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ISS: An iterative scrubbing strategy for improving memory reliability against MBU 2nd

ISS: An iterative scrubbing strategy for improving memory re...

引用

2nd International Conference on Human Centered Computing, HCC 2016

作者： Wang, Hui Wang, Yun Key Laboratory of Computer Network and Information Integration MOE Department of Computer Science and Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9783319318530

As technology scales, multiple bit upsets (MBUs) have shown prominent effect, thus affecting the reliability of memory to a great extent. In order to mitigate MBUs errors, interleaving schemes together with single error correction (SEC) codes can be used to provide the greatest protection for advanced computer memories. In this paper, an algorithm of iterative scrubbing strategy (ISS) is proposed for the optimal interleaving distance (ID), which should be maximized under some conditions. The proposed algorithm should keep the complexity and the area overhead of designing ID as low as possible without compromising memory reliability. The key principle is to take advantage of the locality of MBU errors and to realize efficient scrubbing. The efficiency of the proposed approach will be compared with conventional strategy. © Springer International Publishing Switzerland 2016.

关键词： Reliability

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RBAS: A real-Time user behavior analysis system for Internet TV in cloud computing 16

RBAS: A real-Time user behavior analysis system for Internet...

引用

11th International Conference on Future Internet Technologies, CFI 2016

作者： Zhu, Chengang Cheng, Guang Guo, Xiaojun Wang, Yuxiang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

ISBN: (纸本)9781450341813

The characteristic of Internet TV user behavior is quite essential for designers to optimize resource schedule and improve user experience. With the rapid development of Internet, both Internet TV users and STB (set top boxes) models are booming. This brings a large amount of behavior data which requires matching computing and storage resource to process. Therefore, scalable Internet TV user behavior analysis becomes more difficult. As a solution, cloud computing framework such as Hive is emerged. But limited by performance, it's not an appropriate choice for interactive analysis or real-Time data exploration. In this paper, we present a real-Time Internet TV user behavior analysis system with advantages of high concurrency, low latency and good transportability. Firstly, we design an event capture scheme, consisted of agents embedded in STBs and capture server clusters, to capture every manipulation performed by users. Secondly, we develop a SQL-on-Hadoop engine with distributed transactional management to decrease the response time. The engine has excellent query performance and ability to interactively query various data sources in different Hadoop formats. Lastly, we evaluate RBAS in a commercial Internet TV platform of 16 million registered users. The results show that, with a 32-node cluster, the system can effectively process 10.2 TB of behavior data every day, which is about 40x faster than original Hive-based system. © Copyright 2016 ACM.

关键词： Cloud computing

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Enabling fast recovery from unanticipated data access failure in named data networking 16

Enabling fast recovery from unanticipated data access failur...

引用

11th International Conference on Future Internet Technologies, CFI 2016

作者： Hu, Xiaoyan Gong, Jian School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Jiangsu Provincial Key Laboratory of Computer Network Technology China

ISBN: (纸本)9781450341813

We show that an aggregated Interest in Named Data networking (NDN) may fail to retrieve desired data as the Interest sent upstream in advance is recognized as a duplicate one due to its multipath forwarding and dropped by an upstream node. Furthermore, we introduce NDRUDAF, a mechanism based on negative acknowledgement for the router that aggregates the Interests to detect such unanticipated data access failure and assist the requester whose Interest is aggregated fast recovering from the failure. We qualitatively analyze the performance of the NDN with our proposal NDRUDAF and compare its performance with that of the present NDN. NDRUDAF improves the system performance in case of the unanticipated data access failure in terms of data access delay and network resource utilization efficiency at routers. © Copyright 2016 ACM.

关键词： Routers

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Identification of critical variables for soft error detection 2nd

Identification of critical variables for soft error detectio...

引用

2nd International Conference on Human Centered Computing, HCC 2016

作者： Ma, Junchi Wang, Yun School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

ISBN: (纸本)9783319318530

As process technology scales, electronic devices become more susceptible to soft error induced by radiation. Silent data corruption occurs without any symptoms and is the most severe result incurred by soft error. Duplication is the effective way to protect the program from soft error, but it has high overhead which is increasingly unacceptable for applications. Duplicating critical variables only can significantly reduce the overhead. This paper introduces an approach that identifying the variables which are critical to the execution of the program. We apply Dynamic Dependence Graph to analyze the propagation between instructions and find out the propagation path. The criticality of memory location is characterized and the criticality of each variable is then calculated. Fault injections show that with our approach the duplication achieves high detection rate with low overhead. © Springer International Publishing Switzerland 2016.

