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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Rezazadeh, Reza Sedaahat, Yasser Ghodsollahee, Ismail Azizi, Farzad Laboratory Department of Computer Engineering Mashhad Iran Amirkabir University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350338195

the dependability of IIoT functionality is strongly influenced by its communication protocols. One of the most popular communication standards for the Internet of Things, whose features make it attractive in the IIoT field, is LoRaWAN. LoRaWAN can be considered a low-consumption competitor to cellular networks. This protocol has important features such as long-range coverage, low energy consumption, and a low implementation cost. Although the LoRaWAnfeatures make it a good choice for IoT applications, the existence of real-time constraints and a harsh environment in the IIoT field make it a challenge for network designers to guarantee the dependability of IIoT networks with LoRaWAN. Therefore, in this paper, to overcome this challenge, a novel method is presented for improving energy consumption efficiency, latency, and fault tolerance of the LoRaWAN protocol. The evaluation results show that the proposed method improves packet delivery and latency rate by 11% and 21% compared to standard LoRaWAn. © 2023 IEEE.

关键词： Energy utilization

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Targeted materials discovery using Bayesian algorithm execution

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npj Computational Materials 2024年第1期10卷 1619-1630页

作者： Sathya R.Chitturi Akash Ramdas Yue Wu Brian Rohr Stefano Ermon Jennifer Dionne Felipe Hda Jornada Mike Dunne Christopher Tassone Willie Neiswanger Daniel Ratner SLAC National Accelerator Laboratory Menlo ParkCA94025USA Department of Materials Science and Engineering Stanford UniversityStanfordCA94305USA Department of Computer Science Stanford UniversityStanfordCA94305USA Department of Computer Science University of Southern CaliforniaLos AngelesCA90089USA

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces.A popular strategy is Bayesian optimization,which aims to find candidates that maximize material properties;however,materials design often requires finding specific subsets of the design space which meet more complex or specialized *** present a framework that captures experimental goals through straightforward user-defined filtering *** algorithms are automatically translated into one of three intelligent,parameter-free,sequential data collection strategies(SwitchBAX,InfoBAX,and MeanBAX),bypassing the time-consuming and difficult process of task-specific acquisition function *** framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision *** demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization,and show that our methods are significantly more efficient than state-of-the-art ***,our framework provides a practical solution for navigating the complexities of materials design,and helps lay groundwork for the accelerated development of advanced materials.

关键词： synthesis straight consuming

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Self-Supervised Representation Learning for Knee Injury Diagnosis From Magnetic Resonance Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1613-1623页

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India Indian Institute of Technology Kharagpur Department of Electronics and Electrical Communication Engineering Kharagpur721302 India

In medical image analysis, the cost of acquiring high-quality data and annotation by experts is a barrier in many medical applications. Most of the techniques used are based on a supervised learning framework and require a large amount of annotated data to achieve satisfactory performance. As an alternative, in this article, we propose a self-supervised learning approach for learning the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful context-invariant spatial representations. The downstream task in our article is a class-imbalanced multilabel classification. Different experiments show that the features learned by the pretext model provide competitive performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy toward imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in imbalanced multilabel classification tasks on MR scans. © 2020 IEEE.

关键词： Supervised learning

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Context Unaware Knowledge Distillation for Image Retrieval 1

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International Conference on computer vision and Machine Intelligence, CVMI 2022

作者： Reddy, Bytasandram Yaswanth Dubey, Shiv Ram Sanodiya, Rakesh Kumar Karn, Ravi Ranjan Prasad Department of Computer Science and Engineering Indian Institute of Information Technology Sri City Chittoor India Computer Vision and Biometrics Laboratory Indian Institute of Information Technology Allahabad India

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

Existing data-dependent hashing methods use large backbone networks with millions of parameters and are computationally complex. Existing knowledge distillation methods use logits and other features of the deep (teacher) model and as knowledge for the compact (student) model, which requires the teacher’s network to be fine-tuned on the context in parallel with the student model on the context. Training teacher on the target context requires more time and computational resources. In this paper, we propose context unaware knowledge distillation that uses the knowledge of the teacher model without fine-tuning it on the target context. We also propose a new efficient student model architecture for knowledge distillation. The proposed approach follows a two-step process. The first step involves pre-training the student model with the help of context unaware knowledge distillation from the teacher model. The second step involves fine-tuning the student model on the context of image retrieval. In order to show the efficacy of the proposed approach, we compare the retrieval results, no. of parameters, and no. of operations of the student models with the teacher models under different retrieval frameworks, including deep cauchy hashing (DCH) and central similarity quantization (CSQ). The experimental results confirm that the proposed approach provides a promising trade-off between the retrieval results and efficiency. The code used in this paper is released publicly at https://***/satoru2001/CUKDFIR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Distillation

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A model to measure the spread power of rumors

