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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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the analysis of EEG texture content for seizure prediction

The analysis of EEG texture content for seizure prediction

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： A. Petrosian R.W. Homan Department of Neurology Texas Tech University Health Sciences Center Lubbock TX USA

Texture features have been among major tools in image analysis field for decades. their applications in various computerized biomedical image processing teak advantage of the fact that computers are better than human observers at analyzing second order statistics. the attempt to make use of a similar approach to provide additional insight into EEG pattern recognition was presented by A. Petrosian et al. (Annual Meeting of the American EEG Society, New Orleans, LA, USA, p.88, Oct. 10-15, 1993). this study represents further development of suggested methods in recognition of different interictal, preictal, ictal, and postictal stages from EEG recordings. Practical application that is relevant to this study includes prediction of epileptic activity in patients before an actual seizure occurs. Although this study is preliminary and was carried out on the data obtained from one patient, the results showed feasibility of using signal texture information for distinguishing different abnormal patterns.< >

关键词： Electroencephalography Image texture analysis Biomedical computing Application software Computer applications Biomedical image processing Humans Image analysis Statistical analysis pattern recognition

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Open-World Group Retrieval with Ambiguity Removal: A Benchmark

Open-World Group Retrieval with Ambiguity Removal: A Benchma...

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international conference on pattern recognition

作者： Ling Mei Jianhuang Lai Zhanxiang Feng Xiaohua Xie School of Electronics and Information Technology Sun Yat-sen University China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China School of Data and Computer Science Sun Yat-Sen University China

ISBN: (纸本)9781728188089;9781728188096

Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. this paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. the open-world scenario considers that there are probably non-target people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. the open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.

关键词： Visualization Benchmark testing Cameras Feature extraction Robustness pattern recognition Probes

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Machine Learning-Based, Networking and computing Infrastructure Resource Management 17th

Machine Learning-Based, Networking and Computing Infrastruct...

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Chochliouros, Ioannis P. Kostopoulos, Alexandros Payaro, Miquel Verikoukis, Christos di Vimercati, Sabrina De Capitani Vinogradov, Evgenii Ranjbar, Vida Vardakas, John Rahman, Md Arifur Soumplis, Polyzois Varvarigos, Emmanuel Hellen Telecommun Org OTE SA 99 Kifissias Ave Athens 15124 Greece Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona Spain Univ Milan Milan Italy Katholieke Univ Leuven Leuven Belgium Iquadrat Informat SL Barcelona Spain IS Wireless Warsaw Poland Inst Commun & Comp Syst ICCS Athens Greece

ISBN: (纸本)9783030791599;9783030791575;9783030791568

5G mobile networks will be soon available to handle all types of applications and to provide service to massive numbers of users. In this complex and dynamic network ecosystem, end-to-end performance analysis and optimization will be key features in order to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a vision, the MARSAL project [ 1] targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. At the network design domain, MARSAL targets the development of novel cell-free-based solutions. Namely, scalable and cost-efficient wireless access points deployment will be achieved by exploiting the distributed cell-free concept combined with wireless and wired serial fronthaul approaches. We will target the inclusion of these innovative functionalities in the O-RAN project. In parallel, in the fronthaul/midhaul segments MARSAL aims to radically increase the flexibility of optical access architectures for Beyond-5G cell site connectivity via different levels of fixed-mobile convergence. In the network and service management domain, the design philosophy of MARSAL is to provide a comprehensive framework for the management of the entire set of communication and computational network resources by exploiting novel ML-based algorithms of both edge and midhaul data centers, by incorporating the Virtual Elastic Data Centers/Infrastructures paradigm. Finally, at the network security domain, MARSAL aims to introduce mechanisms that provide privacy and security to application workload and data, targeting to allow applications and users to maintain control over their data when relying on the deployed shared infrastructures, while AI and Blockchain technologies will be developed in order to guarantee a secured

关键词： Cell-free Distributed cloud Network automation Machine learning Secure multi-tenancy

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Variational Deep Embedding Clustering by Augmented Mutual Information Maximization

Variational Deep Embedding Clustering by Augmented Mutual In...

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international conference on pattern recognition

作者： Qiang Ji Yanfeng Sun Yongli Hu Baocai Yin Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China

Clustering is a crucial but challenging task in pattern analysis and machine learning. Recent many deep clustering methods combining representation learning with cluster techniques emerged. these deep clustering methods mainly focus on the correlation among samples and ignore the relationship between samples and their representations. In this paper, we propose a novel end-to-end clustering framework, namely variational deep embedding clustering by augmented mutual information maximization (VCAMI). From the perspective of VAE, we prove that minimizing reconstruction loss is equivalent to maximizing the mutual information of the input and its latent representation. this provides a theoretical guarantee for us to directly maximize the mutual information instead of minimizing reconstruction loss. therefore we proposed the augmented mutual information which highlights the uniqueness of the representations while discovering invariant information among similar samples. Extensive experiments on several challenging image datasets show that the VCAMI achieves good performance. we achieve state-of-the-art ACC results for clustering on MNIST (99.5%) and CIFAR-10 (65.4%) to the best of our knowledge.

