processes in today's rapidly evolving technological landscape. However, the integration of internet-enabled components with physical processes exposes CPS to numerous security threats, rendering them susceptible to potential cyber-attacks. This paper presents a quantitative analysis framework for evaluating the security attributes of CPS conceptual design. Focusing on CPS design architecture, the framework models and quantifies security attributes by considering various dimensions. The paper demonstrates the integration of qualitative expert inputs into a fuzzy logic system to address the challenges and uncertainties associated with vulnerability data in CPS security quantification. Additionally, it examines the statistical dependence of basic attack steps (BASs) and their impact on the overall system security analysis, taking into account the intricate connectivity of CPS and the vulnerabilities that attackers could exploit. The novelty of the proposed framework lies in its integrated approach to modelling and quantifying cybersecurity attributes in the CPS environment while considering uncertainties in vulnerability data and dependencies between security events. The computation of statistical and stochastic dependencies among BASs is achieved by mapping the attack tree (AT) to a higher statistical model of the Bayesian network (BN) model. The application of this framework was demonstrated using an intelligent glucose monitoring and insulin administration system (IGMIAS). The framework's general nature makes it adaptable for quantifying cybersecurity behaviours in any CPS environment. © 2020 IEEE.

关键词： Fuzzy logic

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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Multichannel Steganography: A Provably Secure Hybrid Steganographic Model for Secure Communication

arXiv

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arXiv 2025年

作者： Omego, Obinna Bosy, Michal School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston Universit London United Kingdom

This study introduces a novel steganographic model that synthesizes Steganography by Cover Modification (CMO) and Steganography by Cover Synthesis (CSY), enhancing both security and undetectability by generating cover messages or parameters while retaining the original cover’s form, thus minimizing detection risks and overcoming the limitations of single-method techniques. Building upon this model, a refined Steganographic Communication Protocol is proposed, enhancing resilience against sophisticated threats such as Multichannel Replay Attacks and Multichannel Man-in-the-Middle Attacks, fortifying the protocol against potential tampering and improving upon prior works. To evaluate the security of the proposed protocol, a novel adversarial model is developed simulating a probabilistic polynomial time (PPT) adversary capable of intercepting communications across multiple channels. This model assesses the adversary’s ability to compromise the protocol, providing a comprehensive security analysis. Finally, this study explores the practicality and adaptability of the model to both constrained environments like SMS banking and resource-rich settings such as blockchain transactions, demonstrating their potential to enhance financial services and security. These contributions present a robust and adaptable framework for secure steganographic communication, offering practical solutions for secure communications across diverse environments. Copyright © 2025, The Authors. All rights reserved.

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Convolutional neural networks

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Enhancing skin lesion classification: a CNN approach with human baseline comparison

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PeerJ Computer Science 2025年 11卷 1-45页

作者： Ajabani, Deep Shaikh, Zaffar Ahmed Yousef, Amr Ali, Karar Albahar, Marwan A. Source InfoTech Inc. LoganvilleGA United States Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Karachi Pakistan School of Engineering École Polytechnique Federale de Lausanne Lausanne Switzerland Electrical Engineering Department University of Business and Technology Jeddah Saudi Arabia Engineering Mathematics Department Alexandria University Alexandria Egypt VentureDive Pvt. Limited Karachi Pakistan College of Engineering and Computing in Al-Lith Umm Al-Qura University Makkah Saudi Arabia

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high-confidence CNN predictions while replacing low-confidence outputs with expert human assessments to enhance diagnostic accuracy. A CNN model utilizing the EfficientNetB3 backbone is trained on datasets from the ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges and evaluated on a 150-image test set. The model’s predictions are compared against assessments from 69 experienced medical professionals. Performance is assessed using receiver operating characteristic (ROC) curves and area under curve (AUC) metrics, alongside an analysis of human resource costs. The baseline CNN achieves an AUC of 0.822, slightly below the performance of human experts. However, the augmented hybrid approach improves the true positive rate to 0.782 and reduces the false positive rate to 0.182, delivering better diagnostic performance with minimal human involvement. This approach offers a scalable, resource-efficient solution to address variability in medical image analysis, effectively harnessing the complementary strengths of expert humans and CNNs. © 2025 Ajabani et al.

关键词： Convolutional neural networks

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Designing Lookahead Relocation Rules for the Container Relocation Problem with Genetic Programming 28th

Designing Lookahead Relocation Rules for the Container Rel...

