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Assured data deletion with fine-grained access control for fog-based industrial applications

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arXiv 2018年

作者： Yu, Yong Xue, Liang Li, Yannan Du, Xiaojiang Guizani, Mohsen Yang, Bo School of Computer Science Shaanxi Normal University Xi'an710062 China Shanghai KOAL Software Co. Ltd Shanghai200436 China Dept. of Computer and Information Sciences Temple University United States Department of Electrical and Computer Engineering University of Idaho United States

The advances of cloud computing, fog computing and Internet of Things (IoT) make the industries more prosperous than ever. A wide range of industrial systems such as transportation systems and manufacturing systems have been developed by integrating cloud computing, fog computing and IoT successfully. Security and privacy issues are a major concern that hinders the wide adoptions of these novel techniques. In this paper, we focus on assured data deletion, an issue which is important but received less attention in academia and industry. We firstly propose a framework to integrate the cloud, the fog and the things together to manage the stored data from industries or individuals. We then focus on secure data deletion in this framework by proposing an assured data deletion scheme which fulfills fine-grained access control over sensitive data and verifiable data deletion. Only the data owners and the fog devices are involved when deleting a data key and validating the data deletion, which makes the protocol practical due to the features of low latency and real-time interaction of fog computing. The proposed protocol takes advantage of attributebased encryption and is provably secure under the standard model. The theoretical analysis shows the good performance and functionality requirements while the implementation results demonstrate the feasibility of our proposal. Copyright © 2018, The Authors. All rights reserved.

关键词： Fog computing

Joint optimization of sensing period and transmission time for energy-efficient CRSNs 11

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Joint optimization of sensing period and transmission time f...

11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017

作者： Jin, Zilong Jinsung, Cho Kong, Fanhua Kim, Chang-Woo Na, Sangyeob School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Cho Dept. of Computer Science and Engineering Kyung Hee University Yongin17104 Korea Republic of Dept. of Electronics and Radio Engineering Kyung Hee University Yongin17104 Korea Republic of Dept. of Computer Science Namseoul University Cheonan31020 Korea Republic of

ISBN: (纸本)9781450348881

Cognitive radio technology can enable secondary users (SUs) to occupy licensed bands in a non-interference way. SUs perform spectrum sensing to determine the presence of primary user (PU). Spectrum sensing period and transmission time are the critical parameters for the sensing performance and the secondary throughput, respectively. In this paper, a joint optimization of sensing period and transmission time is proposed for cognitive radio sensor network (CRSNs). The joint optimization model is established by taking the network energy efficiency and interference caused by SU's traffic into consideration. By using the optimization model, we try to figure out the optimal sensing period and transmission time which can guarantee the network energy efficiency and maximize the network throughput jointly. The performance of the proposed optimization model is evaluated through a set of simulations, and the existence of the optimal sensing period and transmission time is verified. © 2017 ACM.

关键词： Sensor networks

Behavior Recognition of a Person in a Daily Video Using Joint Position Information

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Behavior Recognition of a Person in a Daily Video Using Join...

Artificial Intelligence and Knowledge engineering (AIKE)

作者： Jeong-Min Seo Haanju Yoo Kimin Yun Hyunil Kim Sang-Il Choi Dept. Data Science (of Dankook University) Yongin-si South Korea Dept. Computer Science and Engineering (of Dankook University) Yongin-si South Korea Electronics and Telecommunications Research Institute (ETRI) Daejeon South Korea Dept. Software Science (of Dankook University) Yongin-si South Korea

ISBN: (纸本)9781538695562;9781538660843

In this paper, we propose a method to recognize people's specific behaviors in images acquired in everyday life. The poses that occur when taking a specific action are represented using the joint coordinates of the body. To reduce the influence of the perspective effect on the locations in an image, the coordinates of joints for each person were normalized and the pose was classified using SVM with the normalized coordinates. Experimental results on video images of CCTV camera on ordinary alleys showed that the proposed method relatively recognized a specific behavior.

关键词： Image recognition Support vector machines Cameras Neck Benchmark testing Data mining Legged locomotion

Autotiering: Automatic data placement manager in multi-tier all-flash datacenter

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arXiv 2018年

作者： Yang, Zhengyu Hoseinzadeh, Morteza Andrews, Allen Mayers, Clay Evans, David Bolt, Rory Bhimani, Janki Mi, Ningfang Swanson, Steven Dept. of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Dept. of Computer Science and Engineering University of California San Diego San DiegoCA92093 United States Samsung Semiconductor Inc. Memory Solution Research Lab Software Group San DiegoCA92121

In the year of 2017, the capital expenditure of Flashbased Solid State Drivers (SSDs) keeps declining and the storage capacity of SSDs keeps increasing. As a result, the "selling point" of traditional spinning Hard Disk Drives (HDDs) as a backend storage - low cost and large capacity - is no longer unique, and eventually they will be replaced by low-end SSDs which have large capacity but perform orders of magnitude better than HDDs. Thus, it is widely believed that all-flash multi-tier storage systems will be adopted in the enterprise datacenters in the near future. However, existing caching or tiering solutions for SSD-HDD hybrid storage systems are not suitable for all-flash storage systems. This is because that all-flash storage systems do not have a large speed difference (e.g., 10x) among each tier. Instead, different specialties (such as high performance, high capacity, etc.) of each tier should be taken into consideration. Motivated by this, we develop an automatic data placement manager called "AutoTiering" to handle virtual machine disk files (VMDK) allocation and migration in an all-flash multitier datacenter to best utilize the storage resource, optimize the performance, and reduce the migration overhead. AutoTiering is based on an optimization framework, whose core technique is to predict VM's performance change on different tiers with different specialties without conducting real migration. As far as we know, AutoTiering is the first optimization solution designed for all-flash multi-tier datacenters. We implement AutoTiering on VMware ESXi [1], and experimental results show that it can significantly improve the I/O performance compared to existing solutions. Copyright © 2018, The Authors. All rights reserved.

