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A Bayesian Network for the Analysis of Traffic Accidents in Peru 8

A Bayesian Network for the Analysis of Traffic Accidents in ...

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8th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Ugarte, Willy Alcantara-Zapata, Manuel Ayamamani-Choque, Leibnihtz Bances-Morales, Renzo Cabrera-Sanchez, Cristian Univ Peruana Ciencias Aplicadas UPC Lima Peru

ISBN: (纸本)9789897585739

Traffic accidents are a problem that affects the State and society, because they cause material damage, injuries and even the death of a person. This has led countries such as China, Switzerland and Australia to carry out studies using Bayesian networks to determine the main causes and, based on them, propose measures to reduce the number of traffic accidents. Following this trend, we, without having any expert knowledge on the subject, decided to analyze the data of traffic accidents on the Pan-American Highway in Lima, Peru. This analysis was done by means of directed graph learning with the Hill Climbing Search, Chow-Liu, K2, BIC and BDEU. In addition, we used a Bayesian estimator to calculate the conditional probability distribution for our dataset. This dataset contains observations from the years 2017 to 2019 and approximately 16 km of this highway. Our results show that it is possible to identify the possible causes of excess accidents in specific areas of the Pan-American Highway in certain shifts i.e., 32% of fatal accidents occur between 12 am and 7 pm in the Rimac district and of these 20% are due to pedestrians on the highway.

关键词： probabilistic graphical model Bayesian Network Graph Learning Traffic Accidents

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Inferring Correspondences from Multiple Sources for Microblog User Tags 3

Inferring Correspondences from Multiple Sources for Microblo...

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3rd National Conference on Social Media Processing (SMP)

作者： Tu, Cunchao Liu, Zhiyuan Sun, Maosong Tsinghua Univ Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9783662455579

Some microblog services encourage users to annotate themselves with multiple tags, indicating their attributes and interests. User tags play an important role for personalized recommendation and information retrieval. In order to better understand the semantics of user tags, we propose Tag Correspondence model (TCM) to identify complex correspondences of tags from the rich context of microblog users. In TCM, we divide the context of a microblog user into various sources (such as short messages, user profile, and neighbors). With a collection of users with annotated tags, TCM can automatically learn the correspondences of user tags from the multiple sources. With the learned correspondences, we are able to interpret implicit semantics of tags. Moreover, for the users who have not annotated any tags, TCM can suggest tags according to users' context information. Extensive experiments on a real-world dataset demonstrate that our method can efficiently identify correspondences of tags, which may eventually represent semantic meanings of tags.

关键词： User Tag Suggestion Tag Correspondence model probabilistic graphical model

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iGlasses: A Novel Recommendation System for Best-fit Glasses 16

iGlasses: A Novel Recommendation System for Best-fit Glasses

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39th International ACM SIGIR conference on Research and Development in Information Retrieval

作者： Gu, Xiaoling Shou, Lidan Peng, Pai Chen, Ke Wu, Sai Chen, Gang Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450340694

We demonstrate iGlasses, a novel recommendation system that accepts a frontal face photo as the input and returns the best-fit eyeglasses as the output. As conventional recommendation techniques such as collaborative filtering become inapplicable in the problem, we propose a new recommendation method which exploits the implicit matching rules between human faces and eyeglasses. We first define fine-grained attributes for human faces and frames of glasses respectively. Then, we develop a recommendation framework based on a probabilistic graphical model, which effectively captures the correlation among these fine-grained attributes. Ranking of the frames (glasses) is done by their similarity to the query facial attributes. Finally, we produce a synthesized image for the input face to demonstrate the visual effect when wearing the recommended glasses.

关键词： Eyeglasses Recommendation probabilistic graphical model

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A Novel Approach For Global Abnormal Event Detection in Multi-camera Surveillance System

A Novel Approach For Global Abnormal Event Detection in Mult...

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IEEE International Conference on Information and Automation 2015

作者： Guo, Huiwen Wu, Xinyu Wang, Huan Chen, Lixin Ou, Yongsheng Feng, Wei Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Beijing 100864 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synerg Syst Beijing 100864 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781467391047

In this paper, a novel global abnormal event detection algorithm is proposed for multiple disjoint synchronous camera network. We treat detecting unusual global events as discovering context-incoherent patterns through learning temporal dependencies between distributed local activities observed within and across camera views. Trajectories are firstly extracted using mean-shift approach in each camera view. As local activities are learned by applying clustering algorithm for trajectories, we model global event patterns using a probabilistic graphical model with different nodes representing entry/exit regions from different views and the directed links between nodes encoding their temporal dependencies. A novel two-stage structure learning algorithm is formulated to learn globally optimized temporal dependencies. Modified Dynamic Time Warping is used to learning the links in unobservable regions in camera network. Then, Monte Carlo(MC) algorithm is used to refine the structure and produce a final dependency structure. We validate the effectiveness of the proposed approach using a synthetic data set and videos captured from a camera network installed at a research institute.

