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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Kuvshinnikov, Vladimir Kovshov, Evgeny JSC 'Research and Development Institute of Construction Technology - Atomstroy' Research Laboratory of Digital Computer Systems and Automation Moscow Russia

ISBN: (纸本)9798331511241

The implementation of digital industrial technologies marks a pivotal step towards maximizing automation in production processes, aligning with the principles of Industry 4.0 and the NDE 4.0 paradigms. One key area of focus is the modeling of radiation non-destructive evaluation operations within a virtual reality framework. This innovative approach allows for the creation and software application of simulators specifically designed for non-destructive testing of various materials and products. In this context, a complex multi-component digital twin of radiographic image is proposed. The digital twin serves as a comprehensive virtual model that mirrors the characteristics and behaviors of its physical counterpart. The images are generated using discrete modeling techniques that utilize both fundamental physical principles and empirical data provided by manufacturers. This dual approach ensures that the resulting radiographic images closely resemble those produced during actual X-ray inspections. One of the spatial factors that affects image sharpness and quality of inspection is also included in the model. By utilizing virtual reality and digital twin technologies, radiation non-destructive testing training becomes more efficient and better aligned with the automation goals of contemporary industrial practices. © 2025 IEEE.

关键词： Software testing

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A Survey on Time Series Data Classification: Blockchain Technologies and Security Concerns 28th

A Survey on Time Series Data Classification: Blockchain Tech...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Alsayyad, Ahmed Faris Abid Ali, Alaa Abid Muslam Mabrouk, Mohamed Al-Shammari, Ahmed Zrigui, Mounir Research Laboratory in Algebra Numbers Theory and Intelligent Systems RLANTIS University of Monastir Monastir Tunisia Department of Computer Science College of Computer Science and Information Technology University of Al-Qadisiyah Al Diwaniyah58002 Iraq Al-Qadisiyah University Al-Qadisiyah Iraq Military Academy Fondok Jedid Tunisia

The difficulties about user security and privacy have appeared as significant concerns in recent years. The number of cyber-attacks grows at a concerning velocity, hence rendering internet users susceptible to malicious activities perpetrated by hackers. Data mining approaches are employed to extract accurate results from massive and complex databases. Furthermore, the utilization of Blockchain (BC) approaches is increasingly popular in current Internet of Things (IoT) applications as an opportunity to address issues related to privacy and security. Lots of studies have been performed on algorithms for data mining and techniques concerning blockchain. Time series data is a commonly used form of data. Time Series Classification (TSC) refers to the creation of predictive models that generate a target variable or label based on linear or sequential data inputs across a considerable duration. The possible results may be presented in either ordinal or numerical form. Even so, previous studies have shown major limitations when it comes to handling privacy and security issues that can't be applicable in dynamic instances, as well as the substantial computational cost necessary. Moreover, correctly determining the amount of sensitive parameters required to complete the classification process remains a challenge. We have put forth a comprehensive survey on the classification of blockchain data. In the first phase of our study, we conducted an analysis and categorization of both conventional data classification approaches and contemporary time series data classification techniques. We further discussed limitations and strengths of existing techniques. Finally, we highlight future research problems and directions. © 2024 The Authors.

关键词： Differential privacy

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Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes

Neural 3D Strokes: Creating Stylized 3D Scenes with Vectoriz...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao-Bin Duan Miao Wang Yan-Xun Li Yong-Liang Yang State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Zhongguanchun Laboratory Department of Computer Science University of Bath

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present Neural 3D Strokes, a novel technique to gen-erate stylized images of a 3D scene at arbitrary novel views from multi-view 2D images. Different from existing methods which apply stylization to trained neural radiance fields at the voxel level, our approach draws inspiration from image-to-painting methods, simulating the progressive painting process of human artwork with vector strokes. We develop a palette of stylized 3D strokes from basic primitives and splines, and consider the 3D scene stylization task as a multi-view reconstruction process based on these 3D stroke primitives. Instead of directly searching for the parame-ters of these 3D strokes, which would be too costly, we introduce a differentiable renderer that allows optimizing stroke parameters using gradient descent, and propose a training scheme to alleviate the vanishing gradient issue. The extensive evaluation demonstrates that our approach effectively synthesizes 3D scenes with significant geomet-ric and aesthetic stylization while maintaining a consis-tent appearance across different views. Our method can be further integrated with style loss and image-text con-trastive models to extend its applications, including color transfer and text-driven 3D scene drawing. Results and code are available at http://***/Neura13DStrokes.