关键词： Application programs

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Logistic boosting regression for label distribution learning?

Logistic boosting regression for label distribution learning...

引用

2016 IEEE Conference on computer Vision and Pattern Recognition, CVPR 2016

作者： Xing, Chao Geng, Xin Xue, Hui Key Lab of Computer Network and Information Integration Ministry of Education School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9781467388511

Label Distribution Learning (LDL) is a general learning framework which includes both single label and multi-label learning as its special cases. One of the main assumptions made in traditional LDL algorithms is the derivation of the parametric model as the maximum entropy model. While it is a reasonable assumption without additional information, there is no particular evidence supporting it in the problem of LDL. Alternatively, using a general LDL model family to approximate this parametric model can avoid the potential influence of the specific model. In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint. For each step, we can fit individual weighted regression function (base learner) to realize the optimization gradually. The base learners are chosen as weighted regression tree and vector tree, which constitute two algorithms named LDLogitBoost and AOSO-LDLogitBoost in this paper. Experiments on facial expression recognition, crowd opinion prediction on movies and apparent age estimation show that LDLogitBoost and AOSO-LDLogitBoost can achieve better performance than traditional LDL algorithms as well as other LogitBoost algorithms.

关键词： Face recognition

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Mining opinion polarity from multilingual song lyrics 2nd

Mining opinion polarity from multilingual song lyrics

引用

2nd International Workshop on Worldwide Language Service Infrastructure, WLSI 2015

作者： Liu, Qian Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9783319314679

Song opinion is an important criterion when people organize and access songs. The ever growing amount of song data in the Web, which includes multilingual songs, calls for the development of automatic tools in classifying songs by opinion polarity. Sony lyric is a critical resource for song opinion classification. In this paper, we propose an approach to mine the opinion polarity of songs based on song lyrics in a multilingual environment. This approach is based on classification and translation. Firstly, we build monolingual opinion classifiers using supervised learning techniques for resource-rich languages, i.e., languages that are rich of labeled training data. However, it is difficult to build a classifier for a resource-rare language. In this case, we employ Language Grid, which is an infrastructure that is built on the top of the Internet, and provides easy-to-use services for multilingual translation, to bridge the gap between the resources in different languages. Song lyrics are translated from resource-rare languages into resource-rich languages, then the pre-trained monolingual opinion classifiers can be used to classify the translated unseen lyrics. To build effective monolingual opinion classifiers, we employ statistical information of song lyrics as features rather than individual words in the song lyrics. Experiments show that, our proposed approach performs better than two typical baseline approaches. © Springer International Publishing Switzerland 2016.

关键词： Classification (of information)

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Detecting bot-infected machines based on analyzing the similar periodic DNS queries

Detecting bot-infected machines based on analyzing the simil...

引用

International Conference on Computing, Management and Telecommunications, ComManTel 2015

作者： Tu, Truong Dinh Guang, Cheng Xin, Liang Yi School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Department of Information Technologies Tuyhoa Industrial College Phuyen Viet Nam

ISBN: (纸本)9781467365475

Modern botnets such as Zeus and Conficker com-monly utilize a technique called domain fluxing or a Domain Generation Algorithm (DGA) to generate a large number of pseudo-random domain names dynamically for botnet operators to control their bots. These botnets are becoming one of the most serious threats to the Internet security on a global scale. In this paper, we present a method based on analyzing the similar peri-odic time intervals series of DNS queries to identify DGA-bot infected machines. This method passively captures all DNS traffic from the gateway of monitor network. Firstly, we group queries of the same domain name that is requested by hosts, and then extracts time interval series between adjacent queries. Secondly, we measure the similar periodicity of DNS queries by calculating the squared Euclidean distance between each pair of their time interval series. Finally, we apply a hierarchical clustering algo-rithm to cluster high similar domain names. The experiment re-sults show that the domain names are generated by the same botnet or DGA would be grouped into the same cluster, thus all of the hosts that query to these clusters are marked as compro-mised hosts running a domain-flux botnet within monitor net-work. © 2015 IEEE.

