Temporal Probabilistic Model Research Articles

Temporal duration-based probabilistic argumentation frameworks

Abstract The study of Dung-style Argumentation Frameworks in recent years has focused on incorporating time. For example, availability intervals have been added to arguments and relations, resulting in different outputs of Dung semantics over time. This paper examines the probability distribution of arguments over time intervals. Using this temporal probabilistic model, the study explores how these frameworks can be transformed into a probabilistic argumentation according to the constellation approach and how they can be interpreted within the epistemic approach. The epistemic approach relies on the notion of defeat to select significant conflicts based on probability distributions. The study also introduces the temporal acceptability of arguments based on the concept of defence, allowing for more precise results over time. Finally, the models (constellation and epistemic) are extended to account for events that have a duration, i.e. that can occur for several consecutive instants of time.

A Novel Probabilistic Network Model for Estimating Cognitive-Gait Connection Using Multimodal Interface

Research in human gait analysis has captivated several computer vision researchers to solve human identification problems. The proposed work provides a novel approach for cognitive state estimation via multi-modal analysis. The advantage of multi-modal system is to provide adequate motion signatures with ensemble of multimodal gait data to ensure data reliability for classification. The relationship between human cognitive states and gait is predicted using both temporal and non-temporal probabilistic models. We estimate prior probability tables for non-temporal probabilistic model known as Simple Bayesian Model after analysing the data acquired from Inertial Measurement Unit (IMU), Electroencephalography (EEG) and multiple Kinect V2.0 sensors. A novel Dynamic Bayesian Network (DBN) is used as probabilistic temporal model for estimating most probable sequence of transitions among human cognitive states. We apply Gaussian Mixture Modelling with Expectation maximisation (GMM-EM) to tune transition and emission probabilities for maximizing the probability of the observed sequence. A promising estimation accuracy of 88.6% is obtained. Also, Principal Component Analysis (PCA) and k-nearest neighbour (kNN) algorithms are applied separately to calculate the input probabilities for DBN model. It is observed that both GMM-EM and k-NN based approaches outperform all the state-of-the-art techniques. Moreover, standard statistical tests are performed on acquired dataset to validate the experimental results.

2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting.

Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model. To solve this, we propose a model that retains the spatial aspect of the target sequence with a DMM that consists of 2D convolutional neural networks. We then show the robustness of our method to data with large variance compared with naive forecast, vanilla DMM, and convolutional long short-term memory (LSTM) using synthetic data, even outperforming the DNN models over a longer forecast period. We also point out the limitations of our model when forecasting real-world precipitation data and the possible future work that can be done to address these limitations, along with additional future research potential.

Compliant Parametric Dynamic Movement Primitives

SummaryIn this paper, we propose and implement an advanced manipulation framework that enables parametric learning of complex action trajectories along with their haptic feedback profiles. Our framework extends Dynamic Movement Primitives (DMPs) method with a new parametric nonlinear shaping function and a novel force-feedback coupling term. The nonlinear trajectories of the action control variables and the haptic feedback trajectories measured during execution are encoded with parametric temporal probabilistic models, namely parametric hidden Markov models (PHMMs). PHMMs enable autonomous segmentation of a taught skill based on the statistical information extracted from multiple demonstrations, and learning the relations between the model parameters and the properties extracted from the environment. Hidden states with high-variances in observation probabilities are interpreted as parts of the skill that could not be reliably learned and autonomously executed due to possibly uncertain or missing information about the environment. In those parts, our proposed force-feedback coupling term, which computes the deviation of the actual force feedback from the one predicted by the force-feedback PHMM, acts as a compliance term, enabling a human to scaffold the ongoing movement trajectory to accomplish the task. Our method is verified in a number of tasks including a real pick and place task that involves obstacles of different heights. Our robot, Baxter, successfully learned to generate the trajectory taking into the heights of the obstacles, move its end effector stiffly (and accurately) along the generated trajectory while passing through apertures, and allow human–robot collaboration in the autonomously detected segments of the motion, for example, when the gripper picks up the object whose position is not provided to the robot.

A Prefetching Technique Using HMM Forward and Backward Chaining for the DFS in Cloud

A general class of temporal probabilistic model have recently developed, which extends the Forward, Backward and Viterbi algorithm for hidden Markov models. The HMM (Midden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The algorithm is based on shrinking the state space of the HMM noticeably using such chains. The states through which the world passes are hidden, or unobserved. However, at each point in time also gets an observation that in some way reflects on the current state of the world. The Cloud Computing is a big deal for three reasons: It does not need any effort on Clients part to maintain or manage. It’s effectively infinite in size, so clients don’t need to worry about it running out of capacity. User can access cloud-based applications and services from anywhere all you need is a device with an Internet connection. In this Cloud Computing used the Distributed File Systems (DFS) for sharing and allocating the data during dynamic process .Those process are using some Prediction algorithms here using HMM Forward and Backward Chain. In this paper represents, Cloud Storage Server can Share the data among with the multiple users, using two prediction algorithms such as forward and backward chain in HMM.

