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3rd Annual International Conference on Human System Interaction (HSI)

作者： Yin, GuoQing Bruckner, Dietmar Vienna Univ Technol Inst Comp Technol Vienna Austria

ISBN: (纸本)9781424475629

In an intelligent environment one important task is to observe and analyze person's daily activities. Through analyzing the corresponding time series sensor data the person's daily activity model should be build. To build such a model some problems have to be overcome: the sensor data count increase sharp with time and the distribution of the data is dynamically according the person's daily activities. In an Ambient Assisted Living (AAL) project we handle this kind of time series sensor data from a motion detector. At first we reduce the data count through a predefined threshold value and build data "states" in time interval. Secondly, we analyze the states using a hidden Markov model, the forward algorithm, and the Viterbi algorithm to build the person's daily activity model. To test the correctness of the model some special and random day's activities routine will be given.

关键词： forward algorithm hidden Markov model (HMM) intelligent environment Viterbi algorithm

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A Quantitative Situation Awareness Approach for CBTC Systems based on Multi-dimensional Gaussian Hidden Markov Model

A Quantitative Situation Awareness Approach for CBTC Systems...

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Chinese Automation Congress (CAC)

作者： Zhang Kanghao Wang Hongwei Cui Dongliang Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Beijing Jiaotong Univ Natl Res Ctr Railway Softy Assessment Beijing Peoples R China Northeast Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176871

Communication-based train control (CBTC) is the main method of railway signaling systems applying wireless communication to improve capacity by realizing the moving block. Due to the large adoption of Commercial Off-The-Shelf and the multiple interconnections with other service systems of railways, CBTC systems are facing increasing and severe information security challenges. However, there have been few studies highlighting the situation awareness of CBTC systems. This paper is intended to achieve the information security situation of CBTC systems quantitatively based on mass audit data, where the four dynamic features are both considered with the Gaussian Hidden Markov Model, the dynamic performance of critical equipment of CBTC systems is described. Baum-Welch algorithm is applied to obtain the optimal parameter of the model. Then, the forward algorithm is adopted to determine the probability distribution of hidden states. Moreover, we develop a method for an integrated SA value of CBTC systems based on entropy weight. The simulation results show the availability of the proposed approach.

关键词： situation awareness multi-dimensional gaussian hidden markov model forward algorithm

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Active Listening and Interaction Using Index of Co-Incidence and Birthday Problem with Hidden Markov Models 2

Active Listening and Interaction Using Index of Co-Incidence...

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2nd International Conference on Cognitive Computing and Information Processing (CCIP)

作者： Mohanesh, B. M. Nandini, B. M. Anusuya, M. A. Natl Inst Engn Mysuru Karnataka India Sri Jayachamarajendra Coll Engn Mysuru Karnataka India

ISBN: (纸本)9781509010257

The objective of the study is to look into a new method to build a human machine interactive system using active listening where machine becomes an active listener based on different states of emotions with which the machine interacts. Using the Index of Co-Incidence we determine the relationships between the emotion-states. The Birthday problem is used to determine the relationships between the visible words with each emotion state. Both of these inputs are given to the Hidden Markov Models model to determine the emotion emitted from the visible words, to decide which statement needs to be identified to continue the conversation during Active Listening. This paper proposes emotion recognition using the new concept of birthday problem and Index of Co-Incidence.

关键词： Active Listening Index of Co-Incidence Birthday Problem forward algorithm Hidden Markov Model

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Simplified Scoring Methods in HMM Based Speech Recognition 1

Simplified Scoring Methods in HMM Based Speech Recognition

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2014 International Conference on Soft Computing & Machine Intelligence (ISCMI 2014)

作者： Paramonov, Pavel Sutula, Nadezhda Natl Res Univ Moscow Power Engn Inst Inst Automat & Comp Engn Moscow Russia

In this paper we present two ways to simplify scoring in Hidden Markov Models (HMM) based speech recognition in order to reduce it's computational complexity. We tested proposed approaches on Russian words recogni... 详细信息

ISBN: (纸本)9781479942312

关键词： Hidden Markov Models speech recognition forward algorithm isolated words recognition

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On Brownian Motion Governed by Telegraph Process

On Brownian Motion Governed by Telegraph Process

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作者： Hu, Chaoran University of Connecticut

