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arXiv 2023年

作者： Zhang, Pengcheng Zheng, Jin Bai, Xiao Ning, Xin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for persons earch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Towards Fully Decoupled End-to-End Person Search

Towards Fully Decoupled End-to-End Person Search

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Pengcheng Zhang Xiao Bai Jin Zheng Xin Ning School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for personsearch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search.

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification

Image Based Feature Separation Using RBM Tech with ADBN Tech...

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： V Revathi Balasubramanian Prabhu Kavin Arunadevi Thirumalraj E. Gangadevi Balamurugan Balusamy Shilpa Gite Department of Applied Sciences - Physics New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India Department of Computer Science Engineering K.Ramakrishnan College of Technology Trichy Tamil Nadu Department of Computer Science Loyola College Chennai Delhi-National Capital Region (NCR) Shiv Nadar University AIML department Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person’s diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN’s superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme’s relatively strong computational mo

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Precise Prediction of Cardiovascular Diseases Using Machine Learning

Precise Prediction of Cardiovascular Diseases Using Machine ...

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Smart Technologies and Systems for Next Generation computing (ICSTSN), International Conference on

作者： G. Revathy S. Venkateswaran V. Senthil Murugan V. Devi A. Mohanadevi G. Saravanan School of Computing SASTRA DEEMED University Thanjavur Department of CSE Veltech Rangarajan Dr Sagunthala R&D Institute of science and Technology Chennai School of Computing Mohan Babu University Tirupathi Department of Computer Science Thiruthangal Nadar College Chennai Department of CS & BS Saranathan College of Engineering Trichy Department of AIDS Erode Sengunthar Engineering College Erode

Cardiovascular disease is among the conditions that pose the greatest risk to life. Nearly 17 million people die as a result of its high mortality rate worldwide. To treat the illness quickly and reduce mortality, early diagnosis is helpful. The occurrence and absenteeism of the ailment can be hush-hush consuming a variability of ML techniques. The UCI dataset is to categorize heart disease using the techniques of LR, NB,SVM and Convolution Neural Networks(CNN). To progress the model’s recital, the dataset was cleaned, missing value searches were carried out, and feature selection was done through correlation by the goal worth for all of the features. The traits with the highest favourable associations were picked. The dataset is then alienated into train and trial sets, and classification is completed experimenting with a 70:30:80 ratio. The most accurate dividing ratio is 80:20. The best outcome will be recorded and used in the suggested model, which will compare Logistic regression, Naive Bayes, and Support vector machines with and without feature selection. Among all the models CNN shows the best result.

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Secure Data Reduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage

Secure Data Reduplication System with Efficient and Reliable...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： B Jayaram T. Suguna Shaik Fairooz Arul Selvam P S. Vimal Department of Computer Science and Business Systems RMK Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of CSE -AIML Malla Reddy Engineering College Secunderabad Telangana India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

Malicious hackers may compromise hundreds or millions of machines with little effort, allowing them to shut down networks, steal sensitive data, and compromise critical infrastructure. Data is stolen and used for malicious purposes by hackers who exploit security flaws in the network. And the sophisticated technology they use, like the bucks they do, are continually feeding them the information they needed, not just once. Moreover, even if Internet firewalls have improved, cybercrime has been on the rise. Then, we have a long way to go before we approach the limit, what with all the different kinds of cloud server devices that can connect to the internet nowadays. It's certain that in the future, little devices, no matter how helpful they are really in our everyday lives, will become the preferred tool of villains. In this research, we offer a novel security technique for protecting sensitive data on cloud servers. This intelligent cloud server was protected from any hacking with the aid of this algorithm. The system's output was measured against those of pre-existing algorithms. More than 96% of inputs are authenticated by the proposed technique, while password cracking operations are reduced by about 3%. The source code security management is improved to 99% and the updating issues for various devices entries is reduced 0.02%, and further the privacy breach is reduced up to 0.32%. Hence the proposed method was more secured from cyber security issues.

