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Privacy issues in social networks: A brief survey

Privacy issues in social networks: A brief survey

引用

14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012

作者： Díaz, Irene Ralescu, Anca University of Oviedo Department of Computer Science E-33204 Oviedo Spain University of Cincinnati School of Computing Sciences and Informatics Machine Learning and Computational Intelligence Lab. Cincinnati OH 45221-0008 United States

ISBN: (纸本)9783642317231

Most social networks allow individuals to share their information with friends but also with unknown people. Therefore, in order to prevent unauthorized access to sensitive, private information, the study of privacy issues in social networks has become an important task. This paper provides a brief overview of the emerging research in privacy issues in social networks. © 2012 Springer-Verlag.

关键词： Data mining

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An evolutionary model for efficient transportation networks 7

An evolutionary model for efficient transportation networks

引用

7th International Conference on intelligent Human-machine Systems and Cybernetics, IHMSC 2015

作者： Huang, Ian Chen, Mei Yang, William Qin, Zengchang International School of Beijing Beijing China Department of Electrical Engineering University of Southern California United States Intelligent Computing and Machine Learning Lab Beihang University China School of Automation Science and Electrical Engineering Beihang University China

ISBN: (纸本)9781479986460

In this paper, we present a model to automatically generate efficient transportation networks given a simulated urban environment with predefined population distributions and other physical constraints. Based on the empirical analysis of different topological structures of networks, we found that that the efficiency of transportation networks heavily depends on the layout of the stations. The model uses the genetic algorithm to optimize the spatial distribution of stations. Then, the Minimum Spanning Tree is constructed upon which extra network routes are built to minimize average travel time within the constraint of a network length. Experimental studies of a simulated Beijing subway system showed that our model can generate a network that is 14% more efficient than the current subway system based on the Beijing population distribution and geography. This study can be extended to other transportation systems designs, and other communication networks. © 2015 IEEE.

关键词： Travel time

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks

Semantic Segmentation of Small Objects and Modeling of Uncer...

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Michael Kampffmeyer Arnt-Børre Salberg Robert Jenssen Machine Learning @ UiT Lab. Arctic Univ. of Norway Tromso Norway Norwegian Computing Center Machine Learning @ UiT Lab UiT-The Arctic University of Norway

We propose a deep Convolutional Neural Network (CNN) for land cover mapping in remote sensing images, with a focus on urban areas. In remote sensing, class imbalance represents often a problem for tasks like land cover mapping, as small objects get less prioritised in an effort to achieve the best overall accuracy. We propose a novel approach to achieve high overall accuracy, while still achieving good accuracy for small objects. Quantifying the uncertainty on a pixel scale is another challenge in remote sensing, especially when using CNNs. In this paper we use recent advances in measuring uncertainty for CNNs and evaluate their quality both qualitatively and quantitatively in a remote sensing context. We demonstrate our ideas on different deep architectures including patch-based and so-called pixel-to-pixel approaches, as well as their combination, by classifying each pixel in a set of aerial images covering Vaihingen, Germany. The results show that we obtain an overall classification accuracy of 87%. The corresponding F1- score for the small object class "car" is 80.6%, which is higher than state-of-the art for this dataset.

关键词： Image segmentation Remote sensing Uncertainty Semantics Automobiles Computer architecture Training

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TuneRank Model for Main Melody Extraction from Multi-part Musical Scores

TuneRank Model for Main Melody Extraction from Multi-part Mu...

引用

International Conference on intelligent Human-machine Systems and Cybernetics, IHMSC

作者： Hanqing Zhao Zengchang Qin École d'Ingénieur Généraliste Beihang University Beijing China Intelligent Computing and Machine Learning Lab. Beihang University China

ISBN: (纸本)9781479949540

A musical part is a strand of music played by an individual instrument within a larger music work, main melody is a single musical part which consists of the most significant melodic elements of multi-part musical score. This part is typically related to dissonance between musical parts and musical instruments. In this paper, we propose a novel model for extracting the main melody from multi-part musical scores. This model is referred to as the TuneRank model that has the conceptually similar idea of the PageRank model. If each musical note can be considered like a web page in the Internet, and the dissonance value between two notes is like the quantity of links between two web pages. The TuneRank (rank of becoming main melody) of each note is calculated using Markov transition probability. This model is tested on the ECPK4 database. By comparing to the previous work, we find that this note-based model is more effective for processing scores containing main melody in multiple parts. Also, the accuracy does not change with the increase of the number of parts. In general, this model can be used for extracting the single-part main melody of digital musical scores.

