A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector

Over the past two decades, human action recognition from video has been an important area of research in computer vision. Its applications include surveillance systems, human–computer interactions and various real-world applications where one of the actor is a human being. A number of review works have been done by several researchers in the context of human action recognition. However, it is found that there is a gap in literature when it comes to methodologies of STIP-based detector for human action recognition. This paper presents a comprehensive review on STIP-based methods for human action recognition. STIP-based detectors are robust in detecting interest points from video in spatio-temporal domain. This paper also summarizes related public datasets useful for comparing performances of various techniques.     

Efficient distance-based outlier detection on uncertain datasets of Gaussian distribution

Uncertain data management, querying and mining have become important because the majority of real world data is accompanied with uncertainty these days. Uncertainty in data is often caused by the deficiency in underlying data collecting equipments or sometimes manually introduced to preserve data privacy. This work discusses the problem of distance-based outlier detection on uncertain datasets of Gaussian distribution. The Naive approach of distance-based outlier on uncertain data is usually infeasible due to expensive distance function. Therefore a cell-based approach is proposed in this work to quickly identify the outliers. The infinite nature of Gaussian distribution prevents to devise effective pruning techniques. Therefore an approximate approach using bounded Gaussian distribution is also proposed. Approximating Gaussian distribution by bounded Gaussian distribution enables an approximate but more efficient cell-based outlier detection approach. An extensive empirical study on synthetic and real datasets show that our proposed approaches are effective, efficient and scalable.     

A wavelet-based dominant feature extraction algorithm for palm-print recognition

In this paper, a multi-resolution feature extraction algorithm for palm-print recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several small spatial modules and the effect of modularization in terms of the entropy content of the palm-print images has been investigated. A palm-print recognition scheme is developed based on extracting dominant wavelet features from each of these local modules. In the selection of the dominant features, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, because of modularization of the palm-print image, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. The effect of using different mother wavelets for the purpose of feature extraction has been also investigated. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.     

Enhancement of docetaxel solubility using binary and ternary solid dispersion systems

Context: Poor biopharmaceutical properties and toxicities associated with the intravenous formulation of docetaxel (DTX) necessitate the exploration of an alternate oral route of delivery.

Objective: This study aims at enhancing the solubility of poorly soluble drug, DTX with the help of solid dispersion (SD) technique.

Method: DTX SDs were formulated with selected solubilizers, including Kollidon 12PF, Lutrol F68, Soluplus and Hydroxypropyl-β-cyclodextrin in different weight ratios. Freeze-drying method was used to prepare the binary and ternary SDs. Kinetic solubility of the SDs was evaluated in order to select best DTX-solubilizer combination. Best performing combination was then characterized using differential scanning calorimeter (DSC), powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM).

Results and Discussion: Among all SDs tested, Soluplus outperformed all the excipients at equivalent weight ratio. Binary SD of DTX and Soluplus (1:10) resulted in the highest improvement in solubility (362.93?±?11.01?µg/mL). This is approximately a 93-fold increment as compared to the solubility of crystalline DTX (3.9?±?0.2?µg/mL). This exceptional performance can be attributed to solid-state transformation as well as micellization.

Conclusion: Among all the excipients tested, Soluplus dispersion is the most promising candidate for oral formulation development.     

New insights into the microencapsulation properties of sodium caseinate and hydrolyzed casein

The aim of the present study was to describe the fundamental physical characteristics of spray-dried carrier matrices based on sodium caseinate and casein hydrolyzate and microcapsules as well as their impact on the stability of a microencapsulated functional ingredient. Spray-dried carrier matrix was characterized by different physical methods (helium pycnometry, nitrogen displacement for surface area analysis, X-ray photoelectron spectroscopy, positron annihilation lifetime spectroscopy). Surface viscoelasticity of protein-stabilized oil–water interfaces was analyzed using dynamic pendant drop tensiometry. Fish oil was microencapsulated and microencapsulation efficiency as well as oxidative stability over time was monitored.     

Technical Analysis of Security Management in Terms of Crowd Energy and Smart Living

In this paper, a technical and statistical analysis of security system and security management is provided for crowd energy and smart living. At the same time, a clear understanding is made for crowd energy concept and next generation smart living. Various case examples have been studied and a brief summary has been provided. Furthermore, a statistical analysis has been provided in terms of security management in smart living where it is found that young technocrats give the highest importance to security management in smart living. Last but not the least, current limitation, constraints, and future scope of security implementation have been discussed in terms of crowd energy clustered with next generation smart living.     

A Data Classification Method for Inconsistency and Incompleteness Detection in Access Control Policy Sets

Access control policies may contain anomalies such as incompleteness and inconsistency, which can result in security vulnerabilities. Detecting such anomalies in large sets of complex policies automatically is a difficult and challenging problem. In this paper, we propose a novel method for detecting inconsistency and incompleteness in access control policies with the help of data classification tools well known in data mining. Our proposed method consists of three phases: firstly, we perform parsing on the policy data set; this includes ordering of attributes and normalization of Boolean expressions. Secondly, we generate decision trees with the help of our proposed algorithm, which is a modification of the well-known C4.5 algorithm. Thirdly, we execute our proposed anomaly detection algorithm on the resulting decision trees. The results of the anomaly detection algorithm are presented to the policy administrator who will take remediation measures. In contrast to other known policy validation methods, our method provides means for handling incompleteness, continuous values and complex Boolean expressions. In order to demonstrate the efficiency of our method in discovering inconsistencies, incompleteness and redundancies in access control policies, we also provide a proof-of-concept implementation.     

Production of bio-oil from fixed bed pyrolysis of bagasse

The objective of this work was to produce renewable liquid fuel (bio-oil) from locally produced bagasse by pyrolysis in a batch feeding and fixed bed reactor. The experiments were performed at different temperatures ranging from 300 to 600 °C. The bio-oil was collected from two condensers of different temperatures and defined as oil-1 and oil-2. The maximum total yield of bio-oil was found to be 66.0 wt% based on bagasse. The carbon based non-condensable gases were CO, CO₂, methane, ethane, ethene, propane and propene. The density and viscosity of oil-1 were found to be 1130 kg/m³ and 19.32 centipoise and that were 1050 kg/m³ and 4.25 centipoise for oil-2, respectively. The higher heating values (HHV) of them were 17.25 and 19.91 MJ/kg, respectively. The pH of the bio-oils was found to be around 3.5 and 4.5 for oil-1 and oil-2, respectively. The water, solid and ash contents of oil-1 and oil-2 were determined and found to be around 15, 0.02 and 0.03 wt% and 11, 0.01 and 0.02 wt%, respectively based on bagasse.     

Auto-ignition synthesis of rare-earth metal-doped Ni–Zn spinel ferrites for electronic applications

A series of Ni_0.6Zn_0.4Fe_2?xLa_xO₄ (0?≤?x?≤?0.10) was synthesized using the sol–gel auto combustion method with metal nitrate to citrate ratio 1:1. The resultant powders were then characterized for their structural morphological compositional and electrical properties. The synthesized powders exhibited a single phase with a spinel structure. TEM, SEM, and DLS evaluation confirmed the fine particle nature of the particles. While the EDAX indicated the samples were having the expected stoichiometric cation ratios. An increase in electrical conductivity, high dielectric constant, and low dielectric losses in combination with high saturation magnetization, low coercivity, observed in the present samples make them prospective candidates for high-frequency electronic applications like inductance/transformer cores, antennas, and microwave absorbers.