关键词： Hard disk storage

Comparative Analysis of Context Based Classification of Twitter

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Comparative Analysis of Context Based Classification of Twit...

International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall)

作者： Muhammad Salman Tiwana Farooq Javeed Muhammad Ikramullah Lali Hafsa Dar Muhammad Bilal Dept. of CS&IT University of Sargodha Sargodha Pakistan Dept. of Computer Science University of Gujrat Gujrat Pakistan Dept. of Software Engineering University of Gujrat Gujrat Pakistan School of Computing & IT Taylor’s University Subang Jaya Malaysia

ISBN: (纸本)9781538671689

Social media offers online data source where internet users' share their opinions, views and discuss any news or events that happen around the world. Twitter is a popular social media which effectively takes part in any mega event and is used before, during and after live events. Some tweets become trend based on their popularity. The trending tweets are classified by twitter into categories like: Sports (S), Entertainment (E), Politics (P), and Technology (T) which are used in our work. We have used context based meanings to further improve the trending tweet classification results. In this article, we have verified the twitter's automated classification results with manual classification by experts. Furthermore, we compared the classification results with SVM, Linear Regression, Logistic Regression and Naive Bayes implemented in Weka tool. We found that twitter's classifier works with almost 89% accuracy in our dataset. This work would contribute in improving misclassification of data of different categories on social media.

关键词： Twitter Sports Data mining Media Support vector machines

MPIgnite: An MPI-Like Language and prototype implementation for apache spark

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arXiv 2017年

作者： Morris, Brandon L. Skjellum, Anthony Auburn University Dept. of Computer Science and Software Engineering

Scale-out parallel processing based on MPI is a 25-year-old standard with at least another decade of preceding history of enabling technologies in the High Performance Comput-ing community. Newer frameworks such as MapReduce, Hadoop, and Spark represent industrial scalable comput-ing solutions that have received broad adoption because of their comparative simplicity of use, applicability to relevant problems, and ability to harness scalable, distributed re-sources. While MPI provides performance and portability, it lacks in productivity and fault tolerance. Likewise, Spark is a specific example of a current-generation MapReduce and data-parallel computing infrastructure that addresses those goals but in turn lacks peer communication support to al-low featherweight, highly scalable peer-to-peer data-parallel code sections. The key contribution of this paper is to demonstrate how to introduce the collective and point-to-point peer communica-tion concepts of MPI into a Spark environment. This is done in order to produce performance-portable, peer-oriented and group-oriented communication services while retaining the essential, desirable properties of Spark. Additional concepts of fault tolerance and productivity are considered. This ap-proach is offered in contrast to adding MapReduce frame-work as upper-middleware based on a traditional MPI im-plementation as baseline infrastructure. Copyright © 2017, The Authors. All rights reserved.

关键词： Fault tolerance

Bag-of-visual-words for signature-based multi-script document retrieval

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arXiv 2018年

作者： Mandal, Ranju Roy, Partha Pratim Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University QLD Australia Dept. of Computer Science & Engineering Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Software University of Technology Sydney Australia

An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a Support Vector Machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e. signature strokes) and the background spatial information (i.e. background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The 'Tobacco' [1] document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents and promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Support vector machines

N-gram and Neural Language Models for Discriminating Similar Languages

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arXiv 2017年

作者： Cianflone, Andre Kosseim, Leila Dept. of Computer Science & Software Engineering Concordia University

This paper describes our submission (named clac) to the 2016 Discriminating Similar Languages (DSL) shared task. We participated in the closed Sub-task 1 (Set A) with two separate machine learning techniques. The first approach is a character based Convolution Neural Network with a bidirectional long short term memory (BiLSTM) layer (CLSTM), which achieved an accuracy of 78.45% with minimal tuning. The second approach is a character-based n-gram model. This last approach achieved an accuracy of 88.45% which is close to the accuracy of 89.38% achieved by the best submission, and allowed us to rank #7 overall. Copyright © 2017, The Authors. All rights reserved.

关键词： Multilayer neural networks

Balancing different fitness levels in competitive exergames based on heart rate and performance 17

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Balancing different fitness levels in competitive exergames ...

29th Australian computer-Human Interaction Conference, OzCHI 2017

作者： Bayrak, Aslihan Tece Kumar, Rahul Tan, Jak AhMu, Devon Hohepa, Jordan Shaw, Lindsay A. Lutteroth, Christof Wünsche, Burkhard C. Dept. of Game Development Media Design School Auckland New Zealand Dept. of Software Engineering University of Auckland Auckland New Zealand Dept. of Computer Science University of Auckland Auckland New Zealand Dept. of Computer Science University of Bath Bath United Kingdom

ISBN: (纸本)9781450353793

Combining exercises with game elements (exergaming) is a promising approach to increase physical activity of users not intrinsically motivated to exercise. Multiplayer games are particularly promising, since they add a social (competitive or collaborative) component. However, exercising together can become frustrating if users have vastly different abilities. In this paper, we review existing approaches for balancing differing physical abilities of players in competitive exergames, and based on this, we propose a novel exertion-based balancing formula. In contrast to previous work, we evaluate exertion-based balancing formulas with a moderately complex gameplay and show that it results in closer competitive gameplay and a more enjoyable game experience. The proposed formula can be applied to a wide variety of exergames and has the potential to increase compliance and long-term use of exergames. © 2017 Association for Computing Machinery. All rights reserved.

关键词： Heart