关键词： Tuners Accuracy Feature extraction Probability Educational institutions Internet

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Stock volatility prediction using recurrent neural networks with sentiment analysis 30th

Stock volatility prediction using recurrent neural networks ...

引用

30th International Conference on Industrial, Engineering, and Other Applications of Applied intelligent Systems, IEA/AIE 2017

作者： Liu, Yifan Qin, Zengchang Li, Pengyu Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9783319600413

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have lab.led the sentiment of the online financial posts and make the dataset public availab.e for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators. © Springer International Publishing AG 2017.

关键词： Forecasting

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Protein-protein binding affinity prediction based on an SVR ensemble

Protein-protein binding affinity prediction based on an SVR ...

引用

8th International Conference on intelligent computing Technology, ICIC 2012

作者： Li, Xueling Zhu, Min Li, Xiaolai Wang, Hong-Qiang Wang, Shulin Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei Anhui 230031 China Robot Sensor and Human-Machine Interaction Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei Anhui 230031 China School of Computer and Communication Hunan University Changsha Hunan 410082 China

ISBN: (纸本)9783642315879

Accurately predicting generic protein-protein binding affinities (PPBA) is essential to analyze the outputs of protein docking and may help infer real status of cellular protein-protein interaction sub-networks. However, accurate PPBA prediction is still extremely challenging. machine learning methods are promising to address this problem. We propose a two-layer support vector regression (TLSVR) model to implicitly capture binding contributions that are hard to explicitly model. The TLSVR circumvents both the descriptor compatibility problem and the need for problematic modeling assumptions. Input features for TLSVR in first layer are scores of 2209 interacting atom pairs within each distance bin. The base SVRs are combined by the second layer to infer the final affinities. Leave-one-out validation on our heterogeneous data shows that the TLSVR method obtains a very good result of R=0.80 and SD=1.32 with real affinities. Comparison experiment further demonstrates that TLSVR is superior to the previous state-of-art methods in predicting generic PPBA. © 2012 Springer-Verlag.

关键词： Proteins

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A Bayesian model of game decomposition 30th

A Bayesian model of game decomposition

引用

30th International Conference on Industrial, Engineering, and Other Applications of Applied intelligent Systems, IEA/AIE 2017

作者： Zhao, Hanqing Qin, Zengchang Liu, Weijia Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China École Centrale de Pékin Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9783319600413

In this paper, we propose a Bayesian probabilistic model to describe collective behavior generated by a finite number of agents competing for limited resources. In this model, the strategy for each agent is a binary choice in the Minority Game and it can be modeled by a Binomial distribution with a Beta prior. The strategy of an agent can be learned given a sequence of historical choices by using Bayesian inference. Aggregated micro-level choices constitute the observable time series data in macro-level, therefore, this can be regarded as a machine learning model for time series prediction. To verify the effectiveness of the new model, we conduct a series of experiments on artificial data and real-world stock price data. Experimental results demonstrate the new proposed model has a better performance comparing to a genetic algorithm based decomposition model. © Springer International Publishing AG 2017.

关键词： Genetic algorithms

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A Lightweight Network Model For Video Frame Interpolation Using Spatial Pyramids

A Lightweight Network Model For Video Frame Interpolation Us...

引用

IEEE International Conference on Image Processing

作者： Jiankai Zhuang Zengchang Qin Jialu Chen Tao Wan Intelligent Computing and Machine Learning Lab School of ASEE Beihang University China School of BSME Beihang University China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

In recent years, deep learning based video frame interpolation methods have shown impressive results in handling occlusion, blur and large motion. However, they are usually very heavy in terms of model size, and they hardly to be employed in i.e. mobile phones or other portable devices with limited computing power. To address the problem, we propose light-weighted Spatial Pyramid Frame Interpolation Network (SPFIN), a hierarchical network in a coarse-to-fine approach to reconstruct frames. At each pyramid level, we apply two light sub-networks to model optical flow and visibility mask instead of commonly used U-Net architecture. The flow and mask are up-sampled and optimized progressively. Finally, the intermediate frame is formed by linearly blending warped frames and masks. Experimental results on two benchmark problems show that our model has the smallest size, but better or comparable performance comparing to existing state-of-the art models.

关键词： Interpolation Computational modeling Estimation Optical transmitters Training machine learning Integrated optics

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A HSC-based sample selection method for Support Vector machine

A HSC-based sample selection method for Support Vector Machi...

引用

International Conference on machine learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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