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3rd international conference for Convergence in Technology, I2CT 2018

作者： Suresh, Babu K.V. Vanama, Venkata Sai Krishna International Institute of Information Technology Lab for Spatial Informatics Hyderabad India Mumbai India

ISBN: (纸本)9781538642733

Forest fires are the most frequent phenomenon during the summer season in India, and especially in the hilly terrains of Uttarakhand forests. Remote sensing sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), advanced Very High Resolution Radiometer (AVHRR), advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Visible Infrared Imaging Radiometer Suite (VIIRS) with coarse spatial resolution on board different satellites were used to detect the forest fires across the world. Landsat-8 Operational Land Imager (OLI) data has the better spatial resolution (30m) as compared with the MODIS and VIIRS, therefore useful to detect the smaller fires. Nainital district in Uttarakhand state was severely affected by the massive forest fire events occurred during April-May, 2016. The main objective of the study is to identify the potential of Landsat-8 data in detecting the forest fire for varying topographic region like Nainital. Landsat-8 data acquired on 28th April 2016 and 1st May 2016 has been used in this study. The results obtained from Landsat-8 data are compared with the MODIS fire products and showed an improvement in the detection of small fires. © 2018 IEEE.

关键词： Landsat

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The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm: An Overview 4th

The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm...

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4th Euro-China conference on Intelligent data Analysis and Applications, ECC 2017

作者： Meng, Zhenyu Pan, Jeng-Shyang Li, Xiaoqing Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783319685267

QUasi-Affine TRansformation Evolution (QUATRE) algorithm is a new simple but powerful stochastic optimization algorithm proposed recently. The QUATRE algorithm aims to tackle the representational/positional bias inborn with DE algorithm and secures an overall better performance on commonly used conference of Evolutionary Computation (CEC) benchmark functions. Recently, several QUATRE variants have been already proposed since its inception in 2016 and performed very well on many benchmark functions. In this paper, we mainly have a brief overview of all these proposed QUATRE variants first and then make simple contrasts between these QUATRE variants and several state-of-the-art DE variants under CEC2013 test suites for real-parameter single objective optimization benchmark functions. Experiment results show that the movement trajectory of individuals in the QUATRE structure is much more efficient than DE structure on most of the tested benchmark functions. © 2018, Springer international Publishing AG.

关键词： Global optimization

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Machine Learning based Video Coding using data-driven Techniques and advanced Models

Machine Learning based Video Coding using Data-driven Techni...

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IEEE international conference on Cognitive Informatics

作者： Sam Kwong City University of Hong Kong Hong Kong

ISBN: (数字)9781728114194

ISBN: (纸本)9781728104966

In June 6th 2016, Cisco released the white paper [1], VNI Forecast and Methodology 2015–2020, reported that 82 percent of Internet traffic will come from video applications such as video surveillance, content delivery network, so on by 2020. It also reported that Internet video surveillance traffic nearly doubled, Virtual reality traffic quadrupled, TV grew 50 percent and similar increases for other applications in 2015. The annual global traffic will first time exceed the zettabyte (ZB;1000 exabytes[EB]) threshold in 2016, and will reach 2.3 ZB by 2020. It implies that 1.886ZB belongs to video data. Thus, in order to relieve the burden on video storage, streaming and other video services, researchers from the video community have developed a series of video coding standards. Among them, the most up-to-date is the High Efficiency Video Coding (HEVC) or H.265 standard, which has successfully halved the coding bits of its predecessor, H.264/AVC, without significant increase in perceived distortion. With the rapid growth of network transmission capacity, enjoying high definition video applications anytime and anywhere with mobile display terminals will be a desirable feature in the near future. Due to the lack of hardware computing power and limited bandwidth, lower complexity and higher compression efficiency video coding scheme are still desired. For higher video compression performance, the key optimization problems, mainly decision making and resource allocation problem, shall be solved. In this talk, I will present the most recent research results on machine learning and game theory based video coding. This is very different from the traditional approaches in video coding. We hope applying these intelligent techniques to vide coding could allow us to go further and have more choices in trading off between cost and resources.

关键词： Handheld computers Cognitive systems

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data mining and analytics for exploring bulgarian diabetic register 19th

Data mining and analytics for exploring bulgarian diabetic r...

