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International Conference on Data engineering

作者： Zhuochen Fan Jiarui Guo Xiaodong Li Tong Yang Yikai Zhao Yuhan Wu Bin Cui Yanwei Xu Steve Uhlig Gong Zhang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Theory Lab Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Hong Kong SAR China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate" the two machine learning models through end-to-end experiments. We have released our source code at GitHub.

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Robustface Recognition by Fusing Fuzzy Type 2 Induced Multiple Facial Fused Image 4

Robustface Recognition by Fusing Fuzzy Type 2 Induced Multip...

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4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Ghosh, Manas Dey, Aniruddha Shaw, Rabindra Nath Ghosh, Ankush RCCIIT Department of Master of Computer Application Kolkata India MAKAUT Department of Information Technology Kolkata India Galgotias University Department of EC and Communication Engineering India School of Engineering and Applied Science Neotia University Kolkata India

ISBN: (纸本)9781728199511

This paper projects a new decision making model for face recognition from the original image fused with their true and partial diagonal images by integrating the type-2 fuzzy set based approach to mitigate the factors that pretend the face recognition accuracy. The G2DFLD based feature vectors corresponding to a test image are given input to neural network based classifier trained with the feature vectors of the fused images to generate the merit weights with respect to different classes (subjects) under consideration. A new scheme has been introduced in the present approach to generate a score by employing a fuzzy type-2 set based treatment. These scores with respect to each of the classes under consideration are rendered from the feature vectors of the test image and those of the diagonally fused training samples. For each class, the score is fused weighted by the corresponding merit weights to generate the concluding score. These class-wise concluding scores are deliberated in recognizing the test face image. Faces from the well-known databases (gallery) with varied pose, illumination and occlusion are used to evaluate the performance of the model. It has been found that our model exhibits more accurate classification performance than existing similar kind of image level fusion method. © 2021 IEEE.

关键词： Face recognition

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DETR-DC5 Approaches for Improved Spatial Object Detection in Satellite Imagery

DETR-DC5 Approaches for Improved Spatial Object Detection in...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Radhika Baskar Rajat Kumar Dwibedi Makhan Kumbhkar Swati Sah Harshal Patil Firos A Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Aarupadai Veedu Institute of Technology VMRF Chennai India Department of Computer Application Indian Institute of Soybean Research Indore India School of SET Sharda university Greater Noida India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of computer Science and Engineering Rajiv Gandhi University (A Central University) Rono-Hills Doimukh Arunachal Pradesh India

Satellite spatial object detection is crucial for urban planning, environmental monitoring, and catastrophe management. The DETR-DC5 (Data Efficient Transformer with DC5 Backbone) is a cutting-edge object detection model. This study introduces DETR-DC5 framework-based satellite spatial object detection improvements. DETR-DC5's strengths are used to improve feature extraction and post-processing. First, a specific feature augmentation technique extracts richer and more discriminative spatial features to let the model capture subtle object attributes. Second, a semi-supervised learning training technique is used to address the lack of labeled satellite imagery data and improve the model's generalization. A new satellite imagery post-processing refining method uses non-maximum suppression methods. This refinement stage reduces false positives and improves localization, especially in densely populated metropolitan regions and complicated natural surroundings. On benchmark satellite imagery datasets, the suggested methods outperform baseline DETR-DC5 models in spatial object detection. Experimental findings indicate significant improvements in precision, recall, and F1-score, proving the upgrades work. This study uses the DETR-DC5 architecture, targeted feature augmentation, semi-supervised training, and refined post-processing algorithms to recognize spatial objects in satellite data. These techniques have the potential to dramatically improve satellite-based object detection, which has implications for urban planning, environmental monitoring, and disaster response.

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UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner

arXiv

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arXiv 2023年

作者： Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The University of Adelaide Australia DAMO Academy Alibaba Group China

The universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM). One popular strategy to build universal models is to encode each task as a one-hot vector and generate dynamic convolutional layers at the end of the decoder to extract the interested target. Although successful, it ignores the correlations among tasks and meanwhile is too late to make the model ‘aware’ of the ongoing task. To address both issues, we propose a prompt-driven Universal Segmentation model (UniSeg) for multi-task medical image segmentation using diverse modalities and domains. We first devise a learnable universal prompt to describe the correlations among all tasks and then convert this prompt and image features into a task-specific prompt, which is fed to the decoder as a part of its input. Thus, we make the model ‘aware’ of the ongoing task early and boost the task-specific training of the whole decoder. Our results indicate that the proposed UniSeg outperforms other universal models and single-task models on 11 upstream tasks. Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation. Code and model are available at https://***/yeerwen/UniSeg. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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A range and radial velocity measurement method based on up-chirp and down-chirp LFM signal in pulsed radar

A range and radial velocity measurement method based on up-c...

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IET International Radar Conference (IRC 2023)

作者： Hao Chen Yi Lei Key Lab of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei People's Republic of China School of Computer Science and Information Engineering Hefei University of Technology Hefei People's Republic of China

ISBN: (数字)9781837240982

Range accuracy and radial velocity accuracy are important indexes in pulsed radar. However, in pulsed radar using linear frequency modulation (LFM) pulse, the range-Doppler coupling of LFM signal will lead to large range error in high-speed target measurement. The target's radial velocity is computed using range differentiation in traditional method, which is also not accurate. This paper presents a measurement method based on up-chirp and down-chirp LFM signal to get the target's accurate range and radial velocity simultaneously. Experiments confirm that the proposed method outperforms traditional method in both range accuracy and radial velocity accuracy.