关键词： Training Solid modeling Three-dimensional displays Stroke (medical condition) Neural radiance field Vectors Pattern recognition

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NeSyKHG: Neuro-Symbolic Knowledge Hypergraphs 2

NeSyKHG: Neuro-Symbolic Knowledge Hypergraphs

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Bhuyan, Bikram Pratim Singh, Thipendra P. Tomar, Ravi Ramdane-Cherif, Amar LISV Laboratory University of Paris Saclay 10-12 Avenue of Europe Velizy78140 France School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India School of Computer Science Engineering & Technology Bennett University Greater Noida201306 India Persistent Systems Pune411016 India

In the rapidly evolving landscape of artificial intelligence, there is an escalating demand for enhanced reasoning capabilities and robust representation of higher-order relationships. Despite their prowess in pattern identification and generalization, Neural networks often grapple with challenges linked to interpretability and the absence of explicit relational reasoning. Symbolic logic, on the other hand, excels in formal knowledge representation and inference yet remains limited in deriving knowledge directly from data. We introduce the Neuro-Symbolic Knowledge Hypergraphs (NeSyKHG) framework to bridge this gap. This innovative model synergistically merges the strengths of neural networks and symbolic logic, offering an optimized representation and reasoning system for higher-order relationships using knowledge hypergraphs. Our empirical evaluations on the Chinese Medical High-order Relational (CMHR) dataset revealed that NeSyKHG significantly outperforms several established baseline models. Specifically, when compared to models such as Hypergraph Neural Networks (HGNN), Hypergraph Convolutional Networks (HyperGCN), Logical Hypergraph Link Prediction (LHP), and Universal Graph Convolutional Networks (UniGCN), NeSyKHG achieved an F1-score of 0.845, an accuracy (ACC) of 0.947, and an area under the curve (AUC) of 0.965. These metrics underscore NeSyKHG's exceptional ability in higher-order relational learning and reasoning. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer circuits

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Drone-Enabled Connectivity: Advancements and Challenges in B5G / 6G Networks 8

Drone-Enabled Connectivity: Advancements and Challenges in B...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Telli, Khaled Kraa, Okba Himeur, Yassine Boumehraz, Mohamed Ouamane, Abdelmalik Benbraika, Kamel Atalla, Shadi Mansoor, Wathiq Mohamed Khider University Energy-Systems-Modelling Laboratory Biskra Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates University of Biskra LI3C Laboratory Biskra Algeria University of El-Oued Department of Computer Science El-Oued Algeria

ISBN: (纸本)9798350309249

The rapid advancement of networking and broad-band communications has created a demand for efficient relaying methods, particularly in the context of high-fidelity and low-latency last-mile communications. This paper explores the utilization of cellular-enabled-drones as Aerial-Base-Station (Aerial-BS) in cellular networks next-generation, specifically focusing on their potential as flying base stations in Beyond 5G/6G networks. The study summarises various aspects, including energy availability, mobility, optimal positioning, and node localization, handover efficiency. Additionally, multi-objective optimizations for bandwidth allocation, accommodating highly mobile User Equipment (UE), mitigating interference, and ensuring quality of service (QoS) are discussed. The paper highlights the use of drone-base-stations (DBS) as extending terrestrial network capacity and coverage, assisting mobile ad-hoc-networks, and enabling beam-forming applications. The challenges associated with optimal positioning, energy availability, path planning mobility, security, and QoS are examined, providing insights into future research directions. The findings contribute to the enhancement of cellular connectivity and offer valuable insights into the utilization of drones as flying aerial base stations in B5G / 6G network. © 2024 IEEE.