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19th international conference on data Analytics and Management in data Intensive Domains, DAMDID/RCDL 2017

作者： Boytcheva, Svetla Angelova, Galia Angelov, Zhivko Tcharaktchiev, Dimitar Institute of Information and Communication Technologies Bulgarian Academy of Sciences 25A Acad. G. Bonchev Street Sofia1113 Bulgaria ADISS Lab Ltd. 4 Hristo Botev Blvd Sofia1463 Bulgaria University Specialized Hospital for Active Treatment of Endocrinology – Medical University Sofia 2 Zdrave Street Sofia1431 Bulgaria

ISBN: (纸本)9783319965529

This paper discusses the need of building diabetic registers in order to monitor the disease development and assess the prevention and treatment plans. The automatic generation of a nation-wide Diabetes Register in Bulgaria is presented, using outpatient records submitted to the National Health Insurance Fund in 2010–2014 and updated with data from outpatient records for 2015–2016. The construction relies on advanced automatic analysis of free clinical texts and business analytics technologies for storing, maintaining, searching, querying and analyzing data. Original frequent pattern mining algorithms enable to discover maximal frequent itemsets of simultaneous diseases for diabetic patients. We show how comorbidities, identified for patients in the prediabetes period, can help to define alerts about specific risk factors for Diabetes Mellitus type 2, and thus might contribute to prevention. We also claim that the synergy of modern analytics and data mining tools transforms a static archive of clinical patient records to a sophisticated knowledge discovery and prediction environment. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： data mining

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MIMiS: Minimally Intrusive mining of Smartphone User Behaviors 10

MIMiS: Minimally Intrusive Mining of Smartphone User Behavio...

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IEEE/ACM international conference on Advances in Social Networks Analysis and mining (ASONAM)

作者： Devineni, Pravallika Papalexakis, Evangelos E. Michalska, Kalina Faloutsos, Michalis Univ Calif Riverside Dept Comp Sci Riverside CA 92521 USA Univ Calif Riverside Dept Psychol Riverside CA 92521 USA

ISBN: (纸本)9781538660515

The proliferation of smartphones has lead researchers towards using them as an observational tool in psychological science. However, there is little effort towards protecting user privacy in these analyses. The overarching question of our work is: Given a set of sensitive user features, what is the minimum amount of information required to group similar users? Our contributions are two fold: we introduce privacy surfaces that combine sensitive user data at different levels of temporal granularity. Second, we introduce MIMiS, an unsupervised privacy-aware framework that clusters users as homogeneous groups with respect to their temporal signature. In addition, we explore the trade-off between intrusiveness and prediction accuracy. We extensively evaluate MIMiS on real data across a variety of privacy surfaces. MIMiS identified groups that are highly homogeneous w.r.t. user mental health scores and their academic performance.

关键词： Privacy Sensors data privacy Global Positioning System Wireless fidelity Tensile stress

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Optimized DPCluster Based on Iterative KNN 5

Optimized DPCluster Based on Iterative KNN

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5th IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Li, Hengyu He, Xu Yang, Wenyu Zhang, Lujia Huang, Qing Zou, Zhiqiang Nanjing Univ Posts & Telecommun Coll Comp Nanjing 210023 Jiangsu Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Bell Honors Sch Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781538660058

Density peaks clustering (DPCluster) algorithm published in the journal Science in 2014 is a novel clustering algorithm based on density, which is simple and efficient. However, in this algorithm, the selection of its parameter cut-off distance is subjective, which could lead to the poor accuracy of clustering results. Besides, the assignment strategy DPCluster proposes could not identify the datasets with manifold structure well. To overcome these defects, an optimized clustering algorithm (named as KIK-DPCluster) that joints K-distance graph with Iterative KNN (K-Nearest Neighbor algorithm) is proposed in this paper. We introduce the idea of K-distance graph to define the parameter in order to separate cluster cores and halos accurately. When assigning the points to the proper clusters, we divide the original strategy into three stages by combining the original algorithm with KNN and putting forward the thought of 'reuse of the existing label'. Firstly, the points that satisfy the constraint are assigned. Then, KNN is used to assign the rest points. Finally, we utilize the existing label information to re-determine the result by using KNN iteratively, which is similar to semi-supervised' algoritlun. It is presented that KNN algorithm plays the role of the assignment and the correction in the above process. Experiments on publicly available synthetic and real-world datasets show that the proposed clustering algorithm outperforms DPCluster when identifying the datasets with manifold structure. Besides, compared with the classic clustering algorithm, KIK-DPCluster increases accuracy of the clustering results by 13%similar to 14%. In the end, applying KIK-DPCluster to Weibo check-in dataset presents that the clustering result is consistent with actual situation, thus our algorithm has great practical value.