关键词： Cloud computing computer hacking Source coding Software algorithms Passwords Software Virtual private networks

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Wu, Zhaotao Wei, Jia Wang, Jiabing Li, Rui The School of Computer Science and Engineer- ing South China University of Technology Guangzhou China The School of Computer Science and Engineering South China University of Technology Guangzhou China The Golisano College of Computing and Information Sciences Rochester Institute of Technology RochesterNY14623 United States

We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and their corresponding segmentation labels can be increased between two consecutive slices in anisotropic 3D medical volumes. Unlike previous inter-slice imputation methods, which only focus on the smoothness in the axial direction, this study aims to improve the smoothness of the interpolated 3D medical volumes in all three directions: axial, sagittal, and coronal. The proposed multitask inter-slice imputation method, in particular, incorporates a smoothness loss function to evaluate the smoothness of the interpolated 3D medical volumes in the through-plane direction (sagittal and coronal). It not only improves the resolution of the interpolated 3D medical volumes in the through-plane direction but also transforms them into isotropic representations, which leads to better segmentation performances. Experiments on whole tumor segmentation in the brain, liver tumor segmentation, and prostate segmentation indicate that our method outperforms the competing slice imputation methods on both computed tomography and magnetic resonance images volumes in most cases. © 2022, CC BY.

关键词： Magnetic resonance imaging

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APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging

arXiv

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arXiv 2023年

作者： Chen, Zixuan Yang, Lingxiao Lai, Jianhuang Xie, Xiaohua The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging that aims to acquire high-quality CT images based on sparsely-sampled measurements. Recent works use Implicit Neural Representations (INRs) to build the coordinate-based mapping between sinograms and CT images. However, these methods have not considered the correlation between adjacent projection views, resulting in aliasing artifacts on SV sinograms. To address this issue, we propose a self-supervised SVCT reconstruction method – Anti-Aliasing Projection Representation Field (APRF), which can build the continuous representation between adjacent projection views via the spatial constraints. Specifically, APRF only needs SV sinograms for training, which first employs a line-segment sampling module to estimate the distribution of projection views in a local region, and then synthesizes the corresponding sinogram values using a center-based line integral module. After training APRF on a single SV sinogram itself, it can synthesize the corresponding dense-view (DV) sinogram with consistent continuity. High-quality CT images can be obtained by applying re-projection techniques on the predicted DV sinograms. Extensive experiments on CT images demonstrate that APRF outperforms state-of-the-art methods, yielding more accurate details and fewer artifacts. Our code will be publicly available soon. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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Energy Efficient Collaborative Federated Learning Design: A Graph Neural Network based Approach

Energy Efficient Collaborative Federated Learning Design: A ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

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SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection

arXiv

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arXiv 2024年

作者： Mulat, Kassaw Abraham Feng, Zhengyong Eshetie, Tegegne Solomon Hasen, Ahmed Endris School of Computer Science China West Normal University Sichuan Nanchong637002 China School of Electronic and Information Engineering China West Normal University Sichuan Nanchong637002 China Paul C. Lauterbur Research Center Shenzhen Institute of Advanced Technology Chinese Academy of Science Guangdong Shenzhen518055 China

Salient object detection (SOD) remains an important task in computer vision, with applications ranging from image segmentation to autonomous driving. Fully convolutional network (FCN)-based methods have made remarkable progress in visual saliency detection over the last few decades. However, these methods have limitations in accurately detecting salient objects, particularly in challenging scenes with multiple objects, small objects, or objects with low resolutions. To address this issue, we proposed a Saliency Fusion Attention U-Net (SalFAU-Net) model, which incorporates a saliency fusion module into each decoder block of the attention U-net model to generate saliency probability maps from each decoder block. SalFAU-Net employs an attention mechanism to selectively focus on the most informative regions of an image and suppress non-salient regions. We train SalFAU-Net on the DUTS dataset using a binary cross-entropy loss function. We conducted experiments on six popular SOD evaluation datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our method, SalFAU-Net, achieves competitive performance compared to other methods in terms of mean absolute error (MAE), F-measure, s-measure, and e-measure. © 2024, CC BY.

关键词： Image segmentation

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