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A Novel Approach to Avoid Selfish Nodes During Allocation of data Items in MANETs

A Novel Approach to Avoid Selfish Nodes During Allocation of...

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international conference on advanced computing and Intelligent Engineering (ICACIE)

作者： Samal, Satyashree Mishra, Manas Ranjan Pati, Bibudhendu Panigrahi, Chhabi Rani Sarkar, Joy Lal CV Raman Coll Engn Dept Comp Sci & Engn Mahura India CV Raman Coll Engn Dept Informat Technol Mahura India

ISBN: (纸本)9789811068720;9789811068713

Performance of mobile ad hoc networks (MANETs) increases by the cooperation of mobile nodes among themselves. In MANETs, mobile nodes share their resources such as battery power, memory, and bandwidth. But, in the real world some nodes do not want to contribute their resources either fully or not at all and thereby storing their resources for future use. So these nodes behave as selfish due to their resource constraint. In this work, we propose an approach for detection of the selfish nodes in MANET and avoid them during the data distribution process when there is Sybil attack by considering the routing optimality, checking optimality, and forwarding optimality. The analysis of the simulation results indicate that the network performance has been increased as compared to the traditional network in terms of data accessibility and hence decreasing overall selfishness alarm after detecting the selfish nodes.

关键词： MANET Sybil attack Selfish node data accessibility

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Towards Distributed Cyberinfrastructure for Smart Cities using Big data and Deep Learning Technologies 38

Towards Distributed Cyberinfrastructure for Smart Cities usi...

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38th IEEE international conference on Distributed computing Systems (ICDCS)

作者： Shams, Shayan Goswami, Sayan Lee, Kisung Yang, Seungwon Park, Seung-Jong Louisiana State Univ LSU Div Comp Sci & Engn Baton Rouge LA 70803 USA Louisiana State Univ LSU Sch Lib & Informat Sci Baton Rouge LA 70803 USA Louisiana State Univ LSU Ctr Computat & Technol Baton Rouge LA 70803 USA

ISBN: (纸本)9781538668719

Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure for effectively integrating such data to generate more value and gain insights is still not well-addressed in the research literature. In this paper, we present our current efforts and ultimate vision to build distributed cyberinfrastructure which integrates big data and deep learning technologies with a variety of data for enhancing public safety and livability in cites. We also introduce several methodologies and applications that we are developing on top of the cyberinfrastructure to support diverse community stakeholders in cities.

关键词： Urban areas Machine learning Big data Distributed databases Servers Cameras Videos

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Accelerating CNN Algorithm with Fine-grained dataflow Architectures 20

Accelerating CNN Algorithm with Fine-grained Dataflow Archit...

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20th IEEE international conference on High Performance computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on data Science and Systems (DSS)

作者： Xiang, Taoran Feng, Yujing Ye, Xiaochun Tan, Xu Li, Wenming Zhu, Yatao Wu, Meng Zhang, Hao Fan, Dongrui Chinese Acad Sci ICT State Key Lab Comp Architecture Beijing Peoples R China UCAS Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538666142

Convolutional Neural Network(CNN) is a hot and state-of-the-art algorithm which is widely used in applications such as face recognition, intelligent monitoring, image recognition and text recognition. Because of its high computational complexity, many efficient hardware accelerators have been proposed to exploit high degree of parallel processing for CNN. However, accelerators which are implemented on FPGAs and ASICs usually sacrifice generality for higher performance and lower power consumption. Other accelerators, such as GPUs, are general enough, but they lead to higher power consumption. Fine-grained dataflow architectures, which break conventional Von Neumann architectures, show natural advantages in processing CNN-like algorithms with high computational efficiency and low power consumption. At the same time, it remains broadly applicable and adaptable. In this paper, we propose a scheme for implementing and optimizing CNN on fine-grained dataflow architecture based accelerators. The experiment results reveal that by using our scheme, the performance of AlexNet running on the dataflow accelerator is 3.11x higher than that on NVIDIA Tesla K80, and the power consumption of our hardware is 8.52x lower than that of K80.

关键词： fine-grained dataflow Convolutional Neural Network general accelerator data reuse high parallel

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Last developments of the INFN CNAF Long Term data Preservation (LTDP) project: the CDF data recover and safekeeping 18

Last developments of the INFN CNAF Long Term Data Preservati...

