Hierarchical topical segmentation in instructional films based on cinematic expressive functions

In this paper, we propose a novel solution for segmenting an instructional video into hierarchical topical sections. Incorporating the knowledge of education-oriented film theory with our previous study of expressive functions namely the content density and the thematic functions, we develop an algorithm to effectively structuralize an instructional video into a two-tiered hierarchy of topical sections at the main and sub-topic levels. Our experimental results on a set of ten industrial instructional videos demonstrate the validity of the detection scheme.

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.

Topic transition detection using hierarchical hidden Markov and semi-Markov models

In this paper we introduce a probabilistic framework to exploit hierarchy, structure sharing and duration information for topic transition detection in videos. Our probabilistic detection framework is a combination of a shot classification step and a detection phase using hierarchical probabilistic models. We consider two models in this paper: the extended Hierarchical Hidden Markov Model (HHMM) and the Coxian Switching Hidden semi-Markov Model (S-HSMM) because they allow the natural decomposition of semantics in videos, including shared structures, to be modeled directly, and thus enabling efficient inference and reducing the sample complexity in learning. Additionally, the S-HSMM allows the duration information to be incorporated, consequently the modeling of long-term dependencies in videos is enriched through both hierarchical and duration modeling. Furthermore, the use of the Coxian distribution in the S-HSMM makes it tractable to deal with long sequences in video. Our experimentation of the proposed framework on twelve educational and training videos shows that both models outperform the baseline cases (flat HMM and HSMM) and performances reported in earlier work in topic detection. The superior performance of the S-HSMM over the HHMM verifies our belief that duration information is an important factor in video content modeling.

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.

Electrospun spinel LiNi0.5Mn1.5O4 hierarchical nanofibers as 5 v cathode materials for lithium-ion batteries

Spinel LiNi0.5Mn1.5O4 hierarchical nanofibers with diameters of 200–500 nm and lengths of up to several tens of micrometers were synthesized using low-cost starting materials by electrospinning combined with annealing. Well-separated nanofiber precursors impede the growth and agglomeration of Li-Ni0.5Mn1.5O4 particles. The hierarchical nanofibers were constructed from attached LiNi0.5Mn1.5O4 nanooctahedrons with sizes ranging from 200 to 400 nm. It is proven that these Li-Ni0.5Mn1.5O4 hierarchical nanofibers exhibit a favorable electrochemical performance. At a 0.5C (coulombic) rate, it shows an initial discharge capacity of 133 mAhg_1 with a capacity retention over 94% after 30 cycles. Even at 2, 5, 10, and 15C rates, it can still deliver a discharge capacity of 115, 100, 90, and 80 mAhg_1, respectively. Compared with self-aggregated nanooctahedrons synthesized using common sol–gel methods, the LiNi0.5Mn1.5O4 hierarchical nanofibers exhibit a much higher capacity. This is owing to the fact that the self-aggregation of the unique nanooctahedron-in-nanofiber structure has been greatly reduced because of the attachment of nanopolyhedrons in the long nanofibers. This unique microstructured cathode results in the large effective contact areas of the active materials, conductive additives and fully realize the advantage of nanomaterial-based cathodes.

Learning latent activities from social signals with hierarchical dirichlet processes

Understanding human activities is an important research topic, most noticeably in assisted-living and healthcare monitoring environments. Beyond simple forms of activity (e.g., an RFID event of entering a building), learning latent activities that are more semantically interpretable, such as sitting at a desk, meeting with people, or gathering with friends, remains a challenging problem. Supervised learning has been the typical modeling choice in the past. However, this requires labeled training data, is unable to predict never-seen-before activity, and fails to adapt to the continuing growth of data over time. In this chapter, we explore the use of a Bayesian nonparametric method, in particular the hierarchical Dirichlet process, to infer latent activities from sensor data acquired in a pervasive setting. Our framework is unsupervised, requires no labeled data, and is able to discover new activities as data grows. We present experiments on extracting movement and interaction activities from sociometric badge signals and show how to use them for detecting of subcommunities. Using the popular Reality Mining dataset, we further demonstrate the extraction of colocation activities and use them to automatically infer the structure of social subgroups. © 2014 Elsevier Inc. All rights reserved.

Regularizing topic discovery in emrs with side information by using hierarchical bayesian models

We propose a novel hierarchical Bayesian framework, word-distance-dependent Chinese restaurant franchise (wd-dCRF) for topic discovery from a document corpus regularized by side information in the form of word-to-word relations, with an application on Electronic Medical Records (EMRs). Typically, a EMRs dataset consists of several patients (documents) and each patient contains many diagnosis codes (words). We exploit the side information available in the form of a semantic tree structure among the diagnosis codes for semantically-coherent disease topic discovery. We introduce novel functions to compute word-to-word distances when side information is available in the form of tree structures. We derive an efficient inference method for the wddCRF using MCMC technique. We evaluate on a real world medical dataset consisting of about 1000 patients with PolyVascular disease. Compared with the popular topic analysis tool, hierarchical Dirichlet process (HDP), our model discovers topics which are superior in terms of both qualitative and quantitative measures.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Development of hierarchical porous NiO-YSZ with improved mechanical properties

 The thesis developed an hierarchical porous NiO/YSZ with high mechanical performance using a novel process. This process fabricates initial scaffolds with a controllable porosity by enhancing the surface energy of poly methyl methacrylate (PMMA) for the assembly of NiO-YSZ/PMMA. It maintains the hierarchical porous structure using two-step sintering (TSS) to restrict the growth of nanoparticles, and improves the mechanical properties in combination with a bimodal distribution of NiO/YSZ nano-particles.