关键词： Correlation Clustering methods Estimation Robustness Data mining Task analysis pattern analysis

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A Memory Access pattern-Based Program Profiling System for Dynamic Parallelism Prediction

A Memory Access Pattern-Based Program Profiling System for D...

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IEEE international conference on Trust, Security and Privacy in computing and Communications

作者： Zijun Han Guangzhi Qu Dona Burkard Kelvin Dobbins Comput. Sci. & Eng. Dept. Oakland Univ. Rochester MI USA Res. & Innovation Center Ford Motor Co. Dearbon MI USA

ISBN: (纸本)9781509032068

Parallel computing is the simultaneous use of multiple computer resources to solve a computational problem. In order to automatically execute a sequential program so that inherent parallelism could utilize the underlying multi-core platforms, program profiling is necessary to learn the control and dataflow dependency which is critical for predicting the parallelization feasibility and finding parallelism solutions. In literature, algorithms and tools have been designed for either dynamic or static program analysis in order to increase the accuracy and performance in discovering all aspects of the target program. However, memory and runtime overhead remain two challenge problems for program profiling. In this work, based on the PIN framework, we have designed and implemented a memory access pattern-based program profiling system for sequential program parallelization prediction. Validation results on seven SPEC 2006 benchmark programs show the effectiveness of proposed system on both memory usage and cpu time for target program dynamic profiling.

关键词： Parallel processing History Memory management Engines Runtime Pins Heuristic algorithms PARALLEL PROCESSING (COMPUTERS) Runtime PIN Store management Heuristic algorithms Engines Bone Nails analysis programme sequential program Synclitism parallelism object programs Memory Memory CPU time

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Quantifying the Uncertainty in 3D CT Lung Cancer Images Classification

Quantifying the Uncertainty in 3D CT Lung Cancer Images Clas...

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pattern recognition Systems (ICPRS), international conference on

作者： Rahimi Zahari Julie Cox Boguslaw Obara School of Computing Newcastle University Newcastle Upon Tyne UK County Durham and Darlington NHS Foundation Trust UK School of Computing and Biosciences Institute Newcastle University Newcastle Upon Tyne UK

Lung cancer has caused the deaths of millions of people. Early diagnosis is proven vital to increase survival rate. this can be done using computed tomography screening, and when such a system is in place with a computer-aided diagnostic system and deep learning algorithms, it has been proven to be an effective approach. However, most existing systems do not quantify the uncertainty in the prediction. Radiologists should have complete confidence in the predictions during the screening. therefore, to address this issue, we have developed an uncertainty-aware model framework not only to classify lung cancer nodules from 3D computed tomography images, but also to estimate the associated uncertainty in the prediction of the model. We have used the ResNet50 architecture as our base model and, through investigation with different loss functions and the inclusion of class weights, we concluded that the use of the focal loss function shows its superiority over the results of the benchmark model. Two approaches have been adopted to estimate the uncertainty during test time; Monte Carlo dropout and Test Time Augmentation, and from the multiple predictions obtained, three uncertainty metrics have been calculated; entropy, standard deviation, and Bhattacharyya coefficient. Our model achieves the best performance with the F1 score, 83.0 % using the dropout rate, p, 0.5 and Monte Carlo iterations, M, 10. We have also shown that using a threshold value can greatly improve the classification performance even more, but the amount of data to be referred needs to be taken into account.

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Data Privacy Protection of Industrial Blockchain 10th

Data Privacy Protection of Industrial Blockchain

引用

10th EAI international conference on Cloud computing, CloudComp 2020

作者： Long, Huaqiu Hou, Jun Li, Qianmu Ma, Na Jiang, Jian Qi, Lianyong Xu, Xiaolong Zhang, Xuyun Intelligent Manufacturing Department Wuyi University Jiangmen529020 China Nanjing Institute of Industry Technology Nanjing210023 China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Jiangsu Zhongtian Internet Technology Co. Ltd. Nantong226009 China School of Information Science and Engineering Qufu Normal University Jining272000 China Nanjing University of Information Science and Technology Nanjing210023 China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9783030699918

this paper studies the data privacy protection of industrial block chain. Aiming at the privacy leakage problem of industrial blockchain, combining symmetric encryption and homomorphic encryption, the data privacy protection method is proposed to ensure the confidentiality and privacy of industrial block chain and improve the privacy security of industrial enterprises. this paper designs common basic chain code, security service chain code and privacy protection chain code in the chain code layer, USES AES algorithm to encrypt sensitive information of industrial enterprises, and USES Paillier algorithm to encrypt maintenance cost. the chain code of privacy protection can be used for security access and fault event audit to guarantee the privacy security of industrial enterprises. In addition, in order to achieve the unity of call chain code, the call chain code is encapsulated to provide support for fast and highly concurrent upload data and access data. Finally, the existing sensitive and private data storage methods of industrial block chain are analyzed, and the experimental comparison with the method in this paper proves the effectiveness of this method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Blockchain