关键词： Global Abnormal Event Detection Multi-camera Temporal Dependency probabilistic graphical model Dynamic Time Warping

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Privacy Preserving Data Publishing

Privacy Preserving Data Publishing

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作者： Zaobo He Georgia State University

学位级别：博士

Recent years have witnessed increasing interest among researchers in protecting individual privacy in the big data era, involving social media, genomics, and Internet of Things. Recent studies have revealed numerous privacy threats and privacy protection methodologies, that vary across a broad range of applications. To date, however, there exists no powerful methodologies in addressing challenges from: high-dimension data, high-correlation data and powerful attackers. In this dissertation, two critical problems will be investigated: the prospects and some challenges for elucidating the attack capabilities of attackers in mining individuals' private information; and methodologies that can be used to protect against such inference attacks, while guaranteeing significant data utility. First, this dissertation has proposed a series of works regarding inference attacks laying emphasis on protecting against powerful adversaries with auxiliary information. In the context of genomic data, data dimensions and computation feasibility is highly challenging in conducting data analysis. This dissertation proved that the proposed attack can effectively infer the values of the unknown SNPs and traits in linear complexity, which dramatically improve the computation cost compared with traditional methods with exponential computation cost. Second, putting differential privacy guarantee into high-dimension and high-correlation data remains a challenging problem, due to high-sensitivity, output scalability and signal-to-noise ratio. Consider there are tens-of-millions of genomes in a human DNA, it is infeasible for traditional methods to introduce noise to sanitize genomic data. This dissertation has proposed a series of works and demonstrated that the proposed differentially private method satisfies differential privacy; moreover, data utility is improved compared with the states of the arts by largely lowering data sensitivity. Third, putting privacy guarantee into social data publis

关键词： Inference Attack Data Sanitization Differential Privacy SNP-Trait Association Belief Propagation probabilistic graphical model

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Speeding Up Gibbs Sampling in probabilistic Optical Flow

Speeding Up Gibbs Sampling in Probabilistic Optical Flow

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作者： Dongzhen Piao Carnegie Mellon University

学位级别：博士

In today's machine learning research, probabilistic graphical models are used ex- tensively to model complicated systems with uncertainty, to help understanding of the problems, and to help inference and predict unknown events. For inference tasks, exact inference methods such as junction tree algorithms exist, but they su? er from expo- nential growth of cluster size and thus is not able to handle large and highly connected graphs. Approximate inference methods do not try to find exact probabilities, but rather give results that improve as algorithm runs. Gibbs sampling, as one of the approximate inference methods, has gained lots of traction and is used extensively in inference tasks, due to its ease of understanding and implementation. However, as problem size grows, even the faster algorithm needs a speed boost to meet application requirement. The number of variables in an application graphical model can range from tens of thousands to billions, depending on problem domain. The original sequential Gibbs sampling may not return satisfactory result in limited time. Thus, in this thesis, we investigate in ways to speed up Gibbs sampling. We will study ways to do better initialization, blocking variables to be sampled together, as well as using simulated annealing. These are the methods that modifies the algorithm itself. We will also investigate in ways to parallelize the algorithm. An algorithm is paral- lelizable if some steps do not depend on other steps, and we will find out such dependency in Gibbs sampling. We will discuss how the choice of di? erent hardware and software architecture will a? ect the parallelization result. We will use optical flow problem as an example to demonstrate the various speed up methods we investigated. An optical flow method tries to find out the movements of small image patches between two images in a temporal sequence. We demonstrate how we can model it using probabilistic graphical model, and solve it using Gibbs sampling. The r

关键词： Gibbs Sampling Optical Flow Parallelization Speed Up probabilistic graphical model Markov Random Field Simulated Annealing

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Analog Optical Computing Using Nonlinear Optics and Photonics

Analog Optical Computing Using Nonlinear Optics and Photonic...

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作者： Babaeian, Masoud The University of Arizona

学位级别：Ph.D.