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13787-13811页

作者： Jahanbakhsh-Nagadeh, Zoleikha Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Akan, Taymaz Asgari-Chenaghlu, Meysam Nikzad-Khasmakhi, Narjes Feizi-Derakhshi, Ali-Reza Ranjbar-Khadivi, Mehrdad Zafarani-Moattar, Elnaz Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering University of Tabriz Tabriz Iran

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cell proliferation

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Granular classifier:Building traffic granules for encrypted traffic classification based on granular computing

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Digital Communications and Networks 2024年第5期10卷 1428-1438页

作者： Xuyang Jing Jingjing Zhao Zheng Yan Witold Pedrycz Xian Li State Key Laboratory on Integrated Services Networks School of Cyber EngineeringXidian UniversityChina Department of Electrical and Computer Engineering University of AlbertaCanada Institute for Future(IFF) Qingdao UniversityQingdaoChina

Accurate classification of encrypted traffic plays an important role in network ***,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability to classify traffic with multi-level features,and degradation due to limited training traffic *** address these problems,this paper proposes a traffic granularity-based cryptographic traffic classification method,called Granular Classifier(GC).In this paper,a novel Cardinality-based Constrained Fuzzy C-Means(CCFCM)clustering algorithm is proposed to address the problem caused by limited training traffic,considering the ratio of cardinality that must be linked between flows to achieve good traffic ***,an original representation format of traffic is presented based on granular computing,named Traffic Granules(TG),to accurately describe traffic structure by catching the dispersion of different traffic *** granule is a compact set of similar data with a refined boundary by excluding *** on TG,GC is constructed to perform traffic classification based on multi-level *** performance of the GC is evaluated based on real-world encrypted network traffic *** results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.

关键词： Encrypted traffic classification Semi-supervised clustering Granular computing Anomaly detection

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New Results in Cooperative Adaptive Optimal Output Regulation

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Journal of Systems Science & Complexity 2024年第1期37卷 253-272页

作者： DONG Yuchen GAO Weinan JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering Tandon School of EngineeringNew York UniversityBrooklynNY 11201USA

This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent *** the multi-agent system dynamics are uncertain,solving regulator equations and the corresponding algebraic Riccati equations is challenging,especially for high-order *** this paper,a novel method is proposed to approximate the solution of regulator equations,i.e.,gradient descent *** is worth noting that this method obtains gradients through online data rather than model information.A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming,so that each follower can achieve asymptotic tracking and disturbance ***,the effectiveness of the proposed control method is validated by simulations.

关键词： Adaptive dynamic programming cooperative output regulation gradient descent method multi-agent systems.

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Preliminary Results on the Impulse Breakdown Characteristics of Natural Ester Oil and Oil-Based Nanofluid with Fe2O3 Nanoparticles 24

Preliminary Results on the Impulse Breakdown Characteristics...

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24th EEEIC International Conference on Environment and Electrical engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Hadjicostas, Alexandros Y. Staikos, Evangelos T. Peppas, George D. Datsios, Zacharias G. Litsardakis, George Pyrgioti, Eleftheria C. Gonos, Ioannis F. Tsovilis, Thomas E. School of Electrical & Computer Engineering Aristotle University of Thessaloniki High Voltage Laboratory Greece School of Electrical & Computer Engineering Technical University of Crete Chania Greece School of Electrical & Computer Engineering Aristotle University of Thessaloniki Laboratory of Materials for Electrotechnics Greece University of Patras High Voltage Laboratory Electrical & Computer Engineering Department Greece

ISBN: (纸本)9798350355185

This study presents an experimental investigation of the impulse breakdown characteristics of natural ester oil, in its pure form, and as the base liquid for a nanofluid with Fe203 nanoparticles (0.050% w/w). A two-stage impulse voltage generator and a combination wave generator have been used to determine the distribution of the breakdown voltage and time to breakdown;a standard test-cell per IEC 60897 and a custom-made metal fixture were employed for that purpose. Experimental results are discussed with emphasis given to the response time of the iron oxide nanofluid when compared to the base liquid. © 2024 IEEE.

关键词： Nanoclay

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Subnetwork-based federated few-shot semantic segmentation of organ images

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Optoelectronics Letters 2025年

作者： Wu Junpeng Zhao Meng Zhang Huanping Key Laboratory of Computer Vision and System of Ministry of Education， School of Computer Science and Engineering， Tianjin University of Technology

Federated learning(FL)， as a distributed learning paradigm， allows multiple medical institutions to collaborate on learning without the need to centralize all client data. However， existing methods pay little attention to more challenging medical image semantic segmentation tasks， especially in the scenario of the imbalanced dataset in federated few-shot learning. In this paper， we propose a subnetwork-based federated few-shot organ image segmentation method. Firstly， individual clients train using local training samples and then upload local model gradients to the server. The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters. Through this method， we can learn the similarities between different clients to address data heterogeneity issues. In addition， to enhance the communication efficiency between clients and the server， we have also designed a personalized layer aggregation strategy， which only transmits partial layer model parameters during the communication process to improve communication efficiency. Finally， we conducted experiments on ABD-MRI and ABD-CT datasets to demonstrate the effectiveness of our method.

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