关键词： Botnet

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BALLKNN:An Efficient and Scalable KNN based on Euclidean Similarity

BALLKNN:An Efficient and Scalable KNN based on Euclidean Sim...

引用

International Joint Conference on Neural networks

作者： Peipei Lv Peng Yang Yongqiang Dong Liang Gu Key Laboratory of Computer Network and Information Integration (Southeast University)

ISBN: (纸本)9781509006212

The K Nearest Neighbor (KNN) technique is very simple, highly efficient and effective in the field of text cate-gorization, pattern recognition, object recognition etc. However, its efficiency is challenged by Big Data, which is characterized with volume, variety and velocity. All these characters require a new KNN algorithm with better time complexity, which is lower than O(n) at least. Additionally, the characters witness that centralized variants executed in a single thread are no longer reasonable. In this paper, we propose an efficient and scalable strategy, which we called as BALLKNN, for KNN classifier. In BALLKNN, since computation of some samples are reduced by replacing complicated quadratic sum with simple subduction and comparison, time efficiency improvement is obtained;the disadvantage of dependency on global data structure is overcome with the help of temporary variable(s), BALLKNN can be implemented with multiple threads easily, and it scales well with the size of the dataset. Meanwhile, as all samples are handled, the classification accuracy is guaranteed different from traditional sample reduction methods. Furthermore, detailed theoretical analysis on relationship between dimensionality of input and time complexity is provided. Finally, extensive experiments in large real datasets, with tens or hundreds of thousands of records and up to 618 dimensions, have demonstrated the efficiency and scalability of our methods.

关键词： complexity classes Scalability Data structures big data THREADS Theoretical analysis temporary variables Euclidean sample reduction Dataset

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Calcified coronary plaques detection in CTA based-on automatic scale selection and fuzzy C means

Calcified coronary plaques detection in CTA based-on automat...

引用

2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Sun, Qiaoyu Yang, Guanyu Shu, Huazhong Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Department of Electric Information Engineering Huaihai Institute of Technology Lianyungang China Centre de Recherche en Information Bimédicale Sino-Français Southeast University Nanjing China

ISBN: (纸本)9781509003891

Coronary artery diseases are the most common type of heart disease. Early detection and quantification of coronary plaques is therefore of high interest. CTA has rapidly emerged, and is nowadays widely used in clinical practice. A calcification detection and quantification method is proposed, which can detect the calcium plaque and quantify the stenosis of coronary artery in CT images. Firstly, the responses of the DoG operator in the center on each slice of the artery are obtained to estimate the diameter of the vessel. Then, the slice is cropped according to the feature size. Secondly, the Fuzzy C-means is applied to classify the cropped image. Finally, the calcium plaques are extracted with their intensity and the stenosis of the artery caused by the calcified plaque is quantified according to the estimated cross-section area of the vessel. Experiments show that the proposed method can detect and quantify the calcium plaques accurately. © 2016 IEEE.

关键词： Calcium

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PIWD: A plugin-based framework for well-designed SPARQL 6

PIWD: A plugin-based framework for well-designed SPARQL

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6th Joint International Conference on Semantic Technology, JIST 2016

作者： Zhang, Xiaowang Song, Zhenyu Feng, Zhiyong Wang, Xin School of Computer Science and Technology Tianjin University Tianjin China School of Computer Software Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

ISBN: (纸本)9783319501116

In the real world datasets (e.g., DBpedia query log), queries built on well-designed patterns containing only AND and OPT operators (for short, WDAO-patterns) account for a large proportion among all SPARQL queries. In this paper, we present a plugin-based framework for all SELECT queries built on WDAO-patterns, named PIWD. The framework is based on a parse tree called well-designed AND-OPT tree (for short, WDAO-tree) whose leaves are basic graph patterns (BGP) and inner nodes are the OPT operators. We prove that for any WDAOpattern, its parse tree can be equivalently transformed into a WDAOtree. Based on the proposed framework, we can employ any query engine to evaluate BGP for evaluating queries built on WDAO-patterns in a convenient way. Theoretically, we can reduce the query evaluation of WDAO-patterns to subgraph homomorphism as well as BGP since the query evaluation of BGP is equivalent to subgraph homomorphism. Finally, our preliminary experiments on gStore and RDF-3X show that PIWD can answer all queries built on WDAO-patterns effectively and efficiently. © Springer International Publishing AG 2016.

关键词： Forestry

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