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Risk Management of Fog computing for Improving IoT Security

Risk Management of Fog Computing for Improving IoT Security

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Shigeaki Tanimoto Mari Matsumoto Teruo Endo Hiroyuki Sato Atsushi Kanai Faculty of Social System Science Chiba Institute of Technology Chiba Japan Faculty of Liberal Arts Osaka Shoin Women&#x0027 s University Osaka Japan Information Technology Center The University of Tokyo Tokyo Japan Faculty of Science and Engineering Hosei University Tokyo Japan

ISBN: (纸本)9781665424219

With the spread of the Internet, various devices are now connected to it and the number of IoT devices is increasing. Data generated by IoT devices has traditionally been aggregated in the cloud and processed over time. However, there are two issues with using the cloud. the first is the response delay caused by the long distance between the IoT device and the cloud, and the second is the difficulty of implementing sufficient security measures on the IoT device side due to the limited resources of the IoT device at the end. To address these issues, fog computing, which is located in the middle between IoT devices and the cloud, has been attracting attention as a new network component. However, the risks associated with the introduction of fog computing have not yet been fully investigated. In this study, we conducted a risk assessment of fog computing, which is newly established to promote the use of IoT devices, and identified 24 risk factors. the main countermeasures include the gradual introduction of connected IoT connection protocols and security policy matching. We also demonstrated the effectiveness of the proposed risk measures by evaluating the risk values. the proposed risk countermeasures for fog computing should help us to utilize IoT devices in a safe and secure manner.

关键词： Cloud computing Protocols Electric breakdown Delays Internet of things Security Risk management

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Cloud Advisor Bot: Towards a Chatbot to Enable Efficient Cloud Assessments

Cloud Advisor Bot: Towards a Chatbot to Enable Efficient Clo...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Emanuel Hofer Stella Gatziu Grivas Claudio Giovanoli School of Business FHNW University of Applied Sciences and Arts Northwestern Switzerland

ISBN: (纸本)9781665424219

Increased consciousness of cloud computing capabilities leads to greater adoption of the cloud. For many businesses, cloud computing represents an attractive cost-saving option for the establishment and maintenance of IT resources. Decision-makers are often confronted with multi-criteria decision issues regarding cloud service and deployment model selection and do not have the required technological and business-related background. this work aimed at developing a conversational decisions advisor in the form of the Cloud Advisor Bot to support complex assessment of cloud needs. the literature review revealed a gap in this domain as there is no pre-existing research in the area of chatbots that support the cloud assessment process. Based on the literature reviews as well as further research, it was investigated which elements needed to be developed in order to implement a prototype of the Cloud Advisor Bot. To achieve this, design science research was applied to iteratively develop the solution. the evaluation has shown that the prototype of the Cloud Advisor Bot can be successfully used in order to conduct an assessment regarding a possible cloud deployment scenario. the paper also proposes further developments in order to improve the solution and make it viable for the future

关键词： Measurement Cloud computing Solid modeling Analytical models Bibliographies Prototypes Oral communication

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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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Knowledge-Based and Intelligent Information and Engineering Systems 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Rossitza Setchi Ivan Jordanov Robert J. Howlett Lakhmi C. Jain

ISBN: (数字)9783642153877

ISBN: (纸本)9783642153860

th the 14 international conference on Knowledge-Based and Intelligent Information and Engineering Systems was held during September 8–10, 2010 in Cardiff, UK. the conference was organized by the School of Engineering at Cardiff University, UK and KES international. KES2010 provided an international scientific forum for the presentation of the - sults of high-quality research on a broad range of intelligent systems topics. the c- ference attracted over 360 submissions from 42 countries and 6 continents: Argentina, Australia, Belgium, Brazil, Bulgaria, Canada, Chile, China, Croatia, Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong ROC, Hungary, India, Iran, Ireland, Israel, Italy, Japan, Korea, Malaysia, Mexico, the Netherlands, New Zealand, Pakistan, Poland, Romania, Singapore, Slovenia, Spain, Sweden, Syria, Taiwan, - nisia, Turkey, UK, USA and Vietnam. the conference consisted of 6 keynote talks, 11 general tracks and 29 invited s- sions and workshops, on the applications and theory of intelligent systems and related areas. the distinguished keynote speakers were Christopher Bishop, UK, Nikola - sabov, New Zealand, Saeid Nahavandi, Australia, Tetsuo Sawaragi, Japan, Yuzuru Tanaka, Japan and Roger Whitaker, UK. Over 240 oral and poster presentations provided excellent opportunities for the presentation of interesting new research results and discussion about them, leading to knowledge transfer and generation of new ideas. Extended versions of selected papers were considered for publication in the Int- national Journal of Knowledge-Based and Intelligent Engineering Systems, Engine- ing Applications of Artificial Intelligence, Journal of Intelligent Manufacturing, and Neural computing and Applications.

