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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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NAS Honeypot Technology Based on Attack Chain 10th

NAS Honeypot Technology Based on Attack Chain

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10th international conference on Computer Engineering and Networks, CENet 2020

作者： Liu, Bing Shu, Hui Kang, Fei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811584619

With the wide application of network attached storage (NAS), the security problem is becoming more and more serious, together with the increasingly fierce attacks against NAS devices of different manufacturers. In order to better capture various types of attacks against NAS devices and detect security threats in time, this paper proposes a solution named NAS honeypot, mining the potential security threats through modeling and analyzing the NAS threats. then a NAS honeypot based on device virtualization technology and depth monitoring and attack induction was designed on the basis of the construction NAS attack chain. Experimental results show that the NAS honeypot can effectively capture, record and analyze network attacks against multi-types NAS devices with a strong guiding effect in mastering popular attack method of NAS devices and alleviating the security threats of NAS. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Security systems

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Stacked Dark COVID-Net: A Multi-class Multi-label Classification Approach for Diagnosing COVID-19 Using Chest X-Ray Images 4th

Stacked Dark COVID-Net: A Multi-class Multi-label Classifica...

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4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

作者： Anila Glory, H. Meghana, S. Kesav Kumar, J.S. Shankar Sriram, V.S. School of Computing SASTRA Deemed University Tamil Nadu Thanjavur613401 India

ISBN: (纸本)9783031070044

Most challenging yet, the need of the hour is accurate diagnosis of COVID-19, as the Coronavirus cases are increasing drastically day-by-day. Ceaseless efforts by the researchers and innovators have led to the development of several diagnostic models based on Deep Learning for effective diagnosis of COVID-19. However, the Deep Learning techniques that have been developed so far, fail to address major challenges such as overfitting, stability, computation overhead due to the usage of the massive volume of parameters and problems associated with the multi-class classification. Also in the medical perspective, researchers often suffer to identify the infinitesimal difference that exists in the radiographic images among the several lung diseases which makes the decision-making process difficult. thus, to curb the crisis and to provide promising solutions & expertise for accurate diagnosis, this paper presents a novel lightweight multi-class multi-label COVID-19 detection model to assist physicians with greater ease to fight against this pandemic situation. Radiographic images are pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and classified using novel Stacked Dark COVID-Net. the proposed model is validated using chest X-ray images and the results confirm the efficacy of the proposed model in terms of classification accuracy, sensitivity, specificity and stability. © 2022, Springer Nature Switzerland AG.

关键词： Deep learning

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5th international conference on Intelligent, Interactive Systems and Applications, IISA 2020

5th International Conference on Intelligent, Interactive Sys...

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5th international conference on Intelligent, Interactive Systems and Applications, IISA 2020

ISBN: (纸本)9783030637835

the proceedings contain 120 papers. the special focus in this conference is on Active Inference. the topics include: Investigation and Design of Multi-wavelength LED Based Optical Communication System;Research on High-Speed Data Acquisition System Based on PCIE;impact of Transmission Power Control Mechanism in Underwater Wireless Sensor Networks;design and Implementation of Deep-Sea Emergency Response System;research on Component Classification Strategy Based on Convolutional Neural Network;research on Multi-label Clothing Image Classification Based on Convolutional Neural Network;partition-Based Energy Efficient Routing Protocol for Wireless Sensor Networks;design of Remote Control Signal Hijacking Device Based on Microcomputer System;design and Implementation of Simple Radio Signal Transmitter;multi-station Integration-Based Coordinated Control for Voltage Support in Active Distribution Network;Research on Service Evaluation of Automobile Inspection and Testing Based on AHP - Fuzzy Comprehensive Evaluation Method;scheme to Optimize and Improve the Stepped Structured Sleeve Hydraulic Cylinder;optimization Algorithm of Pigeon Flight Path Location Based on Least Square;multi-Colony Ant Algorithm Applied to the Yangtze Gorges Ship Lock Arrangement Optimization;infrared and Visible Image Fusion Based on Multi-scale Decomposition and Gradient Optimization;novel Bayesian Network Incremental Learning Method Based on Particle Swarm Optimization Algorithm;feed Formulation Cost Optimization Based on the Improved Genetic Algorithm;vehicle Disposition and Routing Optimization of Reverse Logistics Based on Improved Genetic Algorithm;two-Stage Fusion of Local Binary pattern and Discrete Cosine Transform for Infrared and Visible Face recognition;illustration and Identification of Skin Lesions in Dermoscopic Dataset Using pattern recognition Techniques;correlation Analysis of Children’s Stroller Information.