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18th international Workshop on advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Ricci, P. P. Cavalli, A. Dal Pra, S. Falabella, A. Fattibene, E. Pezzi, M. Amerio, S. INFN CNAF Viale Berti Pichat 6-2 I-40127 Bologna Italy INFN Sez Padova Via Marzolo 8 I-35131 Padua Italy

The INFN CNAF Tier-1 has become the Italian national data center for the INFN computing activities since 2005. As one of the reference sites for data storage and computing provider in the High Energy Physics (HEP) community it offers resources to all the four LHC experiments and many other HEP and non-HEP collaborations. The CDF experiment has used the INFN Tier-1 resources for many years and, after the end of data taking in 2011, it faced the challenge to both preserve the large amount of scientific data produced and give the possibility to access and reuse the whole information in the future using the specific computing model. For this reason starting from the end of 2012 the CDF Italian collaboration, together with the INFN CNAF and Fermilab (FNAL), introduced a Long Term data Preservation (LTDP) project with the purpose of preserving and sharing all the CDF data and the related analysis framework and knowledge. This is particularly challenging since part of the software releases is no longer supported and the amount of data to be preserved is rather large. The first objective of the collaboration was the copy of all the CDF RUN-2 raw data and user level ntuples (about 4 PB) from FNAL to the INFN CNAF tape library backend using a dedicated network link. This task was successfully accomplished during the last years and, in addition, a system to implement regular integrity check of data has been developed. This system ensures that all the data are completely accessible and it can automatically retrieve an identical copy of problematic or corrupted file from the original dataset at FNAL. The setup of a dedicated software framework, which allows users to access and analyse the data with the complete CDF analysis chain, was also carried out with the addition of users and system administrators detailed documentation for the long-term future. Furthermore a second and more ambitious objective emerged during 2016 with a feasibility study for reading the first CDF RUN-1 da

关键词： High energy physics

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Developing knowledge-based systems using data mining techniques for advising secondary school students in field of interest selection 23rd

Developing knowledge-based systems using data mining techniq...

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23rd international conference on database Systems for advanced Applications, DASFAA 2018

作者： Yitagesu, Sofonias Feng, Zhiyong Meshesha, Million Mekuria, Getachew Yasin, Muhammad Qasim School of Computer Software Tianjin University Tianjin300350 China Tianjins Key Laboratory of Cognitive Computing and Application Tianjin300350 China School of Informatics Addis Ababa University Addis Ababa Ethiopia College of Computing Debre Berhan University Debre Berhan Ethiopia

ISBN: (纸本)9783319914541

Ethiopia gives a highly emphasis on a secondary school and reform program of impressive expansion. In doing this, students interest towards the fields they are assigned to needs to be taken into consideration. This has been put into practice when the Ministry of Education and preparatory schools have assigned students in fields of studies based on their performance at secondary schools. However, they used only the students grade 10 Ethiopian General School Leaving Certificate Examination result to assign them. The objective of this study is to develop a knowledge-based systems using machine learning (data mining) techniques that consults the students in their field of study selection process. In this study, the hybrid model that was developed for academic research is used. To build the predictive model, 9364 sample students data from selected secondary schools are used. The sample data is preprocessed for missing values, outliers, noisy and errors. Then the model is experimented using decision tree (j48) and rule induction (PART) algorithms. In this study as compared to j48, the PART unpruned decision list algorithm has 98.003% predictive performance. Thus, the knowledge discovered with this algorithm is further used to build the knowledge-based systems. Hence, the Java program is used to integrate data mining results to knowledge-based systems. As a result, the developed knowledge based-systems is used to predict students field of study based on their performance at secondary school. The study concludes that, to build the accurate knowledge-based systems discovering knowledge using data mining techniques is significant. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Knowledge based systems

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Towards the Big data in Official Statistics: An Analytic Service Framework for Distributed Multiple Sourced Heterogeneous datasets 18

Towards the Big Data in Official Statistics: An Analytic Ser...

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2nd international conference on Computer Science and Application Engineering (CSAE)

作者： Zhao, Zhuo Li, Xingying Li, Shanzi Wu, Yixuan Zhao, Xin Beijing Normal Univ 18 Jinfeng Rd Tangjiawan Zhuhai Peoples R China

ISBN: (纸本)9781450365123

High volumes(1) of business data is continuously produced by different kinds of information system, which provides big values for the official statistics. However, it is not easy to leverage big volume of business data for the statistical analysis under existing technologies, since they are generally distributed multiple sourced heterogeneous data set. In this paper, we first present the problem scenario and discuss in details the challenges confronting with the problem. Then, we propose an analytical framework for the distributed multiple sourced heterogeneous data set based on the service oriented architecture. Finally, we present a prototype of elementary statistical services for the primary data analysis tasks based on the proposed analytic service framework.

关键词： Big data official statistics web services data mining distributed computing multiple sourced dataset

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Different ways of research on pricing done math model base on big data

Different ways of research on pricing done math model base o...