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

Electronic Medical Record (EMR) has established itself as a valuable resource for large scale analysis of health data. A hospital EMR dataset typically consists of medical records of hospitalized patients. A medical record contains diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Traditional topic models, such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP), can be employed to discover disease topics from EMR data by treating patients as documents and diagnosis codes as words. This topic modeling helps to understand the constitution of patient diseases and offers a tool for better planning of treatment. In this paper, we propose a novel and flexible hierarchical Bayesian nonparametric model, the word distance dependent Chinese restaurant franchise (wddCRF), which incorporates word-to-word distances to discover semantically-coherent disease topics. We are motivated by the fact that diagnosis codes are connected in the form of ICD-10 tree structure which presents semantic relationships between codes. We exploit a decay function to incorporate distances between words at the bottom level of wddCRF. Efficient inference is derived for the wddCRF by using MCMC technique. Furthermore, since procedure codes are often correlated with diagnosis codes, we develop the correspondence wddCRF (Corr-wddCRF) to explore conditional relationships of procedure codes for a given disease pattern. Efficient collapsed Gibbs sampling is derived for the Corr-wddCRF. We evaluate the proposed models on two real-world medical datasets - PolyVascular disease and Acute Myocardial Infarction disease. We demonstrate that the Corr-wddCRF model discovers more coherent topics than the Corr-HDP. We also use disease topic proportions as new features and show that using features from the Corr-wddCRF outperforms the baselines on 14-days readmission prediction. Beside these, the prediction for procedure codes based on the Corr-wddCRF also shows considerable accuracy.

Leveraging visual features and hierarchical dependencies for conference information extraction

Traditional information extraction methods mainly rely on visual feature assisted techniques; but without considering the hierarchical dependencies within the paragraph structure, some important information is missing. This paper proposes an integrated approach for extracting academic information from conference Web pages. Firstly, Web pages are segmented into text blocks by applying a new hybrid page segmentation algorithm which combines visual feature and DOM structure together. Then, these text blocks are labeled by a Tree-structured Random Fields model, and the block functions are differentiated using various features such as visual features, semantic features and hierarchical dependencies. Finally, an additional post-processing is introduced to tune the initial annotation results. Our experimental results on real-world data sets demonstrated that the proposed method is able to effectively and accurately extract the needed academic information from conference Web pages. © 2013 Springer-Verlag.

Hierarchical Nafion enhanced carbon aerogels for sensing applications

This work describes the fabrication of hierarchical 3D Nafion enhanced carbon aerogels (NECAGs) for sensing applications via a fast freeze drying method. Graphene oxide, multiwalled carbon nanotubes and Nafion were mixed and extruded into liquid nitrogen followed by the removal of ice crystals by freeze drying. The addition of Nafion enhanced the mechanical strength of NECAGs and effective control of the cellular morphology and pore size was achieved. The resultant NECAGs demonstrated high strength, low density, and high specific surface area and can achieve a modulus of 20 kPa, an electrical conductivity of 140 S m(-1), and a specific capacity of 136.8 F g(-1) after reduction. Therefore, NECAG monoliths performed well as a gas sensor and as a biosensor with high sensitivity and selectivity. The remarkable sensitivity of 8.52 × 10(3)μA mM(-1) cm(-2) was obtained in dopamine (DA) detection, which is two orders of magnitude better than the literature reported values using graphene aerogel electrodes made from a porous Ni template. These outstanding properties make the NECAG a promising electrode candidate for a wide range of applications. Further in-depth investigations are being undertaken to probe the structure-property relationship of NECAG monoliths prepared under various conditions.

Hydrangea-like multi-scale carbon hollow submicron spheres with hierarchical pores for high performance supercapacitor electrodes

Uniform hydrangea-like multi-scale carbon hollow submicron spheres (HCSSg) are fabricated by a simple hydrothermal method using glucose as carbon source and fibrous silicon dioxides spheres as shape guide. Structure characterization suggests that petal-like partially graphitized carbon nanosheets with the thickness of about 10 nm arranged in three dimensions (3D) to form the hydrangea-like hollow spheres (size ranging from 250 to 500 nm) with mesoporous channels, which can be conducive to be a high specific surface area (934 m2 g-1) and bulk density (0.87 cm g-3), hierarchical pores structure with good conductivity. As a result, the HCSSg has been demonstrated to be a supercapacitor electrode material with high gravimetric (386 F g-1 at 0.2 A g-1) and outstanding volumetric (335 F cm-3) capacitance, good rate capability and cycling stability with 94% capacitance retention after 5000 cycles in aqueous electrolytes, thus suggesting its application potential.

Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes

Hierarchical Dirichlet processes (HDP) was originally designed and experimented for a single data channel. In this paper we enhanced its ability to model heterogeneous data using a richer structure for the base measure being a product-space. The enhanced model, called Product Space HDP (PS-HDP), can (1) simultaneously model heterogeneous data from multiple sources in a Bayesian nonparametric framework and (2) discover multilevel latent structures from data to result in different types of topics/latent structures that can be explained jointly. We experimented with the MDC dataset, a large and real-world data collected from mobile phones. Our goal was to discover identity–location– time (a.k.a who-where-when) patterns at different levels (globally for all groups and locally for each group). We provided analysis on the activities and patterns learned from our model, visualized, compared and contrasted with the ground-truth to demonstrate the merit of the proposed framework. We further quantitatively evaluated and reported its performance using standard metrics including F1-score, NMI, RI, and purity. We also compared the performance of the PS-HDP model with those of popular existing clustering methods (including K-Means, NNMF, GMM, DP-Means, and AP). Lastly, we demonstrate the ability of the model in learning activities with missing data, a common problem encountered in pervasive and ubiquitous computing applications.