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A Container-Based of Edge Device Monitoring on Kubernetes 10th

A Container-Based of Edge Device Monitoring on Kubernetes

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10th international conference on Frontier computing, FC 2020

作者： Fathoni, Halim Yen, Hao-Yi Yang, Chao-Tung Huang, Chin-Yin Kristiani, Endah Department Industrial Engineering and Enterprise Information Tunghai University Taichung City407302 Taiwan Departement of Ekonomi dan Bisnis Politeknik Negeri Lampung Bandar Lampung Indonesia Department Computer Science Tunghai University Taichung City407302 Taiwan Departement of Informatics Krida Wacana Christian University Jakarta11470 Indonesia

ISBN: (纸本)9789811601149

the concept of the Internet of things has widely used in various applications in life from home to big cities that have a variety of edge devices to facilitate edge computing. this paper attempts to use Kubernetes to establish an environment for collecting the internal resource usage status of various node devices and implementing monitoring and visualization. the deep learning models are deployed on heterogeneous devices for performance evaluation and verification. In this experiment, Kubernetes used to maintain a Docker container that used to deploy all applications mentioned above. All the data then visualize through Grafana. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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PQ-HDC: Projection-Based Quantization Scheme for Flexible and Efficient Hyperdimensional computing

PQ-HDC: Projection-Based Quantization Scheme for Flexible an...

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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： Huang, Chi-Tse Chang, Cheng-Yang Chuang, Yu-Chuan Wu, An-Yeu (andy) Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan

ISBN: (纸本)9783030791520;9783030791506;9783030791490

Brain-inspired Hyperdimensional (HD) computing is an emerging technique for low-power/energy designs in many machine learning tasks. Recent works further exploit the low-cost quantized (bipolarized or ternarized) HD model and report dramatic improvements in energy efficiency. However, the quantization loss of HD models leads to a severe drop in classification accuracy. this paper proposes a projection-based quantization framework for HD computing (PQ-HDC) to achieve a flexible and efficient trade-off between accuracy and efficiency. While previous works exploit thresholding-quantization schemes, the proposed PQ-HDC progressively reduces quantization loss using a linear combination of bipolarized HD models. Furthermore, PQ-HDC allows quantization with flexible bit-width while preserving the computational efficiency of the Hamming distance computation. Experimental results on the benchmark dataset demonstrate that PQ-HDC achieves a 2.82% improvement in accuracy over the state-of-the-art method.

关键词： Brain-inspired computing Hyperdimensional computing Dynamic model Energy efficiency

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Data Cataloging of the heritage-based villages through geographic information system (GIS) 10

Data Cataloging of the heritage-based villages through geogr...

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10th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Nair, Preeti Singh, Devendra Pratap Munoth, Navneet Amity Univ Amity Sch Architecture & Planning Noida Uttar Pradesh India MANIT Bhopal Dept Architecture & Planning Bhopal 462003 India

ISBN: (纸本)9781728127910

this research paper's main objective is to present the current status of some old vernacular residential buildings in the rural areas of Himachal Pradesh a state rich in heritage, religious and cultural aspects in India. the research paper also correlates to the traces of the human life (socio-cultural and economic) and communities' perception towards built heritage in Rural areas. For this purpose, the studies have been conducted on two of its villages which have been given as the heritage village status by the state government. the paper uses GIS as a software tool for a better understanding and valorization of the cultural heritage in an holistic manner.

关键词： Rural Heritage Vernacular architecture Community Participation Data Cataloguing Settlement pattern

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Domain Adaptation for Cross-Subject Emotion recognition by Subject Clustering

Domain Adaptation for Cross-Subject Emotion Recognition by S...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Jin Liu Xinke Shen Sen Song Dan Zhang Department of Biomedical Engineering Laboratory of Brain and Intelligence School of Medicine Tsinghua University Beijing China Laboratory of Brain and Intelligence School of Medicine Tsinghua University Beijing China Laboratory of Brain and Intelligence Tsinghua University Beijing China

the high inter-subject variability in emotional EEG activities has posed great challenges for practical EEG-based affective computing applications. the recently popular domain adaptation strategy seemed to be a promising technique for addressing this issue, by minimizing the discrepancy of EEG data from different subjects. the present study proposed and implemented an extended Domain Adaptation method by introducing Subject Clustering (DASC). By clustering subjects based on the similarity of their emotion-specific EEG activities, the DASC method could make a flexible use of the available source domain information towards an optimized target domain application. Using the publicly available EEG dataset of DEAP, the DASC method achieved an average accuracy of 73.9±13.5% and 68.8±11.2% for binary classifications of the high or low levels of valence and arousal. Comparison with the state-of-the-art performance as well as the ablation experiments suggest the proposed DASC method as an effective extension to the conventional domain adaptation methods for EEG-based emotion recognition.

