PRATIT: a CNN-based emotion recognition system using histogram equalization and data augmentation

Multimedia Tools and Applications - Emotions are spontaneous feelings that are accompanied by fluctuations in facial muscles, which leads to facial expressions. Categorization of these facial...     

Handling Movement Epenthesis and Hand Segmentation Ambiguities in Continuous Sign Language Recognition Using Nested Dynamic Programming

We consider two crucial problems in continuous sign language recognition from unaided video sequences. At the sentence level, we consider the movement epenthesis (me) problem and at the feature level, we consider the problem of hand segmentation and grouping. We construct a framework that can handle both of these problems based on an enhanced, nested version of the dynamic programming approach. To address movement epenthesis, a dynamic programming (DP) process employs a virtual me option that does not need explicit models. We call this the enhanced level building (eLB) algorithm. This formulation also allows the incorporation of grammar models. Nested within this eLB is another DP that handles the problem of selecting among multiple hand candidates. We demonstrate our ideas on four American Sign Language data sets with simple background, with the signer wearing short sleeves, with complex background, and across signers. We compared the performance with Conditional Random Fields (CRF) and Latent Dynamic-CRF-based approaches. The experiments show more than 40 percent improvement over CRF or LDCRF approaches in terms of the frame labeling rate. We show the flexibility of our approach when handling a changing context. We also find a 70 percent improvement in sign recognition rate over the unenhanced DP matching algorithm that does not accommodate the me effect.     

Improved gait recognition by gait dynamics normalization

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanlD Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.     

Effect of beam limiting aperture and collector potential on multi-element focused ion beams

A compact microwave driven plasma based multi-element focused ion beam system has been developed. In the present work, the effect of reduced beam limiter (BL) aperture on the focused ion beam parameters, such as current and spot size, and a method of controlling beam energy independently by introducing a biased collector at focal point (FP) are investigated. It is found that the location of FP does not change due to the reduction of BL aperture. The location of FP and beam size are found to be weakly dependent on the collector potential in the range from -8 kV to -18 kV.     

Antioxidant effect of turmeric powder,nitrite and ascorbic acid on stored chicken mince

This investigation was carried out to evaluate the antioxidant effect of turmeric (turmeric 1000 ppm, turmeric 5000 ppm), nitrite (nitrite 200 ppm) and ascorbic acid (ascorbic acid 500 ppm) on raw minced chicken stored at 4 ± 1 °C. Physicochemical properties [pH, water activity, cooking loss, thiobarbituric acid (TBA) value, peroxide value (PV), free fatty acid (FFA)] were evaluated on 0, 2, 4 and 6th day of storage. Highly significant differences (P < 0.01) in pH, TBA value, PV and FFA value were noticed between treatments and between storage periods. TBA values were observed to be lowest for nitrite 200 ppm and then turmeric 5000 ppm, and there was no significant difference between nitrite 200 ppm and turmeric 5000 ppm, and both were superior to ascorbic acid 500 ppm and turmeric 1000 ppm. Among different treatments, PV was found to be lowest in turmeric 5000 ppm and highest in nitrite 200 ppm. FFA value was found to be lowest in turmeric 5000 ppm and highest in ascorbic acid 500 ppm among all treated samples. It can be concluded that turmeric has potential to replace synthetic antioxidants presently used in meat processing with many added advantages.     

In-Place Stabilization of Pond Ash Deposits by Hydrated Lime Columns

Abandoned coal ash ponds cover up vast stretches of precious land and cause environmental problems. Application of suitable in situ stabilization methods may bring about improvement in the geotechnical properties of the ash deposit as a whole, converting it to a usable site. In this study, a technique of in-place stabilization by hydrated lime columns was applied to large-scale laboratory models of ash ponds. Samples collected from different radial distances and different depths of the ash deposit were tested to study the improvements in the water content, dry density, particle size distribution, unconfined compressive strength, pH, hydraulic conductivity, and leachate characteristics over a period of one year. The in-place stabilization by lime column technique has been found effective in increasing the unconfined compressive strength and reducing hydraulic conductivity of pond ash deposits in addition to modifying other geotechnical parameters. The method has also proved to be useful in reducing the contamination potential of the ash leachates, thus mitigating the adverse environmental effects of ash deposits.     

Fake News Identification and Classification Using DSSM and Improved Recurrent Neural Network Classifier

ABSTRACT

The widespread use of social media has enormous consequences for the society, culture and business with potentially positive and negative effects. As online social networks are increasingly used for dissemination of information, at the same they are also becoming a medium for the spread of fake news for various commercial and political purposes. Technologies such as Artificial Intelligence (AI) and Natural Language Processing (NLP) tools offer great promise for researchers to build systems, which could automatically detect fake news. However, detecting fake news is a challenging task to accomplish as it requires models to summarize the news and compare it to the actual news in order to classify it as fake. This project proposes a framework that detects and classifies fake news messages using improved Recurrent Neural Networks and Deep Structured Semantic Model. The proposed approach intuitively identifies important features associated with fake news without previous domain knowledge while achieving accuracy 99%. The performance analysis method used for the proposed system is based on accuracy, specificity and sensitivity.