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AR-CNN: an attention ranking network for learning urban perception

Science China(information Sciences) 2022年第1期65卷 164-174页

作者： Zhetao LI Ziwen CHEN Wei-Shi ZHENG Sangyoon OH Kien NGUYEN Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Data and Computer Science Sun Yat-sen University Department of Computer and Information Engineering Ajou University Graduate School of Engineering Chiba University

An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.

关键词： ranking network urban perception attribute learning attention network colour and texture

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DLTree: efficient and accurate phylogeny reconstruction using the dynamical language method

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Bioinformatics (Oxford, England) 2017年第14期33卷 2214-2215页

作者： Qi Wu Zu-Guo Yu Jianyi Yang Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan China. School of Mathematical Sciences Nankai University Tianjin China.

SUMMARY:A number of alignment-free methods have been proposed for phylogeny reconstruction over the past two decades. But there are some long-standing challenges in these methods, including requirement of huge computer memory and CPU time, and existence of duplicate computations. In this article, we address these challenges with the idea of compressed vector, fingerprint and scalable memory management. With these ideas we developed the DLTree algorithm for efficient implementation of the dynamical language model and whole genome-based phylogenetic analysis. The DLTree algorithm was compared with other alignment-free tools, demonstrating that it is more efficient and accurate for phylogeny reconstruction. AVAILABILITY AND IMPLEMENTATION:The DLTree algorithm is freely available at http://***. CONTACT:yuzuguo@*** or yangjy@***. SUPPLEMENTARY information:Supplementary data are available at Bioinformatics online.

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Efficient differentiated storage architecture for large-scale flow tables in openflow networks 21

Efficient differentiated storage architecture for large-scal...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xiong, Bing Wu, Rengeng Liao, Zhuofan Wang, Jin School of Computer AND Communication Engineering Changsha University of Science AND Technology Changsha410114 China Department of Computer and Information Sciences Temple University Philadelphia19122 United States Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science AND Technology Changsha China

ISBN: (纸本)9781728120584

As a novel network paradigm, Software Defined Networking (SDN) decouples control logic functions from data forwarding devices, and introduces a separate control plane to manipulate underlying switches via southbound interfaces like OpenFlow. However, it leads to large-scale flow tables and poses serious challenges on their storage resources and lookup performance in OpenFlow switches. This paper is thus motivated to propose an efficient differentiated storage architecture for large-scale flow tables in OpenFlow networks. Firstly, we investigate into the impact of wildcards in match fields on packet-in-batch property within a flow based on network traffic locality. Then, packet flows are dynamically distinguished into active ones and idle ones in terms of their short-term states. Subsequently, we store the match fields of active flows and idle flows respectively in TCAM and SRAM, and the content fields of both types of flows in DRAM, to effectively relieve the insufficiency of TCAM capacity. Finally, we evaluate the performance of our proposed flow table storage architecture with real network traffic traces by experiments. The experimental results indicate that our proposed storage architecture obviously outperforms the traditional one applying the elephant/mice flow differentiation method in terms of TCAM hit rates and average flow table access time. © 2019 IEEE.

关键词： Software defined networking

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Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields

Open-vocabulary 3D Semantic Understanding via Affinity Neura...

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Next Generation Data-driven Networks (NGDN), International Conference on

作者： Yujie Fan Huan Luo Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes.

关键词： Training Image segmentation Vocabulary Visualization Three-dimensional displays Semantics Natural languages Neural radiance field Feature extraction Robustness

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Yangding Li Shaobin Fu Hao Feng Yangyang Zeng Jinghao Wang Zhihao Jiang Lvyun Zhang School of Information Science and Engineering Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China School of Big Data and Computer Science Hechi University Guangxi China

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive.

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Double Time Slot RFID Anti-collision Algorithm based on Gray Code

Double Time Slot RFID Anti-collision Algorithm based on Gray...