关键词： Clustering K-distance graph KNN Iterative Compute

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Digging Deep Inside: An Extended Analysis of SCHOLAT E-Learning data 3rd

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3rd international conference on Human Centered computing (HCC)

作者： Akram, Aftab Fu, Chengzhou Tang, Yong Jiang, Yuncheng Guo, Kun South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Univ Educ Lahore 54000 Pakistan Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Fujian Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350116 Fujian Peoples R China

ISBN: (纸本)9783319745213;9783319745206

More and more higher education institutions are adopting computer based learning management system to boost learning of the students. Networking and collaboration through social media platforms are vital realities. Learning is not merely limited to class rooms, which is now independent of location and time. Understanding how students learn in this realm is a mighty challenge for teaching professionals. Fortunately, data is abundantly available through learning management systems and social media platforms. Analyzing this vast data could give an insight into how learning is happening in these days. data mining techniques are vastly being used for this purpose. In this paper, we present a statistical analysis of e-leaning data obtained from SCHOLAT, a scholar oriented social networking system. The analysis aims at getting data oriented perspectives of learning, e.g., which factor to what extent impacts learning. The analysis revealed factors which positively or negatively affect learning achievement of the students, i.e., course final scores.

关键词： data mining Academic social network Learning management system Statistical analysis

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Guest Editor's Introduction to the Special Section on Social Network Security

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE computing 2018年第4期15卷 549-550页

作者： Qiu, Meikang Xiang, Yang Zhang, Yan Columbia Univ New York NY 10027 USA Swinburne Univ Technol Hawthorn Vic 3122 Australia Univ Oslo N-0315 Oslo Norway

The nine papers included in this special section focus on cyber security methods to protect social network services. The emerging paradigm of social network provides an enormous number of novel approaches to implementing advanced networking communications and data analysis schemes efficiently using existing datasets, networks, and infrastructure. Social networks have had a great impact on people’s daily life and global businesses, as has been addressed by recent research. However, the security issue is also a critical concern when adopting social network technologies in practice. Considering the uniqueness of social networks, the mechanism is now facing a variety of security challenges from multiple dimensions, such as mobile apps, wireless communication, cloud systems, big data, and security operations. Compared with traditional security issues, the applications of social networks are operated in a dynamic circumstance involving different internal and external inputs and factors, which requires new security mechanisms in distinct operational environments. The complexity of the technical implementations may result in unexpected consequences when adopting social network technologies. It is therefore important for current researchers and practitioners to address the security issues and seek out efficient ways to handle different hazards. For the purpose of preventing social network-based solutions from the threats of social networks, a variety of cyber security approaches or mechanism have been proposed.

关键词： Special Issues And Sections Social Network Services Computer Security data Privacy data Analysis Big data

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international conference on Intelligent computing and Applications 1st ed. 2018

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丛书名： Advances in Intelligent Systems and computing

2018年

作者： Subhransu Sekhar Dash Swagatam Das Bijaya Ketan Panigrahi

ISBN: (数字)9789811055201

ISBN: (纸本)9789811055195

The bookis a collection of best papers presented in international conference on Intelligent computing and Applications (ICICA 2016) organized by Department of Computer Engineering, D. Y. Patil College of Engineering, Pune, India during 20-22 December 2016. The book presents original work, information, techniques and applications in the field of computational intelligence,power and computing technology. This volume also talks about image language processing, computer vision and pattern recognition, machine learning, data mining and computational life sciences, management of data including Big data and analytics, distributed and mobile systems including grid and cloud infrastructure.

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Outlier detection over distributed trajectory streams

Outlier detection over distributed trajectory streams

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2018 SIAM international conference on data mining, SDM 2018

作者： Mao, Jiali Sun, Pengda Jin, Cheqing Zhou, Aoying School of Data Science and Engineering East China Normal University School of Computing China West Normal University China School of Computer Science and Software Engineering East China Normal University China

The wide deployments of GPS-embedded devices have produced multiple rapid voluminous trajectory streams, which needs to be analyzed to extract abnormal behaviors of moving objects in real-time. To date, outlier detection over distributed trajectory streams has not received enough focuses due to the constraint factors like skewness distribution and evolving nature of trajectory data, and on-the-y execution requirement with minimal communication cost. In this paper, we present the first scalable decentralized outlier detection framework over distributed trajectory streams, called ODDTS. It consists of remote site processing and co-ordinator processing, with the aim of continuously provid-ing feature-grouping based outliers detection over distribut-ed trajectory streams. Extensive experiments over real data demonstrate high detecting validity, less communication cost and linear scalability of ODDTS method for online identify-ing outliers upon distributed trajectory streams. © 2018 by SIAM.

关键词： Scalability

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