关键词： Emotion recognition Affective computing Neural engineering Electroencephalography

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From DevOps to NoOps: Is It Worth It? 1

引用

10th international conference on Cloud computing and Services Science (CLOSER)

作者： Jindal, Anshul Gerndt, Michael Tech Univ Munich Chair Comp Architecture & Parallel Syst Garching Germany

ISBN: (数字)9783030723699

ISBN: (纸本)9783030723682;9783030723699

With the rise of the adoption of microservice architecture due to its agility, scalability, and resiliency for building the cloud-based applications and their deployment using containerization, DevOps were in demand for handling the development and operations together. However, nowadays serverless computing offers a new way of developing and deploying cloud-native applications. Serverless computing also called NoOps, offloads management and server configuration (operations work) from the user to the cloud provider and lets the user focus only on the product developments. Hence, there are debates regarding which deployment strategy to use. this research provides a performance comparison of a cloud-native web application along with three different function benchmarks in terms of scalability, reliability, and latency when deployed using DevOps and NoOps deployment strategy. NoOps deployment in this work is achieved using Google Cloud Function and OpenWhisk, while DevOps is achieved using the Kubernetes engine. this research shows that neither of the deployment strategies fits all the scenarios. the experimental results demonstrate that each type of deployment strategy has its advantages under different scenarios. the DevOps deployment strategy has a huge performance advantage (almost 72% lesser 90 percentile response time) for simple web-based requests and requests accessing databases while compute-intensive applications perform better with NoOps deployment. Additionally, NoOps deployment provides better scaling-agility as compared to DevOps.

关键词： Microservices Serverless DevOps NoOps Cloud-native applications Cloud computing

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Integrate-and-Fire Neurons for Low-Powered pattern recognition 20th

Integrate-and-Fire Neurons for Low-Powered Pattern Recogniti...

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20th international conference on Artificial Intelligence and Soft computing (ICAISC)

作者： Bacho, Florian Chu, Dominique Univ Kent Sch Comp CEMS Canterbury CT2 7NF Kent England

ISBN: (纸本)9783030879860;9783030879853

Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the data and the decision is complex and/or the amount of data to transmit is large (e.g. in biologgers). Artificial Neural Networks (ANNs) can efficiently detect patterns in the input data which makes them suitable for decision making or compression of information for data transmission. However, ANNs require a substantial amount of energy which reduces the lifetime of battery-powered devices. therefore, the use of Spiking Neural Networks can improve such systems by providing a way to efficiently process sensory data without being too energy-consuming. In this work, we introduce a low-powered neuron model called Integrate-and-Fire which exploits the charge and discharge properties of the capacitor. Using parallel and series RC circuits, we developed a trainable neuron model that can be expressed in a recurrent form. Finally, we trained its simulation with an artificially generated dataset of dog postures and implemented it as hardware that showed promising energetic properties.

关键词： Remote system Spiking Neural Networks Integrate-and-fire Neuromorphic hardware

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Parallel Asynchronous Memetic Optimization for Freeform Optical Design 10th

Parallel Asynchronous Memetic Optimization for Freeform Opti...

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10th international conference on the theory and Practice of Natural computing (TPNC)

作者： Sakharov, Maxim Houllier, thomas Lepine, thierry Bauman Moscow State Tech Univ 5-1 2 Ya Baumanskaya Moscow 105005 Russia Univ Lyon Lab Hubert Curien UMR CNRS 5516 18 Rue Benoit Lauras F-42000 St Etienne France Sophia Engn 5 Rue Soutrane F-06560 Sophia Antipolis France Inst Opt Grad Sch 18 Rue Benoit Lauras F-42000 St Etienne France

ISBN: (纸本)9783030904258;9783030904241

Freeform optical systems include lens or mirror surfaces with shapes that have no axis of revolution in order to reach better performance in off-axis designs such as Head-Mounted Displays (HMD). they have an increased number of design parameters compared to rotationally symmetrical optical systems which makes their optimization more difficult. In this paper we presented results of several studies where various optimization techniques were used to optimize a freeform HMD prism with two mirrors. It was demonstrated that algorithms which performed well on traditional systems were not capable of obtaining high-quality solutions for the system under investigation. this paper presents a new parallel asynchronous memetic algorithm designed for optimizing freeform optical systems. the algorithm incorporates local search techniques into an asynchronous parallel computing procedure thus helping to speed-up the convergence to a high-quality solution. the results of a comparative study, presented in this paper along with the algorithm description, demonstrate that the proposed method is not only capable of obtaining a good solution but is also comparable with high-end commercial optical software packages.

关键词： Global optimization Freeform optics Memetic algorithms Parallel computing

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