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Application and Evaluation of a Conditioned Hidden Markov Model for Estimating Interaction Quality of Spoken Dialogue Systems [chapter]

Stefan Ultes, Robert ElChab, Wolfgang Minker
2013 Natural Interaction with Robots, Knowbots and Smartphones  
account • Our approach: -Models which take into account temporal dependencies and previous values inherently • Hidden Markov Model • Conditioned Hidden Markov Model • Often: one class per HMM  ...  • Observation sequence probability Start Hidden Markov Model (HMM) Motivation | IQ | CHMM | Experiment | Conclusion S1 S2 Conditioned Hidden Markov Model (CHMM) (Glodek et al., Interspeech 2011  ... 
doi:10.1007/978-1-4614-8280-2_27 fatcat:chgotrfxojgjvm5lzy4oqxdkx4

Marimba: A Tool for Verifying Properties of Hidden Markov Models [chapter]

Noé Hernández, Kerstin Eder, Evgeni Magid, Jesús Savage, David A. Rosenblueth
2015 Lecture Notes in Computer Science  
The formal verification of properties of Hidden Markov Models (HMMs) is highly desirable for gaining confidence in the correctness of the model and the corresponding system.  ...  A significant step towards HMM verification was the development by Zhang et al. of a family of logics for verifying HMMs, called POCTL*, and its model checking algorithm.  ...  Introduction A Hidden Markov Model (HMM) is an extension of a Discrete Time Markov Chain (DTMC) where the states of the model are hidden but the observations are visible.  ... 
doi:10.1007/978-3-319-24953-7_14 fatcat:uziot5sabbgvjg7asgp7prh4py

Modeling search processes using hidden states in collaborative exploratory web search

Zhen Yue, Shuguang Han, Daqing He
2014 Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing - CSCW '14  
Author Keywords Collaborative information behavior; exploratory search; Hidden Markov Model; information seeking process.  ...  Our results showed that the identified hidden patterns of search process through HMM are compatible with previous well-known models.  ...  The Markov model is built on four pre-defined Areas of Interests (AOIs). The Hidden Markov Model (HMM) is a well-established model with mature techniques for parameter estimation.  ... 
doi:10.1145/2531602.2531658 dblp:conf/cscw/YueHH14 fatcat:m6lodxa7rvewjiwmkw4qw4aq4a

Analysis of Temporal Features for Interaction Quality Estimation [article]

Stefan Ultes, Alexander Schmitt, Wolfgang Minker
2016 arXiv   pre-print
Furthermore, for the feature sub-group modeling the temporal effects with a window, we modify the window size increasing the overall performance significantly by +15.69%.  ...  We extend the set of temporal features to contain the system and the user view.  ...  [3] uses Hidden Markov Models (HMMs) to model the SDS as a process evolving over time. User Satisfaction was predicted at any point within the dialogue on a 5 point scale.  ... 
arXiv:1604.01985v1 fatcat:63ayuemcxbdr3gawsjdblwvoqi

Recurrent Neural Network Interaction Quality Estimation [chapter]

Louisa Pragst, Stefan Ultes, Wolfgang Minker
2016 Lecture Notes in Electrical Engineering  
Getting a good estimation of the Interaction Quality (IQ) of a spoken dialogue helps to increase the user satisfaction as the dialogue strategy may be adapted accordingly.  ...  [8] put an emphasis on the sequential character of the IQ measure by applying Hidden Markov Models (HMMs) and Conditioned Hidden Markov Models (CHMMs).  ...  [16] presented an approach using Hidden Markov Models (HMMs) to model the SDS as a process evolving over time.  ... 
doi:10.1007/978-981-10-2585-3_31 fatcat:2y3t4i72cvg5ho5kepemkaxani

A QoS-Satisfied Prediction Model for Cloud-Service Composition Based on a Hidden Markov Model

Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Ying Lou, Wangyang Wei
2013 Mathematical Problems in Engineering  
In this paper, we focus on QoS-satisfied predictions about the composition of cloud-service components and present a QoS-satisfied prediction model based on a hidden Markov model.  ...  We discuss the proposed model in detail and prove some aspects of the model. Simulation results show that our model can achieve high prediction accuracies.  ...  The Markov model (MM), particularly the hidden Markov model (HMM), has been shown to be a good technique for solving prediction problems.  ... 
doi:10.1155/2013/387083 fatcat:n67iqjylzfe2fcosnk43iafm7u

A Time-Aware Recommender System Based on Dependency Network of Items

Seyed Mohammadhadi Daneshmand, Amin Javari, Seyed Ebrahim Abtahi, Mahdi Jalili
2014 Computer journal  
There is indeed a hidden network structure among the items and each user tracks a sequence of items in this network.  ...  The proposed model also results in personalized and diverse recommendations. Experimental evaluations show that the model can be trained based on the ratings of a limited number of users.  ...  Markov-based recommender Methods based on Markov chain model the recommendation task as a Markov process [10] .  ... 
doi:10.1093/comjnl/bxu115 fatcat:q7yzp65t5ze25mgxgdsxdihr4a

Temporal Analytics for Software Usage Models [chapter]

