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SEDNet: Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution for Vehicle Re-identification

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Alexandria Engineering Journal 2025年 128卷 297-305页

作者： Mingfu Xiong Tanghao Gui Zhihong Sun Saeed Anwar Aziz Alotaibi Khan Muhammad School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan 430200 China Department of Information Security Naval University of Engineering Wuhan 430030 China The University of Western Australia (UWA) Crawley 6009 Australia Department of Computer Science College of Computing and Information Technology Taif University Taif 21974 Saudi Arabia Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea

To address the issue of information redundancy (such as color and vehicle model) caused by excessive emphasis on local features in vehicle re-identification, this paper proposes a Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution (SEDNet). The proposed SEDNet framework consists of three unique branches: a global embedded multi-head encoder (GEME), local dual-dense atrous convolution (LDAC), and auxiliary attribute embedding (AAM). The GEME branch integrates the global appearance features of the vehicle to enhance consistency in descriptions from different perspectives. To suppress redundant information such as color and vehicle model information, and refine local features, the LDAC branch employs an attention mechanism to capture multiscale features using convolutional kernels with varying dilation rates. In addition, the AAM branch uses vehicle metadata, such as direction and camera perspectives, to enhance feature robustness. Our proposed SEDNet method has been rigorously tested on the mainstream benchmark vehicle re-identification datasets, including VeRi-776, VehicleID, and VeRi-Wild. The results show that our method enhances the mAP by 2.2%, 2.2%, and 0.2%, respectively, when compared to the latest methods, all evaluated on a regular scale. Additional experiments conducted on the Market-1501 and DukeMTMC-reID datasets further verify our method’s generalization capability.

关键词： Vehicle re-identification Embedded encoder Dense atrous convolution Intelligent networks Synergistic learning

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy 23

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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Limits of specifiability for sensor-based robotic planning tasks

arXiv

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arXiv 2025年

作者： Sakçak, Başak Shell, Dylan A. O’Kane, Jason M. Center for Ubiquitous Computing University of Oulu Finland Department of Computer Science and Engineering Texas A&M University College StationTX United States

There is now a large body of techniques, many based on formal methods, for describing and realizing complex robotics tasks, including those involving a variety of rich goals and time-extended behavior. This paper explores the limits of what sorts of tasks are specifiable, examining how the precise grounding of specifications —that is, whether the specification is given in terms of the robot’s states, its actions and observations, its knowledge, or some other information— is crucial to whether a given task can be specified. While prior work included some description of particular choices for this grounding, our contribution treats this aspect as a first-class citizen: we introduce notation to deal with a large class of problems, and examine how the grounding affects what tasks can be posed. The results demonstrate that certain classes of tasks are specifiable under different combinations of groundings. © 2025, CC BY.

关键词： Specifications

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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A hybrid blockchain and AI-based approach for attack protection to secure internet of medical things networks

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Alexandria Engineering Journal 2025年 128卷 223-230页

作者： Hadeel Alsolai Fahd N. Al-Wesabi Ali M. Al-Sharafi Asma Alshuhail Abdulbasit A. Darem Bandar M. Alghamdi Fouad Shoie Alallah Abdulrahman Alzahrani Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha 67714 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems.

关键词： Internet of medical things (IoMT) Blockchain security Federated learning AI-driven anomaly detection Cyber threat mitigation

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Frequency-noise-insensitive universal control of Kerr-cat qubits

arXiv

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arXiv 2025年

作者： Seifert, Lennart Maximilian Hann, Connor T. Noh, Kyungjoo Department of Computer Science University of Chicago ChicagoIL60637 United States AWS Center for Quantum Computing PasadenaCA91125 United States

We theoretically study the influence of frequency uncertainties on the operation of a Kerr-cat qubit. As the mean photon number increases, Kerr-cat qubits provide an increasing level of protection against phase errors induced by unknown frequency shifts during idling and X rotations. However, realizing rotations about the other principal axes (e.g., Y and Z axes) while preserving robustness is nontrivial. To address this challenge, we propose a universal set of gate schemes which circumvents the tradeoff between protection and controllability in Kerr-cat qubits and retains robustness to unknown frequency shifts to at least first order. Assuming an effective Kerr oscillator model, we theoretically and numerically analyze the robustness of elementary gates on Kerr-cat qubits, with special focus on gates along nontrivial rotation axes. An appealing application of this qubit design would include tunable superconducting platforms, where the induced protection against frequency noise would allow for a more flexible choice of operating point and thus the potential mitigation of the impact of spurious two-level systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

arXiv

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arXiv 2025年

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2025, CC BY-NC-ND.

