Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150

LOMo: Latent Ordinal Model for Facial Analysis in Videos

We study the problem of facial analysis in videos. We propose a novel weakly supervised learning method that models the video event (expression, pain etc.) as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for smile, brow lower and cheek raise for pain). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations. In combination with complimentary features, we report state-of-the-art results on these datasets.Comment: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR

Study Of Gaussian & Impulsive Noise Suppression Schemes In Images

Noise is introduced into images usually while transferring and acquiring them.The main type of noise added while image acquisition is called Gaussian noise while Impulsive noise is generally introduced while transmitting image data over an unsecure communication channel , while it can also be added by acquiring. Gaussian noise is a set of values taken from a zero mean Gaussian distribution which are added to each pixel value. Impulsive noise involves changing a part of the pixel values with random ones. Various techniques are employed for the removal of these types of noise based on the properties of their respective noise models. Impulse Noise removal algorithms popularly use ordered statistics based ¯lters. The ¯rst one is an adaptive ¯lter using center-weighted median. In this method, the di®erence of the center weighted mean of a neighborhood with the central pixel under consideration is compared with a set of thresholds. Another method which takes into account the presence of the noise free pixels has been implemented.It convolutes the median of each neighborhood with a set of convolution kernels which are oriented according to all possible con¯gurations of edges that contain the central pixel,if it lies on an edge. A third method which deals with the detection of noisy pixels on the binary slices of an image is implemented. It is based on threshold Boolean ¯ltering. The ¯lter inverts the value of the central pixel if the number of pixels with values opposite to it is more than the threshold. The fourth method has an e±cient double derivative detector, which gives a de- cision based on the value of the double derivative. The substitution is done with the average gray scale value of the neighborhood. Gaussian Noise removal algorithms ideally should smooth the distinct parts of the image without blurring the edges.A universal noise removing scheme is implemented which weighs each pixel with respect to its neighborhood and deals with Gaussian and impulse noise pixels di®erently based on parameter values for spatial, radiometric and impulsive weight of the central pixel. The aforementioned techniques are implemented and their results are compared subjectively as well as objectively

A dataset of continuous affect annotations and physiological signals for emotion analysis

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.Comment: Dataset available at: https://rmc.dlr.de/download/CASE_dataset/CASE_dataset.zi

Gamification based cybersecurity training in higher institutions : an analysis of Delhi University

Cybersecurity challenges are particularly relevant for higher education institutions, which maintain large quantities of sensitive information. Although organizations deploy numerous security controls, the human error aspect leaves them open to attacks such as phishing, using weak passwords and failing to comply with the organization’s security policy. Most cybersecurity training programs today take a passive, very technical approach that fails to create long-lasting changes in behavior. Traditional means, such as lectures or videos, do not excite students and faculty, leading to low retention of cybersecurity best practices. This concludes with the Design of an Interactive Cybersecurity Training Guidelines, which combines gamification, real-world simulation-led learning, and behavior reinforcement to improve engagement and compliance. This research will use a mixedmethods research approach, including surveys to compare traditional interactive cybersecurity education. The research will also evaluate obstacles to the adoption of corporate policies and initiatives, as well as discuss how to integrate cyber hygiene into academic coursework. The resulting outcome will be a scalable, curriculum-integrated guideline that increases cybersecurity content retention, empowers institutional policy adherence, and reduces human-error-based cyber risks. To the best of the knowledge, this is the first research showing an increase in security training and security culture in higher education that empowers students and faculty to combat poor security practices

11 - The impact of obesity on ingestion-induced hippocampal \u3ci\u3eArc\u3c/i\u3e expression in male rats

Obesity is a chronic disease that affects more than 33% of American adults and roughly 13% of adults worldwide. Obesity has many peripheral impacts, such as cardiovascular diseases, type II diabetes, and arthritis. Obesity also influences the functioning of the brain. In particular, obesity and overeating impair the function of the hippocampus, which is vital for memory. In humans, impairing the memory of a meal increases subsequent intake. In rats, our lab has shown that sucrose ingestion activates molecules necessary for hippocampal memory formation, such as activity regulated cytoskeleton associated protein (Arc). We hypothesize that obesity disrupts hippocampal-dependent memory formation of a meal, which could further contribute to obesity. We predict that high-fat diet-induced obesity impairs sucrose ingestion-induced Arc mRNA expression. To test this, rats were placed on a high-fat diet or a control diet for 8 weeks. On the experimental day, rats were given access to sucrose meal for 10 minutes; euthanized, and the hippocampus, fat pads, and liver were extracted. The hippocampus will be tested for the concentration of Arc mRNA by quantitative real-time polymerase chain reaction (qRT-PCR). It is predicted that the number of calories consumed, body, liver and fat pad mass, and the amount of sucrose consumed by the experimental rats will be greater than that of the control rats. More importantly, sucrose-induced Arc mRNA expression is expected to be reduced in the experimental rats compared to the control rats. This would be consistent with the hypothesis that obesity disrupts the hippocampal formation of a memory of a meal