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ISCC 2017-the 5th International Conference on information Science and Cloud computing

作者： Hongwei Deng Lang Li Hui Wang School of Computer Science and Technology Hengyang Normal University School of Information Science and Engineering Central South University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

To solve the radio frequency identification problem,an anti-collision algorithm based on the Gray code(BSGC) was developed. The proposed algorithm reduces the effort required to search for tag prefixes in accordance with Gray code regulations and enhances the identification speed through efficient division and branching of the collision tags. The results of simulation and subsequent analysis of the proposed algorithm confirm that it effectively reduces the number of collisions and transmission delays,enhances the throughput rate,and increases the tag identification efficiency.

关键词： tag Double Time Slot RFID Anti-collision Algorithm based on Gray Code

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Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

arXiv

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arXiv 2024年

作者： Dong, Li Peng, Yubo Jiang, Feibo Wang, Kezhi Yang, Kun School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Intelligent Software and Engineering Nanjing University Suzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Department of Computer Science Brunel University London United Kingdom

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Automatic annotation of visual deep neural networks

arXiv

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arXiv 2021年

作者： Li, Ming Guo, ChenHao College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China

Computer vision is widely used in the fields of driverless, face recognition and 3D reconstruction as a technology to help or replace human eye perception images or multidimensional data through computers. Nowadays, with the development and application of deep neural networks, the models of deep neural networks proposed for computer vision are becoming more and more abundant, and developers will use the already trained models on the way to solve problems, and need to consult the relevant documents to understand the use of the model. The class model, which creates the need to quickly and accurately find the relevant models that you need. The automatic annotation method of visual depth neural network proposed in this paper is based on natural language processing technology such as semantic analysis, which realizes automatic labeling of model application fields. In the three top international conferences on computer vision: ICCV, CVPR and ECCV, the average correct rate of application of the papers of 72 papers reached 90%, indicating the effectiveness of the automatic labeling system. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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A Reconstruction Method for Cross-Cut Shredded Documents Based on the Extreme Learning Machine Algorithm

Research Square

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Research Square 2022年

作者： Zhang, Zhenghui Zou, Juan Zheng, Jinhua Yang, Shengxiang Gong, Dunwei Pei, Tingrui Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Information Engineering College of Xiangtan University Hunan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Information and Electronic Engineering China University of Mining and Technology Xuzhou221116 China

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm’s design is to exploit all available information and computationally suggest matches to increase the algorithm’s ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios. © 2022, CC BY.

关键词： Knowledge acquisition

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Feature Selection Based on Consistent Granulation

SSRN

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SSRN 2024年

作者： Yang, Tian Shen, Shuo Deng, Jinsheng Liang, Jie Qian, Yuhua Dai, Jianhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Institute of Big Data Science and Industry Shanxi university Shanxi Taiyuan030006 China

Classifying large-scale data is a challenging task in machine learning. Feature selection and feature construction can improve the classification performances of classifiers. However, existing feature selections struggle to handle large-scale data. Granulation is the key process for generating accurate and efficient models. In this paper, we propose a novel granulation approach based on rough sets, consistent granulation, and a novel feature construction based on consistent granulation to improve the computation speed, scale, and classification performance of feature selection. As a result, the time and space complexities are reduced to a linear level in terms of sample scales. Moreover, consistent granulation can precisely represent the topological structure of the data and reduce the parameter space from [0, 1] to a finite set lowering the difficulty of optimal parameter searching. Experiments show that the proposed algorithm, FCG, outperforms four classical feature selection algorithms in handling datasets with millions of samples or tens of thousands of dimensions. Additionally, on the first thirteen datasets, FCG reduces the average runtime by 1145.68 times (NARNeMF), 883.11 times (HANDI), 1333.10 times (GBNRS), and 3.73 times (RMDPS), while increasing the average classification accuracy by 2.73% (FARNeMF), 2.30% (HANDI), 4.61% (GBNRS) and 14.33% (RMDPS) compared to these methods. © 2024, The Authors. All rights reserved.

关键词： Granulation

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