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international conference on Mechatronics and Intelligent Robotics, ICMIR 2018

作者： Yang, Chenghui Peng, Xing Luo, Zhongfu Liu, Zeyi College of Electrical Engineering Northwest Minzu University No. 1 Northwest Xincun LanzhouGansu730030 China China National Institute of Information Research No. 1 Northwest Xincun LanzhouGansu730030 China School of Automation and Electrical Engineering Lanzhou Jiaotong University No. 188 AnNing West Rode LanzhouGansu730070 China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing No. 188 AnNing West Rode LanzhouGansu730070 China School of Civil Engineering Northwest Minzu University No. 1 Northwest Xincun LanzhouGansu730030 China

ISBN: (纸本)9783030002138

The use of MATLAB, EXCEL and other software for the classification of data presentment, the completion of the task, through the comparison between the region and the price to find out the reasons for the task is not completed. Combined with the workflow scheduling framework, we take the solution of releasing crowdsourced packages to APP members by combining the tasks together, and combining with the operation flow of crowdsourcing mode, and using the reverse-oriented pricing method to get a new pricing method. Determine the optimal solution, come to the unfinished task area new pricing, improve the completion of the task rate, solve the "photo shoot money" APP task pricing problem. © Springer Nature Switzerland AG 2019.

关键词： MATLAB

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Adaptive Multi-bit Image Steganography Using Pixel-Pair Differential Approach

Adaptive Multi-bit Image Steganography Using Pixel-Pair Diff...

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international conference on advanced computing and Intelligent Engineering (ICACIE)

作者： Ghosh, Uttiya Burman, Debanjan Maity, Smritikana Mukherjee, Imon St Thomas Coll Engn & Technol Dept Comp Sci & Engn Kolkata 700023 India Indian Inst Informat Technol Dept Comp Sci & Engn Kalyani 741235 W Bengal India

ISBN: (纸本)9789811068720;9789811068713

With the increase of communication over Internet, the issue of security has become an important factor. Steganography is a consequence of the increasing degradation of reliability. The roots of steganography lie in ancient Greek civilization. With time, steganography has moved a long way in the path of advancement. It started off with least significant bit (LSB) embedding which mainly focussed on data security. With time, many algorithms have been designed using multi-bit steganography which takes into account both security and capacity of data embedded. In this paper, a new technique has been introduced where various parameters determine the number of bits embedded. This helps to improve the robustness of this method. We show empirically that our method withstands the statistical attacks and benchmark.

关键词： Multi-bit steganography Information hiding Spatial domain data security

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SSDMV: Semi-supervised Deep Social Spammer Detection by Multi-View data Fusion 18

SSDMV: Semi-supervised Deep Social Spammer Detection by Mult...

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18th IEEE international conference on data mining Workshops (ICDMW)

作者： Li, Chaozhuo Wang, Senzhang He, Lifang Yu, Philip S. Liang, Yanbo Li, Zhoujun Beihang Univ Beijing Peoples R China Nanjing Univ Aeronaut & Astronaut Beijing Peoples R China Cornell Univ New York NY 10021 USA Fudan Univ Shanghai Peoples R China Univ Illinois Chicago IL 60680 USA Hortonworks Inc San Jose CA USA

ISBN: (纸本)9781538691595

The explosive use of social media makes it a popular platform for malicious users, known as social spammers, to overwhelm legitimate users with unwanted content. Most existing social spammer detection approaches are supervised and need a large number of manually labeled data for training, which is infeasible in practice. To address this issue, some semi-supervised models are proposed by incorporating side information such as user profiles and posted tweets. However, these shallow models are not effective to deeply learn the desirable user representations for spammer detection, and the multi-view data are usually loosely coupled without considering their correlations. In this paper, we propose a Semi-Supervised Deep social spammer detection model by Multi-View data fusion (SSDMV). The insight is that we aim to extensively learn the task-relevant discriminative representations for users to address the challenge of annotation scarcity. Under a unified semi-supervised learning framework, we first design a deep multi-view feature learning module which fuses information from different views, and then propose a label inference module to predict labels for users. The mutual refinement between the two modules ensures SSDMV to be able to both generate high quality features and make accurate predictions. Empirically, we evaluate SSDMV over two real social network datasets on three tasks, and the results demonstrate that SSDMV significantly outperforms the state-of-the-art methods.

关键词： Social Spammer Detection Deep Learning Semi-supervised Learning

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An efficient way to find frequent patterns using graph mining and network analysis techniques on United States airports network 1st

An efficient way to find frequent patterns using graph minin...

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1st international conference on Smart computing and Informatics, SCI 2016

作者： Joshi, Anant Bansal, Abhay Sai Sabitha, A. Choudhury, Tanupriya Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh NoidaUttar Pradesh India

ISBN: (纸本)9789811055461

We are currently in the Information Age where massive amounts of data is being collected and analyzed to find interesting and frequent patterns. The need for mining data has been steadily increasing over the past few years. Graphs are one of the best studied data structures in the fields of mathematics and computer science. And due to this, in the recent years graph-based data mining has become quite popular. Graph data mining uses the graph nodes and the links between them to represent the entities, their relationships with other entities and their attributes and discovers interesting patterns in the graphs. Transportation networks are networks of routes from one location to another through various modes of travel. In this article, we use a transportation network of airports in United states of America and apply graph data mining techniques and network analysis techniques on US airports and flights datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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