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Data Mining and Knowledge Discovery Computer Communication Networks Information Storage and Retrieval pattern recognition

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ESACR: An Energy Saving Approach from Cloud Resources for Green Cloud Environment

ESACR: An Energy Saving Approach from Cloud Resources for Gr...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Shailesh Saxena Mohammad Zubair Khan Ravendra Singh MJP Rohilkhand University Bareilly India Taibah University Medina KSA

ISBN: (纸本)9781665439718

Cloud is most recognized environment of computing due to the widely availability of its services to the users. Cloud data centers have adopted virtualization of resources technically known as virtual machines (VMs) for the effective and efficient computation of services. But power consumption through the cloud data centers (CDCs) highlighted as one of the major issues responsible to reduce the quality of services of Cloud computing. Because a large number of electronic resources like processing nodes, servers, disk storage, and networking nodes (i.e. switch/routers) in CDC consume a high degree of energy during computation. Energy is also consumed by cooling plants to cool the data centers as it produces a huge amount of heat (CO 2 ) surrounding it. Due to the high energy consumption, the cloud providers pay a high cost of energy computation and it also contributes more carbon emissions to the atmosphere. Reducing energy consumption and CO 2 emission is a major challenge in turning the computation from cloud computing to green computing. So, our main focus in this paper is to develop an energy-saving approach for cloud computing based on resource management. Due to variations in the request of services by the users, the demand for resources also varies during the computation. the amount of users’ request are not always same for all 24 hours in a day. Some period of time it is very low and at that period of time, many resources become idle and consume some fixed amount of energy that is a wastage of energy. In this manner, the proposed ESACR approach first define active (required) and idle (non-required) resources as per the users’ request for services at any instance; after that, it converts the idle resources into an energy-saving mode (i.e., turn OFF) unless it is not required for the service of users’ request. So that the saving of energy will increased, if at any instance all non-useable resources is define in OFF mode.

关键词： Cloud computing Data centers Energy consumption Switches Quality of service Turning Virtual machining

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Knowledge-Based and Intelligent Information and Engineering Systems 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Rossitza Setchi Ivan Jordanov Robert J. Howlett Lakhmi C. Jain

ISBN: (数字)9783642153907

ISBN: (纸本)9783642153891

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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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3D Dental Biometrics: Automatic Pose-invariant Dental Arch Extraction and Matching

3D Dental Biometrics: Automatic Pose-invariant Dental Arch E...

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international conference on pattern recognition

作者： Xin Zhong Zhiyuan Zhang Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Fusionopolis Singapore Digital Manufacturing and Design Centre Singapore University of Technology and Design Singapore

ISBN: (纸本)9781728188089;9781728188096

A novel automatic pose-invariant dental arch extraction and matching framework is developed for 3D dental identification using laser-scanned dental plasters. In our previous attempt [1]-[5], 3D point-based algorithms have been developed and they have shown a few advantages over existing 2D dental identifications. this study is a continuous effort in developing arch-based algorithms to extract and match dental arch feature in an automatic and pose-invariant way. As best as we know, this is the first attempt at automatic dental arch extraction and matching for 3D dental identification. A Radial Ray Algorithm (RRA) is proposed by projecting dental arch shape from 3D to 2D. this algorithm is fully automatic and fast. Preliminary identification result is obtained by matching 11 postmortem (PM) samples against 200 ante-mortem (AM) samples. 72.7% samples achieved top 5% accuracy. 90.9% samples achieved top 10% accuracy and all 11 samples (100%) achieved top 15.5% accuracy out of the 200-rank list. In addition, the time for identifying a single subject from 200 subjects has been significantly reduced from 45 minutes to 5 minutes by matching the extracted 2D dental arch. Although the extracted 2D arch feature is not as accurate and discriminative as the full 3D arch, it may serve as an important filter feature to improve the identification speed in future investigations.

关键词： Matched filters three-dimensional displays Biometrics (access control) Shape Filtering algorithms Feature extraction Dentistry

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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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Extraction of Frequent Sequential patterns from Sequence at Uneven Intervals

Extraction of Frequent Sequential Patterns from Sequence at ...

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international Symposium on Advanced Intelligent Systems

作者： Kenta Morita Haruhiko Takase Masachika Sawamura Noaki Morita Hidehiko Kita Graduate school of Engineering Mie University Mie Japan Graduate school of Engineering Mie University City Japan Graduate school of Information and Telecommunication Engineering Tokai University Tokyo Japan

ISBN: (纸本)9781538626344;9781538626337

In this paper, we discuss a method to extract frequent sub-sequences from time series data with variations in intervals of input elements. Several methods for extracting frequent sub-sequences from sequence data have been proposed. However, since these existing methods target series data whose element input intervals are constant, it is difficult to extract from time series data whose input intervals are not uniform, such as voice data. therefore, we propose a method that can extract frequent sub-sequences from time series data in which elements and element appearance intervals are not uniform. To extract frequent sub-sequences from time series data whose element appearance intervals are not uniform, we proposed a method to utilize the internal potential characteristic of the unit of the spiking neural network. To confirm the effectiveness of the proposed method, we created time series data with nonuniform intervals of elements and elements similar to Japanese voice data and fed it to the proposed neural network. As a result, the proposed neural network extracted frequent sub-sequences from time series data with the same performance as extraction from sequence data.

关键词： Time-series information processing Frequent pattern mining Spiking neuron STDP

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