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Task decomposition based on output parallelism

Task decomposition based on output parallelism

引用

IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Sheng-Uei Guan Shanchun Li Department of Electrical and Computer Engineering National University of Singapore Singapore

In this paper, we propose a new method for task decomposition based on output parallelism, in order to find the appropriate architectures for large-scale real-world problems automatically and efficiently. By using this method, a problem can be divided flexibly into several sub-problems as chosen, each of which is composed of the whole input vector and a fraction of the output vector. Each module (for each subproblem) is responsible for producing a fraction of the output vector of the original problem. this way, the hidden structure for the original problem's output units is decoupled. these modules can be grown and trained in sequence or in parallel. Incorporated with the constructive learning algorithm, our method does not require excessive computation and any prior knowledge concerning decomposition. the feasibility of output parallelism is analyzed and proved. Several benchmarks are implemented to test the validity of this method. their results show that this method can reduce computation time, increase learning speed, and improve generalization accuracy for both classification and regression problems.

关键词： Parallel processing Neural networks Interference Concurrent computing Computer architecture Benchmark testing Multi-layer neural network pattern classification Function approximation Training data

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2. Image computing for digital pathology

2. Image computing for digital pathology

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international conference on pattern recognition

作者： Shishir Shah Edgar Gabriel University of Houston University of Houston Houston TX US

Summary form only given. Pathologists and cancer biologists rely on tissue and cellular analysis to study cancer expression, genetic profiles, and cellular morphology to understand the underlying basis for a disease and to grade the level of disease progression. Conventional analysis of tissue histology and sample cytology includes the steps of examination of the stained tissue or cell smear under a microscope, scoring the expression relative to the most highly expressing (densely stained) area on a predefined scale for normal, cancer, stromal regions based on the morphology of the tissue, estimating the percentage area of cancer tissue relative of normal and stroma, and multiplying the score by the percentage area of cancer region and converting to another predefined scale for statistical analyses. Most of this analysis is done manually or with limited tools to aid the scoring process. Over the last 5 years, automated and semi- automated microscope slide scanners have become available in the marketplace. these scanners rely on sophisticated microscopes and allow for the digitization of the entire sample at varying magnifications. this has led to the emergence of digital pathology and a growing amount of image data. Each sample digitized is typically of the order of 2.7 GB to 10 GB in size depending on the magnification of the digitizing system with an image size of 30,000 ? 30,000 pixels or larger. Further, current software and methods for automated scoring of tissue is very limited. this has led to an increased interest in identifying novel solutions to automated histology and cytology analysis. In order to achieve high computational accuracy with reasonable turnaround times, novel approaches from the data and resource management perspective are also required to address handling of image sizes outlined above. Two developments in computer industry make the current generation of scientists more likely to solve the performance challenges associated with the large ima

关键词： Pathology Image analysis Cancer Parallel processing Microscopy Morphology Diseases Concurrent computing Clustering algorithms Image storage

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Attentive Part-aware Networks for Partial Person Re- identification

Attentive Part-aware Networks for Partial Person Re- identif...

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international conference on pattern recognition

作者： Lijuan Huo Chunfeng Song Zhengyi Liu Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) Artificial Intelligence ResearchChinese Academy of Sciences Jiaozhou Qingdao China School of Computer Science and Technology Anhui University Hefei China University of Chinese Academy of Sciences (UCAS)

Partial person re-identification (re-ID) refers to re-identify a person through occluded images. It suffers from two major challenges, i.e., insufficient training data and incomplete probe image. In this paper, we introduce a part-aware learning method for partial person re-identification. On the one hand, we adopt data augmentation operation to enrich the training data and improve the robustness of the model. On the other hand, we intuitively find that the partial person images usually have fixed percentages of parts, therefore, in partial person re-ID task, the probe image could be cropped from the pictures and divided into several different partial types following fixed ratios. Based on the cropped images, we propose the Cropping Type Consistency (CTC) loss to classify the cropping types of partial images. Moreover, in order to help the network better fit the generated and cropped data, we incorporate the Block Attention Mechanism (BAM) into the framework for attentive learning. To enhance the retrieval performance in the inference stage, we implement cropping on gallery images according to the predicted types of probe partial images. through calculating feature distances between the partial image and the cropped holistic gallery images, the model can recognize the right person from the gallery. To validate the effectiveness of our approach, we conduct extensive experiments on the partial re- ID benchmarks and achieve state-of-the-art performance.

关键词： Training Learning systems Image recognition Semantics Training data Robustness Data models

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