A new approach to temporal modelling for landslide hazard assessment using an extreme rainfall induced-landslide index

An ever-increasing trend of extreme rainfall events in South Korea due to climate change is causing shallow landslides and shallow landslide induced debris flows in the mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion won in losses, and attendant fatalities, every year. The most common type of landslide observed is the shallow landslide occurring at 1–3m depth, which may mobilize into a catastrophic debris flow. A landslide early warning system encompassing different scale-based stages is used to predict potential areas for both the landslide types. Current study focusing on the first stage landslide hazard assessment at regional or medium scale requires the development of spatially evolving landslide hazard maps for both types of landslides based on the real-time rainfall. However, lack of complete landslide inventory data motivates the development of temporal and spatial models as independent components of the landslide hazard. Most of the existing temporal assessment schemes traditionally rooted in recurrence-based concepts does not consider soil factors and are not suitable to be incorporated in to the landslide early warning system since real-time rainfall cannot be considered. This motivated the development of a new probabilistic temporal model termed the extreme rainfall-induced landslide index. The probabilistic index was developed in Gangwon Province through a logistic regression using four factors; namely, continuous rainfall, 20-days antecedent rainfall, saturated hydraulic conductivity, and storage capacity. The developed model exhibited high area under the curve (AUC) values of 82% and 91% obtained for the training and validation curves, exhibiting good performance of the statistical index. Also, a high performance susceptibility model (training and validation AUC values of 96% and 94%, respectively) was developed using a logistic regression analysis, for Deokjeok-ri Creek, located in Gangwon province. Assuming the independence of the hazard components, a dynamic hazard index (DHI) was established through a joint probability of both the well validated models. The DHI was used to study the evolution of landslide hazard for the July 2006 extreme rainfall-induced landslide events in Deokjeok-ri Creek.

Sepsis Patient Detection and Monitor Based on Auto-BN.

Sepsis is a life-threatening condition caused by an inappropriate immune response to infection, and is a leading cause of elderly death globally. Early recognition of patients and timely antibiotic therapy based on guidelines improve survival rate. Unfortunately, for those patients, it is often detected late because it is too expensive and impractical to perform frequent monitoring for all the elderly. In this paper, we present a risk driven sepsis screening and monitoring framework to shorten the time of onset detection without frequent monitoring of all the elderly. Within this framework, the sepsis ultimate risk of onset probability and mortality is calculated based on a novel temporal probabilistic model named Auto-BN, which consists of time dependent state, state dependent property, and state dependent inference structures. Then, different stages of a patient are encoded into different states, monitoring frequency is encoded into the state dependent property, and screening content is encoded into different state dependent inference structures. In this way, the screening and monitoring frequency and content can be automatically adjusted when encoding the sepsis ultimate risk into the guard of state transition. This allows for flexible manipulation of the tradeoff between screening accuracy and frequency. We evaluate its effectiveness through empirical study, and incorporate it into existing medical guidance system to improve medical healthcare.

Recognizing multi-user activities using wearable sensors in a smart home

The advances of wearable sensors and wireless networks offer many opportunities to recognize human activities from sensor readings in pervasive computing. Existing work so far focuses mainly on recognizing activities of a single user in a home environment. However, there are typically multiple inhabitants in a real home and they often perform activities together. In this paper, we investigate the problem of recognizing multi-user activities using wearable sensors in a home setting. We develop a multi-modal, wearable sensor platform to collect sensor data for multiple users, and study two temporal probabilistic models—Coupled Hidden Markov Model (CHMM) and Factorial Conditional Random Field (FCRF)—to model interacting processes in a sensor-based, multi-user scenario. We conduct a real-world trace collection done by two subjects over two weeks, and evaluate these two models through our experimental studies. Our experimental results show that we achieve an accuracy of 96.41% with CHMM and an accuracy of 87.93% with FCRF, respectively, for recognizing multi-user activities.

DYNAMIC MODELING OF GROUNDWATER POLLUTANTS WITH BAYESIAN NETWORKS

The emphasis on the need to protect groundwater quality has resulted in an increased interest in groundwater quality assessment. Water experts and researchers in the area have been, however, arguing that the currently used techniques are not accurate means of measuring groundwater contamination. It is mainly because these techniques neglect not only the probabilistic dependencies between pollutants but also the precision and the accuracy of the tested methods used by environmental laboratories. Therefore, this work describes the development and application of a prototype Dynamic Bayesian Network (DBN) that addresses these problems through the use of a temporal probabilistic model. First, we present a new technique for data preprocessing. Then we describe the network models we developed, as well as the methods used to build these models. Various challenges, such as acquiring groundwater datasets, identifying pollutants and anticipating potential problem contaminants, are addressed. Finally, we present the results of applications of these models.

Probabilistic-Temporal Model of QOS-Routing with Precomputation of Routes under the Terms of Non-Ideal Reliability of Telecommunication Network

Probabilistic-Temporal Model of QOS-Routing with Precomputation of Routes under the Terms of Non-Ideal Reliability of Telecommunication Network

Probabilistic temporal reasoning and its application to fossil power plant operation

Many real-world applications, such as industrial diagnosis, require an adequate representation and inference mechanism that combines uncertainty and time. In this work, we propose a novel approach for representing dynamic domains under uncertainty based on a probabilistic framework, called temporal nodes Bayesian networks (TNBN). The TNBN model is an extension of a standard Bayesian network, in which each temporal node represents an event or state change of a variable and the arcs represent causal–temporal relationships between nodes. A temporal node has associated a probability distribution for its time of occurrence, where time is discretized in a finite number of temporal intervals; allowing a different number of intervals for each node and a different duration for the intervals within a node (multiple granularity). The main difference with previous probabilistic temporal models is that the representation is based on state changes at different times instead of state values at different times. Given this model, we can reason about the probability of occurrence of certain events, for diagnosis or prediction, using standard probability propagation techniques developed for Bayesian networks. The proposed approach is applied to fossil power plant diagnosis through two detailed case studies: power load increment and control level system failure. The results show that the proposed formalism could help to improve power plant availability through early diagnosis of events and disturbances.