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28th European Conference on Genetic Programming, EuroGP 2025, Held as Part of EvoStar 2025

作者： Đurasević, Marko Đumić, Mateja Gil-Gala, Francisco Javier Jakobović, Domagoj Faculty of Electrical Engineering and Computing University of Zagreb Zagreb10000 Croatia School of Applied Mathematics and Informatics J. J. Strossmayer University of Osijek Osijek31000 Croatia Department of Computing University of Oviedo Gijón Spain

ISBN: (纸本)9783031899904

The container relocation problem is an important combinatorial optimisation problem commonly found in warehouses and container ports. The goal of this problem is to retrieve all of the containers from the yard with the fewest container relocations between the stacks. Since the problem is NP-hard, various heuristics have been proposed to solve it, among which are relocation rules (RRs), simple constructive heuristics that incrementally construct the solution. However, it is quite difficult to design such RRs manually, so genetic programming has been applied to design new RRs automatically. A significant problem with RRs, whether manually or automatically designed, is that they usually have a limited view of the problem. This means that they will often make decisions that can negatively influence the future, meaning that the current decision would cause additional relocations. Therefore, this study investigates different relocation schemes that can be used within RRs to obtain rules with lookahead ability. These rules will enable containers to be relocated based on future information and, consequently, arranged better in the yard. For that purpose, three novel relocation schemes for automatically designed RRs are defined and evaluated on an existing problem set. The results demonstrate that integrating additional elements to evolve lookahead RRs can significantly improve the results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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AMT-Net: Adversarial Motion Transfer Network with Disentangled Shape and Pose for Realistic Image Animation

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IEEE Access 2025年 13卷 92712-92729页

作者： Teka, Nega Asebe Chen, Jianwen Gedamu, Kumie Assefa, Maregu Akmel, Feidu Zhou, Zhenting Wu, Weijie UESTC School of Information and Communication Engineering China UESTC Sichuan Artificial Intelligence Research Institute Yibin China Khalifa University School of Computing and Mathematics United Arab Emirates

Computer vision advancements allow motion transfer for animating static objects in images. However, current methods rely on manually collected motion labels and struggle with accurate shape and pose representation, particularly for human bodies due to occlusions and background variations. Thus, we propose an Adversarial Motion Transfer Network with a disentangled Shape and Pose representation for realistic image Animation (AMT-Net), utilizing an encoder-decoder adversarial structure. Specifically, we design a pose and shape learning module that captures the independent shape and pose information by training a discriminator with adversarial loss techniques, enhancing the generation of coherent animated frames. Furthermore, a motion estimation module is introduced to generate masks for objects in consecutive frames and identify occluded parts by creating occlusion maps from these masks and dense motion vectors. To evaluate the effectiveness of our approach, we conducted extensive experiments using four publicly available datasets, including VoxCeleb, TaiChiHD, TED-Talks, and MGif. The results emphasize the importance of landmark detection for video annotation and smooth transitions, while the independent shape and pose module helps capture precise representations. © 2013 IEEE.

关键词： Image coding

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The impact of dwell time on the contextual effect of visual and passive lead-in movements

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Journal of Neurophysiology 2025年第5期133卷 1520-1537页

作者： Alvarez-Hidalgo, Laura Franklin, David W. Howard, Ian S. School of Computing Engineering and Mathematics University of Plymouth Plymouth United Kingdom Neuromuscular Diagnostics TUM School of Medicine and Health Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany Munich Data Science Institute (MDSI) Technical University of Munich Munich Germany

Contextual cues arising from distinct movements are crucial in shaping control strategies for human movement. Here, we examine the impact of visual and passive lead-in movement cues on unimanual motor learning, focusing on the influence of “dwell time,” where two-part movements are separated by the interval between the end of the first movement and the start of the second. We used a robotic manipulandum to implement a point-to-point interference task with switching opposing viscous curl fields in male and female human participants. Consistent with prior research, in both visual and passive lead-in conditions, participants showed significant adaptation to opposing dynamics with short dwell times. As dwell time increased for both visual and passive signals, past movement information had less contextual influence. However, the efficacy of visual movement cues declined more rapidly as dwell times increased. At dwell times greater than 800 ms, the contextual influence of prior visual movement was small, whereas the effectiveness of passive lead-in movement was found to be significantly greater. This indicates that the effectiveness of sensory movement cues in motor learning is modality dependent. We hypothesize that such differences may arise because proprioceptive signals directly relate to arm movements, whereas visual inputs exhibit longer latency and, in addition, can relate to many aspects of movement in the environment and not just to our own arm movements. Therefore, the motor system may not always find visual movement cues as relevant for predictive control of dynamics. Copyright © 2025 The Authors.

关键词： dynamic learning lead-in context motor memory predictive compensation temporal decay

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