学位级别：Ph.D., Doctor of Philosophy

Statistical modeling of animal movement is of critical importance in addressing fundamental questions about space use, movement, resource selection, and behavior in animal ecology. The explosion of telemetry data on animal movement from the recent advancements in tracking and observation technologies presents a storm of opportunities and challenges.A moving-resting-handling (MRH) process is introduced to allow predators to have two different motionless states, resting, and handling. In essence, it is a Brownian motion whose infinitesimal variance changes according to a three-state continuous-time Markov Chain. The Markov Chain can be viewed as a telegraph process with one on state and two off states. We derive the distribution of the occupation times of this Markov Chain and develop a maximum likelihood estimation procedure when the stochastic process at hand is observed at discrete, possibly irregularly spaced time points. The likelihood function is evaluated with forward algorithm in the general framework of hidden Markov models. The second objective of this thesis is to introduce measurement errors to a recently proposed moving-resting (MR) process. MR process is a Brownian motion governed by a telegraph process, which allows periods of inactivity in one state of the telegraph process. It is promising in modeling the movements of predators with long inactive periods such as mountain lions, but the lack of accommodation of measurement errors seriously prohibits its applications in practice. Here we incorporate measurement errors in the MR model and derive basic properties of the ***, convolutions of independent gamma variables are encountered in many applications, including animal movement modeling. Accurate and fast evaluations of their density and distribution functions are critical for such applications. We review several numerical evaluations of convolution of independent gamma variables and compare them with respect to their accuracy and speed. We a

关键词： forward algorithm Likelihood estimation Markov process Occupation time

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Privacy-Preserving HMM forward Computation 17

Privacy-Preserving HMM Forward Computation

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7th ACM Conference on Data and Application Security and Privacy (CODASPY)

作者： Ziegeldorf, Jan Henrik Metzke, Jan Rueth, Jan Henze, Martin Wehrle, Klaus Rhein Westfal TH Aachen Commun & Distributed Syst COMSYS Aachen Germany

ISBN: (纸本)9781450345231

In many areas such as bioinformatics, pattern recognition, and signal processing, Hidden Markov Models (HMMs) have become an indispensable statistical tool. A fundamental building block for these applications is the forward algorithm which computes the likelihood to observe a given sequence of emissions for a given HMM. The classical forward algorithm requires that one party holds both the model and observation sequences. However, we observe for many emerging applications and services that the models and observation sequences are held by different parties who are not able to share their information due to applicable data protection legislation or due to concerns over intellectual property and privacy. This renders the application of HMMs infeasible. In this paper, we show how to resolve this evident conflict of interests using secure two-party computation. Concretely, we propose Priward which enables two mutually untrusting parties to compute the forward algorithm securely, i.e., without requiring either party to share her sensitive inputs with the other or any third party. The evaluation of our implementation of Priward shows that our solution is efficient, accurate, and outperforms related works by a factor of 4 to 126. To highlight the applicability of our approach in real-world deployments, we combine Priward with the widely used HMMER biosequence analysis framework and show how to analyze real genome sequences in a privacy-preserving manner.

关键词： garbled circuits secure two-party computation hidden Markov models forward algorithm privacy-preserving protocols

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MicroRNA target prediction based on second-order Hidden Markov Model

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Frontiers in Biology 2010年第2期5卷 171-179页

作者： Song GAO Liangsheng ZHANG Diangang QIN Tienan FENG Yifei WANG Department of Mathematics School of SciencesShanghai UniversityShanghai 200444China School of Life Sciences Institute of Plant BiologyFudan UniversityShanghai 200433China

MicroRNAs are one class of small singlestranded RNA of about 22 nt serving as important negative gene *** animals,miRNAs mainly repress protein translation by binding itself to the 3'UTR regions of mRNAs with imperfect complementary *** bioinformatics investigations have resulted in a number of target prediction tools,all of these have a common shortcoming—a high false positive ***,it is important to further filter the predicted *** this paper,based on miRNA:target duplex,we construct a second-order Hidden Markov Model,implement Baum-Welch training algorithm and apply this model to further process predicted *** model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%,specificity of 55.10%and accuracy of 69.62%by 10-fold crossvalidation *** order to further verify the applicability of the algorithm,previously collected datasets,including 195 positive and 38 negative,are chosen to test it,with consistent *** believe that our method will provide some guidance for experimental biologists,especially in choosing miRNA targets for validation.

关键词： microRNA target gene experimentally supported targets second-order Hidden Markov Model forward algorithm

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Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures

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BMC BIOINFORMATICS 2004年第1期5卷 2-2页

作者： Alexandrov, V Gerstein, M Yale Univ Dept Mol Biophys & Biochem New Haven CT 06511 USA Yale Univ Dept Comp Sci New Haven CT 06511 USA

Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction. Thus far, however, they have been confined to representing 1D sequence ( or the aspects of structure that could be represented by character strings). Results: We develop an HMM formalism that explicitly uses 3D coordinates in its match states. The match states are modeled by 3D Gaussian distributions centered on the mean coordinate position of each alpha carbon in a large structural alignment. The transition probabilities depend on the spread of the neighboring match states and on the number of gaps found in the structural alignment. We also develop methods for aligning query structures against 3D HMMs and scoring the result probabilistically. For 1D HMMs these tasks are accomplished by the Viterbi and forward algorithms. However, these will not work in unmodified form for the 3D problem, due to non-local quality of structural alignment, so we develop extensions of these algorithms for the 3D case. Several applications of 3D HMMs for protein structure classification are reported. A good separation of scores for different fold families suggests that the described construct is quite useful for protein structure analysis. Conclusion: We have created a rigorous 3D HMM representation for protein structures and implemented a complete set of routines for building 3D HMMs in C and Perl. The code is freely available from http:// www. molmovdb. org/ geometry/ 3dHMM, and at this site we also have a simple prototype server to demonstrate the features of the described approach.

关键词： Match State Structural Alignment Emission Probability Viterbi algorithm forward algorithm

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Probabilistic indoor tracking of Bluetooth Low-Energy beacons

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PERFORMANCE EVALUATION 2023年 162卷

作者： Danis, F. Serhan Ersoy, Cem Cemgil, A. Taylan Galatasaray Univ Dept Comp Engn Istanbul Turkiye Bogazici Univ Dept Comp Engn Istanbul Turkiye

We construct a practical and real-time probabilistic framework for fine target tracking. In our scenario, a Bluetooth Low-Energy (BLE) device navigating in the environment publishes BLE packets that are captured by stationary BLE sensors. The aim is to accurately estimate the live position of the BLE device emitting these packets. The framework is built upon a hidden Markov model (HMM), the components of which are determined with a combination of heuristic and data-driven approaches. In the data-driven part, we rely on the fingerprints formed priorly by extracting received signal strength indicators (RSSI) from the packets. These data are then transformed into probabilistic radio-frequency maps that are used for measuring the likelihood between an RSSI data and a position. The heuristic part involves the movement of the tracked object. Having no access to any inertial information of the object, this movement is modeled with Gaussian densities with variable model parameters that are to be determined heuristically. The practicality of the framework comes from the associated small parameter set used to discretize the components of the HMM. By tuning these parameters, such as the grid size of the area, the mask size and the covariance of the Gaussian;a probabilistic filtering becomes tractable for discrete state spaces. The filtering is then performed by the forward algorithm given the instantaneous sequential RSSI measurements. The performance of the system is evaluated by taking the mean squared errors of the most probable positions at each time step to their corresponding ground-truth positions. We report the statistics of the error distributions and see that we achieve promising results. The approach is also finally evaluated by its runtime and memory usage.

关键词： Belief propagation Bluetooth low-energy Exact inference forward algorithm Hidden Markov model Indoor positioning

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Shape-IT: new rapid and accurate algorithm for haplotype inference

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BMC BIOINFORMATICS 2008年第1期9卷 540-540页

作者： Delaneau, Olivier Coulonges, Cedric Zagury, Jean-Francois Conservatoire Natl Arts & Metiers Chaire Bioinformat F-75003 Paris France

Background: We have developed a new computational algorithm, Shape-IT, to infer haplotypes under the genetic model of coalescence with recombination developed by Stephens et al in Phase v2.1. It runs much faster than Phase v2.1 while exhibiting the same accuracy. The major algorithmic improvements rely on the use of binary trees to represent the sets of candidate haplotypes for each individual. These binary tree representations: (1) speed up the computations of posterior probabilities of the haplotypes by avoiding the redundant operations made in Phase v2.1, and (2) overcome the exponential aspect of the haplotypes inference problem by the smart exploration of the most plausible pathways (ie. haplotypes) in the binary trees. Results: Our results show that Shape-IT is several orders of magnitude faster than Phase v2.1 while being as accurate. For instance, Shape-IT runs 50 times faster than Phase v2.1 to compute the haplotypes of 200 subjects on 6,000 segments of 50 SNPs extracted from a standard Illumina 300 K chip (13 days instead of 630 days). We also compared Shape-IT with other widely used software, Gerbil, PL-EM, Fastphase, 2SNP, and Ishape in various tests: Shape-IT and Phase v2.1 were the most accurate in all cases, followed by Ishape and Fastphase. As a matter of speed, Shape-IT was faster than Ishape and Fastphase for datasets smaller than 100 SNPs, but Fastphase became faster -but still less accurate-to infer haplotypes on larger SNP datasets. Conclusion: Shape-IT deserves to be extensively used for regular haplotype inference but also in the context of the new high-throughput genotyping chips since it permits to fit the genetic model of Phase v2.1 on large datasets. This new algorithm based on tree representations could be used in other HMM-based haplotype inference software and may apply more largely to other fields using HMM.

关键词： Hide Markov Model Single Nucleotide Polymorphism Binary Tree Hide State forward algorithm

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