关键词： Drones

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Automated Detection of Tuberculosis Bacilli Using Deep Neural Networks with Sputum Smear Images 5

Automated Detection of Tuberculosis Bacilli Using Deep Neura...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： An, Le Peng, Kexin Yang, Xing Feng, Peng Huang, Pan Chengdu University of Technology College of Computer Science and Cyber Security Chengdu China Chongqing University The Key Laboratory of Optoelectronic Technology and Systems Ministry of Education Chongqing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Tuberculosis (TB) is a serious contagious disease caused by a bacterium called Mycobacterium tuberculosis (M. tuberculosis). Microscopic examination remains the primary laboratory diagnostic method for early detection of TB, but this method is time-consuming and inefficient due to the manual operation. To accelerate the process, automatic detection models are proposed. In this work, we have detected M. tuberculosis reliably from the sputum smear images using an improved YOLOv5 (DA-YOLO) algorithm. The backbone of YOLOv5 was modified by applying the coordinate attention block and self-attention block: the former encodes both channel relationships and long-range dependencies with precise positional information;the latter (transformer module) can fully rely on the self-attention mechanism to compute feature representations. More importantly, a DA-YOLO is proposed to achieve end-to-end detection and enumeration of Mycobacterium tuberculosis. The combination of the two attention mechanisms enables the backbone network to get rich information and robust features. A proposed model DAYOLO gives an overall accuracy of 87.6%. Experimental results show that our algorithm outperforms state-of-the-art methods, which will help computer-aided tuberculosis diagnosis faster and more accurately. © 2022 IEEE.

关键词： Deep neural networks

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Improving energy consumption forecasting using DLnet based on periodic modeling

Improving energy consumption forecasting using DLnet based o...

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2022 International Conference on Networks, Communications and Information technology, NCIT 2022

作者： Liao, XueChao Huang, Xiang School of Computer Science and Technology Wuhan University of Science and Technology Hubei China Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Hubei China

ISBN: (纸本)9783800760190

Improving the prediction accuracy of energy consumption in office buildings is necessary to achieve high energy efficiency in smart buildings. The existing forecasting methods rarely analyze the periodic characteristics of energy consumption independently. In this paper, a short-term office building energy consumption prediction model (DLnet) is proposed to address the problem of inefficiency in the utilization of periodic energy consumption ***, the period component of the energy consumption data is decomposed using STL, and the optimal period of the energy consumption data is searched for by a grid search algorithm, and then the Periodic block is constructed based on the optimal period;Secondly, the Time-series block data is constructed according to the data shape of the Periodic block;then the Time-series block data and the Periodic block data are trained and learned using LSTM;Finally, the prediction results of the Time-series block data and the Periodic block data are fused by linear *** four prediction accuracy indicators of the proposed model have been demonstrated to be 7%, 21%, 25%,and 26% higher than those of the LSTM model. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Office buildings

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MD-TransUNet: TransUNet with Multi-attention and Dilated Convolution for Brain Stroke Lesion Segmentation 19th

MD-TransUNet: TransUNet with Multi-attention and Dilated C...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xu, Jie Wan, Jian Zhang, Xin School of Computer Science and Technology Hangzhou Dianzi University Zhejiang310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Ministry of Education Beijing China School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China Key Laboratory of Marine Ecosystem Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China

ISBN: (纸本)9783031545276

The accurate segmentation of stroke lesion regions holds immense significance in shaping treatment strategies and rehabilitation protocols. Due to the large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke lesions, whose framework is based on the UNet architecture. To fully obtain deep image features, it uses ResNet50 for downsampling. MD (multi-dilated) module is employed as the skip connection to gain more receptive fields. Different receptive fields can adapt to varying volumes of lesion areas. Then, a feature extraction module with multi-level attention mechanism is designed using ConvLSTM, non-local spatial attention, and channel attention modules to suppress useless information expression in skip connections and upsampling processes while focusing more on effective spatial and channel information in features. The experiments show that our proposed network gets superior performance than benchmark methods and indicates the generalization and effectiveness of the proposed model. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Convolution

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Federated Graph Learning for Cross-Domain Recommendation 38

Federated Graph Learning for Cross-Domain Recommendation

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. We open source the code at https://***/LafinHana/FedGCDR. © 2024 Neural information processing systems foundation. All rights reserved.

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