Analog optical computing uses nonlinear optics and photonics to bring new approaches to attacking several important computational problems that current electronic computational platforms struggle to perform efficiently in terms of energy consumption and time. In this dissertation, Chapter 2, I will demonstrate optical implementation of probabilistic graphical models (PGMs) which are tools that are used to compute probability distributions over large and complex interacting variables. They have applications in social networks, speech recognition, artificial intelligence, machine learning, and many more areas. Our analysis indicates that the optical implementation provides substantial reduction of power and area compared to the electronic-based solutions as problems become large. For a network with 1 million nodes and 100 alphabet size, our proposed wavelength multiplexed all-optical implementation requires approximately 200 kilowatts (kW) of power as compared with 1.47 gigawatts (GW) and 1.7 megawatts (MW) using CPU-based and subthreshold VLSI-based systems, respectively. The optical-based solution is tolerant to shot noise and imperfections of optical modules used in the architecture as well. We also present an all-optical implementation of a PGM through the sum-product message passing algorithm (SPMPA) governed by a wavelength multiplexing architecture. As a proof-of-concept, we demonstrate the use of optics to solve a two node graphical model governed by SPMPA and successfully map the message passing algorithm onto photonics operations. The essential mathematical functions required for this algorithm, including multiplication and division, are implemented using nonlinear optics in thin film materials as well as bulk materials. The multiplication and division are demonstrated through a logarithm-summation-exponentiation operation and a pump-probe saturation process respective. The fundamental bottlenecks for the scalability of the presented scheme are discussed as

关键词： Coherent Ising Machine Doing math with laser Nonlinear optics Optical Computing Photonics probabilistic graphical model

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Continuous visual speech recognition for audio speech enhancement 40

Continuous visual speech recognition for audio speech enhanc...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Benhaim, Eric Sahbi, Hichem Vittey, Guillaume Telecom ParisTech CNRS-LTCI 46 rue Barrault Paris France Parrot S.A. 174 quai de Jemmapes Paris France

ISBN: (纸本)9781467369978

We introduce in this paper a novel non-blind speech enhancement procedure based on visual speech recognition (VSR). The latter is based on a generative process that analyzes sequences of talking faces and classifies them into visual speech units known as visemes. We use an effective graphical model able to segment and label a given sequence of talking faces into a sequence of visemes. Our model captures unary potential as well as pairwise interaction;the former models visual appearance of speech units while the latter models their interactions using boundary and visual language model activations. Experiments conducted on a standard challenging dataset, show that when feeding the results of VSR to the speech enhancement procedure, it clearly outperforms baseline blind methods as well as related work. © 2015 IEEE.

关键词： belief propagation model-based speech enhancement probabilistic graphical model Visual speech recognition

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Retail Product Recognition with a graphical Shelf model 25

Retail Product Recognition with a Graphical Shelf Model

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Baz, Ipek Yoruk, Erdem Cetin, Mujdat Sabanci Univ Muhendisl & Doga Bilimleri Fak Istanbul Turkey Vispera Bilgi Teknol Istanbul Turkey

ISBN: (纸本)9781509064946

Recently, retail product recognition has become an interesting computer vision research topic. The classification of products on shelves is a very challenging classification problem because many product classes are visually similar in terms of shape, color, texture, and metric size. In shelves, same or similar products are more likely to appear adjacent to each other and displayed in certain arrangements rather than at random. The arrangement of the products on the shelves has a spatial continuity both in brand and metric size. By using this context information, the co-occurrence of the products and the adjacency relations between the products can be statistically modeled. In this work, we present a context-aware hybrid classification system for the problem of fine-grained product class recognition. The proposed hybrid approach improves the accuracy of the context-free image classifiers, by combining them with a probabilistic graphical model based on Hidden Markov models. The fundamental goal of this paper is to use contextual relationships in retail shelves to improve accuracy of the product classifier.

关键词： Context-aware Classification probabilistic graphical model Hidden Markov model

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Preliminary Research on Continuous Conditional Random Fields in Predicting High-Dimensional Data 9

Preliminary Research on Continuous Conditional Random Fields...

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9th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Purbarani, Sumarsih Condroayu Sanabila, H. R. Wibisono, Ari Jatmiko, Wisnu Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781538631720

Predictions on time-series multivariate data, such as in the traffic flow dataset, have been largely accomplished through various approaches. The approach with conventional prediction algorithms, such as with Support Vector Machine (SVM), is only capable of accommodating predictions that are independent in each time unit. Hence, the sequential relationships in this time series data is hardly explored. Continuous Conditional Random Field (CCRF) is one of probabilistic graphical model (PGM) algorithms which can accommodate this problem. The neighboring aspects of sequential data such as in the time series data can be expressed by CCRF so that its predictions are more reliable. In this article, CCRF is implemented to increase the prediction ability of different baseline regressors, i.e. SVM and Extreme Learning Machine (ELM). Both algorithms are examined in two different datasets. The result shows that CCRF is superior in performance when examined using dataset with more attribute. This is validated by the increasing of the coefficient of correlation of the baseline up to 7.3% of significance.

关键词： prediction time series continuous conditional random field probabilistic graphical model

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