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MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer

arXiv

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arXiv 2024年

作者： Chen, Geng Yang, Junqing Pu, Xiaozhou Ji, Ge-Peng Xiong, Huan Pan, Yongsheng Cui, Hengfei Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Computing Australian National University Canberra Australia Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Harbin Institute of Technology China

Accurate segmentation of polyps from colonoscopy videos is of great significance to polyp treatment and early prevention of colorectal cancer. However, it is challenging due to the difficulties associated with modelling long-range spatio-temporal relationships within a colonoscopy video. In this paper, we address this challenging task with a novel Mixture-Attention Siamese Transformer (MAST), which explicitly models the long-range spatio-temporal relationships with a mixture-attention mechanism for accurate polyp segmentation. Specifically, we first construct a Siamese transformer architecture to jointly encode paired video frames for their feature representations. We then design a mixture-attention module to exploit the intra-frame and inter-frame correlations, enhancing the features with rich spatio-temporal relationships. Finally, the enhanced features are fed to two parallel decoders for predicting the segmentation maps. To the best of our knowledge, our MAST is the first transformer model dedicated to video polyp segmentation. Extensive experiments on the large-scale SUN-SEG benchmark demonstrate the superior performance of MAST in comparison with the cutting-edge competitors. Our code is publicly available at https://***/Junqing-Yang/MAST. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

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computers, Materials & Continua 2020年第9期64卷 1959-1975页

作者： Dunhong Yao Shijun Li Ang Li Yu Chen School of Computer Science and Engineering Huaihua UniversityHuaihua418000China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China School of Computer Wuhan UniversityWuhan430072China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA School of Computer Science and Engineering Yulin Normal UniversityYulin537000China

There is no reasonable scientific basis for selecting the excellent teachers of the school’s *** solve the practical problem,we firstly give a series of normalization models for defining the key attributes of teachers’professional foundation,course difficulty coefficient,and comprehensive evaluation of ***,we define a partial weight function to calculate the key attributes,and obtain the partial recommendation ***,we construct a highly sparse Teaching Recommendation Factorization Machines(TRFMs)model,which takes the 5-tuples relation including teacher,course,teachers’professional foundation,course difficulty,teaching evaluation as the feature vector,and take partial recommendation value as the recommendation ***,we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse *** results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching *** recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse *** proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools,which can effectively improve the teaching quality.

关键词： Highly sparse dataset normalized models teaching recommendation factorization machines excellent teacher recommendation

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The Evolution and Integration of Seamlessly Synchronized Data Streams in Modern Businesses

The Evolution and Integration of Seamlessly Synchronized Dat...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Kalyan Acharjya Sunil Sharma G. Ezhilarasan Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronics Engineering JAIN (Deemed-to-be University) Bangalore Karnataka India

In today's age of data-driven decision making, it is crucial for organizations to keep all of their data streams in perfect sync. Research methods and findings from an investigation into “The Evolution and Integration of Seamlessly Synchronized Data Streams in Modern Businesses” Data analytics, principal component analysis, k-means clustering, and autoregressive integrated moving average (ARIMA) are all included into the suggested technique. This all-encompassing method permits a thorough investigation into the development and incorporation of data synchronization in the present-day corporate environment. The results analysis demonstrates that the suggested approach outperforms the conventional ones. We compared the suggested strategy to six established alternatives. Data analysis, extensive analysis, mathematical modeling, practical insights, scalability, and overall performance are just a few of the areas where the suggested technique excels above conventional approaches. These distinctions are highlighted, and the data are represented robustly by four various forms of visualizations: scatter plots, pie charts, box plots, and heatmaps. The importance of synchronized data streams in contemporary enterprises is highlighted by our research, which highlights the need for a multidimensional and data-driven approach to this topic. The findings of this study may help businesses enhance customer service, save costs, and streamline operations by enhancing their data synchronization techniques.

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APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting 4th

APADGCN: Adaptive Partial Attention Diffusion Graph Convolut...

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4th International Conference on Spatial Data and Intelligence, SpatialDI 2023

作者： Zhang, Bowen Li, Bohan Wei, Jinzhan Wen, Hao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin541010 China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9783031329098

Traffic flow forecasting is a core task of urban governance and plays a vital role in the development of ITS. Because of the complexity and uncertainty of traffic patterns, it is of great challenge to capture spatial-temporal correlations. Recent researches mainly focus on the pre-defined adjacency matrix based on prior knowledge as the basis of spatial-temporal correlation modeling, but the fixed graph structure cannot adequately describe the dependency between traffic sensors. To tackle this issue, a novel deep learning model framework is proposed in this paper: Adaptive Partial Attention Diffusion Graph Convolutional Network(APADGCN), which consists of three main parts: 1) the Multi-Component module that divides the historical traffic flow into recent, daily-periodic, and weekly-periodic, to capture the traffic patterns of different periodic;2) the spatial correlation modeling which can dynamically capture node relationships and model spatial dependency, and enhance the aggregation ability of low-order information;3) the temporal correlation modeling which models long-term time dependencies using convolution and gating. The final result is obtained by the weighted fusion of the results of the multi-components. We compared our APADGCN with various baseline models in the four real datasets from the Caltrans Performance Measurement System (PeMS). The experimental results show that the prediction accuracy of APADGCN is better than that of the baseline models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

arXiv

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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