Oana Andrei, Muffy Calder
2018 Lecture Notes in Computer Science  
We address the problem of analysing how users actually interact with software.  ...  We define new probabilistic models whose parameters are inferred from logged time series data of user-software interactions.  ...  (mobile app) used by tens of thousands of users, working in close collaboration with the app developers. 2 Technical background Markov models We assume familiarity with Markov models, probabilistic logics  ... 
doi:10.1007/978-3-319-74781-1_1 fatcat:bqmdln4drvdodaadsw4v42migy

Interaction Quality Estimation in Spoken Dialogue Systems Using Hybrid-HMMs

Stefan Ultes, Wolfgang Minker
2014 Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)  
In this work, we investigate an approach for determining Interaction Quality for human-machine dialogue based on methods modeling the sequential characteristics using HMM modeling.  ...  Ultes et al. (2012a) put an emphasis on the sequential character of the IQ measure by applying a Hidden Markov Models (HMMs) and a Conditioned Hidden Markov Models (CHMMs).  ...  presented an approach using Hidden Markov Models (HMMs) to model the SDS as a process evolving over time.  ... 
doi:10.3115/v1/w14-4328 dblp:conf/sigdial/UltesM14 fatcat:4wxit6fuvvc3vesspzov45bjjy

Assisted living technologies for older adults

Parisa Rashidi
2012 Proceedings of the 2nd ACM SIGHIT symposium on International health informatics - IHI '12  
Hidden Semi-Markov Model y 1 y 2 y 3 y 4 x 1 x 2 x 3 x 4 Arbitrary Duration Distribution 51 Markov Network First Order Logic Markov Logic Network  Markov logic networks  ...  −1 =1 48  Coupled Hidden Markov Model (CHMM) [Wang 2010]  O = observations  A, B = activities Multiple Residents?  ... 
doi:10.1145/2110363.2110478 dblp:conf/ihi/Rashidi12 fatcat:vavobpvbqzfslm4343duxh7yfe

Managing travel demand: Location recommendation for system efficiency based on mobile phone data [article]

Yan Leng, Alex 'Sandy' Pentland, Haris N. Koutsopolous
2016 arXiv   pre-print
Under 60% compliance rate, 41% travel delay is saved with a 17% reduction in satisfaction.  ...  Specifically, the results show that under full compliance rate, travel delay fell by 52% at a cost of 31% less satisfaction.  ...  Mathew [29] predicts next-location using a Hidden Markov Model with contextual information, such as activities and purposes.  ... 
arXiv:1610.06825v1 fatcat:t33vmnawzradffdwhq4xkjhb74

A Comparison of Structural Equation Modeling Approaches with DeLone & McLean's Model: A Case Study of Radio-Frequency Identification User Satisfaction in Malaysian University Libraries

Ali Noudoostbeni, Kiran Kaur, Hashem Salarzadeh Jenatabadi
2018 Sustainability  
Two approaches are applied to estimate user satisfaction, such as the Bayesian and maximum likelihood estimation approaches.  ...  The results reveal that Bayesian estimation provides good fit to the data, unlike the model with the maximum likelihood estimator.  ...  The current study suggests that the Bayesian approach is deemed a suitable structural equation model for analyzing user satisfaction with library RFID.  ... 
doi:10.3390/su10072532 doaj:800e6047f7f34c3f9a9d7c1e4811fcc4 fatcat:o4gijvl22faqvdq6r3u5znpktu

Introducing Busy Customer Portfolio Using Hidden Markov Model

Sepideh Emam, Abdollah Aaghaie
2011 Iranian Journal of Management Studies  
Forty four articles were selected and categorized on two major subclasses: articles which had used Markov chain models (MCM) in CRM and those which had applied hidden Markov models (HMM) in CRM.  ...  One hundred articles were identified and reviewed to find direct relevance for applying Markov models in CRM.  ...  Markov chain models (MCM) that includes all Markov models except hidden ones. 2. Hidden Markov models (HMM).  ... 
doaj:b7bdc5b6edb94be2ba1cc33d5c39794b fatcat:r5yllq237rfhbc76ahw4qdtkze

Estimation of spectrum valuation for 5G dynamic frequency allocation and auctions

Ayman Chouayakh, Aurelien Bechler, Isabel Amigo, Loutfi Nuaymi, Patrick Maille
2021 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)  
In this paper, we propose a model for estimating that valuation. The model is based on Markov chain modeling of user behavior, to compute the MNO satisfaction as a function of the obtained spectrum.  ...  With those assumptions, the pair (i, j) of numbers of connected users of each type is a Markov process [20] .  ...  In [14] the authors propose a model based on user satisfaction in order to compute the economic valuation.  ... 
doi:10.1109/vtc2021-spring51267.2021.9448690 fatcat:7q5bc3vf3ngsdowzlu243sxzjy

Transition Discovery of Sequential Behaviors in Email Application Usage Using Hidden Markov Models

William N. Robinson, Arash Akhlaghi, Tianjie Deng
2013 2013 46th Hawaii International Conference on System Sciences  
Herein, we show how dynamically generated hidden Markov models (HMMs) characterize sequence patterns in a software's userinterface event-stream.  ...  This is important for identifying usage transitions, which occur with user learning.  ...  Monitor Design Markov Modeling A hidden Markov model (HMM) is a stochastic signal model [41] . In our application, the signals are sequences of discrete typed events.  ... 
doi:10.1109/hicss.2013.574 dblp:conf/hicss/RobinsonAD13 fatcat:5inoq2n46rhmvplicabgd7oshi
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