关键词： Edge computing

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Whole genome transcriptomic profiling reveals distinct sex-specific responses to heat stroke

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Journal of Applied Physiology 2025年第4期138卷 964-978页

作者： Bouchama, Abderrezak Gomez, Maria Abdullah, Mashan L. Mahri, Saeed Al Malik, Shuja Shafi Yezli, Saber Mohammad, Sameer Lehe, Cynthia Abuyassin, Bisher Hoehndorf, Robert Experimental Medicine Department King Abdullah International Medical Research Center King Saud bin Abdulaziz University for Health Sciences Ministry of National Guard Health Affairs Riyadh Saudi Arabia Computational Bioscience Research Center (CBRC) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Biostatistics Epidemiology and Scientific Computing Department King Faisal Specialist Hospital and Research Centre Riyadh Saudi Arabia Computer Electrical and Mathematical Sciences & Engineering (CEMSE) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Heat-related mortality remains health challenges exacerbated by climate change, with sex-based differences in outcomes, yet underlying mechanisms remain poorly understood. This study examined transcriptomic responses to heat exposure in peripheral blood mononuclear cells from 19 patients with heat stroke (HS;8 males, mean age 64.8 ± 6.6 yr;11 females, mean age 49.7 ± 11 yr) and 19 controls (11 males, mean age 48.9 ± 9.6 yr;8 females, mean age 44.9 ± 11.8 yr). At admission, gene expression revealed upregulation of heat shock protein genes, and pathway analysis demonstrated activation of heat shock and unfolded protein responses across both sexes consistent with proteotoxic stress. However, distinct metabolic, oxidative stress, cell cycle control, and immune responses were observed within each sex. Females displayed inhibition of protein synthesis, oxidative phosphorylation, and metabolic pathways, including glucose metabolism, indicative of a hypometabolic state. Males maintained metabolic activity precooling and enhanced adenosine triphosphate production postcooling. Females activated nuclear factor erythroid 2-related factor 2 (NRF2)-mediated oxidative stress responses and inhibited DNA replication and mitosis, potentially mitigating genomic instability, whereas these pathways showed limited regulation in males. Females promoted innate immunity via interleukin (IL)-6, inflammasome, and triggering receptor expressed on myeloid cells 1 (TREM1) signaling, whereas males showed suppression of both innate and adaptive immunity, including IL-12, Th1, and T-cell receptor pathways. Upstream analysis identified over 100 transcription factors in both sexes. Males primarily relied on transcriptional mechanisms, whereas females also exhibited translational regulation via La ribonucleoprotein 1 (LARP1), fragile X messenger ribonucleoprotein 1 (FMR1), insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1), and eukaryotic translation initiation factor 6 (EIF6). These findin

关键词： heat stroke oxidative phosphorylation proteostasis sex-specific responses transcriptomic

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Classification of Cacao Pods Ripeness Using Convolutional Neural Networks 16

Classification of Cacao Pods Ripeness Using Convolutional Ne...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Borromeo, Karl O. Bonotan, Melbert R. Llagas, Archie P. Juan, Joana Marie O. Sanchez, Philip Donald C. Berro, Nathan Kit A. Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines Department of Information Technology College of Computing and Information Sciences Caraga State University Butuan City Philippines Center for Resource Assessment Analytics and Emerging Technologies College of Engineering and Geo-Sciences Caraga State University Butuan City Philippines Department of Information Systems College of Computing and Information Sciences Caraga State University Butuan City Philippines

This paper presents methods for the development of Ripeness Identification of Cacao Pods using Convolutional Neural Network (CNN) and precision agriculture employed in a Mobile Application. This research explores if there is a correlation between the color dynamics of cacao pods and its physiological characteristics, and the possibility of using this correlation to develop Artificial Intelligence (AI) model for ripeness and variety identification, which conventional eye estimation resulted to unripe and overripe harvesting of Cacao Pods. Data analysis and correlation was developed, integrating detection and classification models based on the Single Shot Detection (SSD) MobileNet and visual Geometry Group (VGG) 16 or VGG16 layers network architectures, respectively. The mobile application utilizes the TensorFlow template for object detection and incorporates image preprocessing techniques for enhanced accuracy. The successful integration of the model into a mobile application demonstrates its potential for practical applications in the cacao industry. © 2025 Elsevier B.V.. All rights reserved.

关键词： Cacao CNN Convolutional Neural Network Deep Learning Physiological Properties Precision Agriculture Ripeness Identification

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Approximate Tree Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees

arXiv

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arXiv 2025年

作者： Veldt, Nate Stanley, Thomas Priest, Benjamin W. Steil, Trevor Iwabuchi, Keita Jayram, T.S. Sanders, Geoffrey Department of Computer Science and Engineering Texas A&M University United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Finding a minimum spanning tree (MST) for n points in an arbitrary metric space is a fundamental primitive for hierarchical clustering and many other ML tasks, but this takes Ω(n2) time to even approximate. We introduce a framework for metric MSTs that first (1) finds a forest of disconnected components using practical heuristics, and then (2) finds a small weight set of edges to connect disjoint components of the forest into a spanning tree. We prove that optimally solving the second step still takes Ω(n2) time, but we provide a subquadratic 2.62-approximation algorithm. In the spirit of learning-augmented algorithms, we then show that if the forest found in step (1) overlaps with an optimal MST, we can approximate the original MST problem in subquadratic time, where the approximation factor depends on a measure of overlap. In practice, we find nearly optimal spanning trees for a wide range of metrics, while being orders of magnitude faster than exact algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Approximation algorithms

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