The role of fear of self and responsibility in obsessional doubt processes: a bayesian hierarchical model

The literature concerning obsessive-compulsive disorder (OCD) indicates that obsessions frequently imply negative evaluative beliefs regarding the self. The construct of the feared self has been used to describe the set of harmful attributes an individual worries they may possess. This study aimed to partially replicate previous research that demonstrated a relationship between feared-self beliefs and obsessional doubt in OCD-relevant contexts. The relationship between perceptions of personal responsibility and associated levels of doubt was also examined. Nonclinical participants (N = 221; 155 female; Mage = 26.4, SD = 9.2) were presented with vignettes related to checking and non OCD-relevant themes, which quantified doubt through the presentation of alternating reality-based (i.e., sensory) and possibility-based information. Of the total sample, 112 participants were randomly allocated to a personally relevant condition (in which the action implied in the vignettes was completed by the reader), and 109 were allocated to a second, other-relevant, condition (in which the action implied in the vignettes was completed by a proximal other). The results provided support for reasoning processes implicated in OCD, suggesting that feared-self beliefs may partially contribute to heightened levels of doubt in response to possibility vs. reality-based information in OCD-relevant contexts. Personal relevance contributed to greater baseline levels of doubt, but not to greater responses to the reality- and possibility-based statements accompanying the OCD-relevant vignette. Implications for theory and future research are discussed.

Hierarchical relationship between values, lifestyles, possessions and food consumption among Beijing adult population

A representative sample (N=801) of the Beijing adult population was used to empirically test and validate a proposed hierarchical model of relationships between values, lifestyles, possessions and food consumption. The theoretical contribution of the present study is the development and an empirically supported paradigm, which explains consumers' consumption behaviour. A further outcome is the substantiation of a pathway model involving hierarchical relationship between values, lifestyle, and demographic variables on material possessions and food consumption.

Educational and mental health implications of the multidimensional model of the self-concept for adolescent girls : comparison of clinical and non-clinical samples

The importance of a positive self-concept as an educational outcome and a facilitator of other desirable outcomes are well established within the education research field. Although the multidimensional and hierarchical model of the self-concept is widely accepted within the educational psychology, this perspective is not widely used within the mental health research. Hence, the purpose of the present investigation is to compare the psychometric properties of the short version of the Self-Description Questionnaire (SDQII-S) based on responses by a large sample of female adolescent high school students (N= 829) and a clinical sample of adolescent girls who have been diagnosed with anorexia nervosa (N= 75). The well-established psychometric properties of the longer version of the SDQII generalise well to both samples of adolescent girls, and analyses provided good support for the invariance of the factor structure across the two samples. Furthermore, analyses employing new structural equation modelling approaches to comparing the latent mean differences indicated that there were differences (although surprisingly small) between the two groups that were generally consistent with a priori predictions. The important educational and clinical implications of these results are discussed.

Hierarchical semi-markov conditional random fields for recursive sequential data

Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.

Narrative structure analysis with education and training videos for e-learning

This paper deals with the problem ofstructuralizing education and training videos for high-level semantics extraction and nonlinear media presentation in e-learning applications. Drawing guidance from production knowledge in instructional media, we propose six main narrative structures employed in education and training videos for both motivation and demonstration during learning and practical training. We devise a powerful audiovisual feature set, accompanied by a hierarchical decision tree-based classification system to determine and discriminate between these structures. Based on a two-liered hierarchical model, we demonstrate that we can achieve an accuracy of 84.7% on a comprehensive set of education and training video data.

Automatic labeling of sports video using umpire gesture recognition

We present results on an extension to our approach for automatic sports video annotation. Sports video is augmented with accelerometer data from wrist bands worn by umpires in the game. We solve the problem of automatic segmentation and robust gesture classification using a hierarchical hidden Markov model in conjunction with a filler model. The hierarchical model allows us to consider gestures at different levels of abstraction and the filler model allows us to handle extraneous umpire movements. Results are presented for labeling video for a game of Cricket.

Teachers' perceptions of their abilities to be educational leaders in Victorian childcare settings

In the context of new national regulatory requirements for designated educational leaders in early childhood settings, 11 Victorian teachers participated in semi-structured interviews exploring their perceptions of their ability to act as educational leaders in their childcare centres. Analysis of these interviews showed that, while teachers successfully made changes within their rooms, only those with a formal title or authority expressed confidence in their ability to lead change across their centres. Barriers to leadership included lack of time and a perception that their teacher qualifications 'did not buy authority'. A hierarchical model of leadership appeared dominant within the centres. The findings suggest both time allowance and formal role designation as strategies to support the new leadership roles, but also raise questions about the most effective models for supporting teacher leadership in childcare contexts.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association

Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPDAF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people.

Polymorphic malware detection using Hierarchical Hidden Markov Model

Binary signatures have been widely used to detect malicious software on the current Internet. However, this approach is unable to achieve the accurate identification of polymorphic malware variants, which can be easily generated by the malware authors using code generation engines. Code generation engines randomly produce varying code sequences but perform the same desired malicious functions. Previous research used flow graph and signature tree to identify polymorphic malware families. The key difficulty of previous research is the generation of precisely defined state machine models from polymorphic variants. This paper proposes a novel approach, using Hierarchical Hidden Markov Model (HHMM), to provide accurate inductive inference of the malware family. This model can capture the features of self-similar and hierarchical structure of polymorphic malware family signature sequences. To demonstrate the effectiveness and efficiency of this approach, we evaluate it with real malware samples. Using more than 15,000 real malware, we find our approach can achieve high true positives, low false positives, and low computational cost.

Discovery of activity structures using the hierarchical hidden Markov model

In building a surveillance system for monitoring people behaviours, it is important to understand the typical patterns of people's movement in the environment. This task is difficult when dealing with high-level behaviours. The flat model such as the hidden Markov model (HMM) is inefficient in differentiating between signatures of such behaviours. This paper examines structure learning for high-level behaviours using the hierarchical hidden Markov model (HHMM).We propose a two-phase learning algorithm in which the parameters of the behaviours at low levels are estimated first and then the structures and parameters of the behaviours at high levels are learned from multi-camera training data. Our algorithm is then evaluated using data from a real environment, demonstrating the robustness of the learned structure in recognising people's behaviour.

A hierarchical PCA-based anomaly detection model

A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.

A Bayesian nonparametric model for hierarchical sequence of images

 Scale features are useful for a great number of applications in computer vision. However, it is difficult to tolerate diversities of features in natural scenes by parametric methods. Empirical studies show that object frequencies and segment sizes follow the power law distributions which are well generated by Pitman-Yor (PY) processes. Based on mid-level segments, we propose a hierarchical sequence of images to obtain scale information stored in a hierarchical structure through the hierarchical Pitman-Yor (HPY) model which is expected to tolerate uncertainty of natural images. We also evaluate our representation by the application of segmentation.

Condition assessment of timber utility poles based on a hierarchical data fusion model

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.