Selection of amine combination for CO2 capture in a packed bed scrubber

This investigation was to test different blends of tertiary amine; triethanolamine (TEA) into primary amine; Monoethanolamine (MEA) used to capture CO2 in packed bed scrubber with recycle stream. Four different operating parameters: Amine Combination (A), Dilution Water (B), Liquid Flow rate (C), and Gas Flow rate (D) were varied to study the behavior of the system. Moreover, Taguchi method was employed to establish the order of importance of different parameters in the process. A 4 factor and 3 level was chosen for the study and it was explored using L9 (34 ) orthogonal array design. According to 3-level design 0%, 20% and 30% were chosen for A, 10%, 20% and 30% for B, 1 Lmin−1, 1.5 Lmin−1 and 2 Lmin−1 for C, 8 Lmin−1, 16 Lmin−1 and 20 Lmin−1 for D. To understand the effectiveness order of different operating parameters, three factors namely Absorption efficiency (E), Absorption Rate (RA), and Scrubbing Factor (E) were calculated upon which the order was compared. The highest efficiency of 92.2% was achieved with 20% TEA. However, with 30% of TEA and 20% solvent mix maximum scrubbing factor (E) of 0.63 mol-CO2/mol-Solvent was achieved. As per Taguchi analysis the significance sequence for absorption efficiency (ϕ) was B > C > D > A; for absorption rate C > B > D > A and for scrubbing factor it was C > B > D > A. The blending of tertiary amine seemed advantageous for carbon dioxide capture process

Investigating Liquid-Liquid Phase Separation in Lipid Bilayers: A Multi-Modal Approach Utilizing Spectroscopy, Microscopy, and Cryo-EM

This thesis explores the characterization of liquid-liquid phase separation in model lipid bilayers using fluorescence, optical microscopy, and cryo-electron microscopy (cryo-EM) integrated with machine learning (ML) analysis. The plasma membrane has a complex composition, lateral heterogeneity and dynamic structure which makes it challenging to study. Simplified model membranes containing three or four-component lipid mixtures, typically comprising low- and high-melting lipids along with cholesterol, form phase separated systems that mimic lateral heterogeneity/lipid rafts in biomembranes. In living cells, lipid rafts are thought to form nanoscopic domains smaller than 200 nm. These domains cannot be resolved by conventional optical microscopy. For a long time, these nanoscopic domains have been characterized using indirect techniques. Seeing is believing and cryo-EM is employed as the primary tool for visualizing these nanoscopic domains, leveraging its ability to analyze samples particle-by-particle. Chapter 3 introduces a novel application of ML for characterizing phase-separated vesicles in cryo-EM images. It presents a simulation-based study testing various supervised and unsupervised ML methods for classifying the phase state of liposomes. Chapter 4 transitions to an experimental study, applying the supervised ML pipeline developed in Chapter 3 to estimate phase fraction, domain size and domain number in a three-component mixture. Substantial heterogeneity is observed in experimental samples that was not present in simulated liposomes. Together, these studies successfully demonstrate cryo-EM\u27s potential for studying nanoscopic domains in model membranes on vesicle-by-vesicle basis. Chapter 5 investigates the role of the membrane dipole potential in lipid phase separation, providing insights into the mechanisms driving domain formation in lipid bilayers. This comprehensive study also highlights the synergy between advanced microscopy, ML, and theoretical modeling in elucidating the complexities vi of lipid phase behavior. These studies underscore the importance of lipid composition in biological membranes as a mechanism for controlling lipid raft formation and function

Simultaneous resource recovery and ammonia volatilization minimization in animal husbandry and agriculture

The study demonstrates that the minimization of ammonia volatilization and urea recovery could be coupled through the use of physical adsorption processes in continuous packed-bed columns. The potential of using microwave activated coconut shell based activated carbon toward the recovery of urea from cattle urine was investigated. The prepared carbon was immobilized onto etched glass beads to investigate the effect of initial concentration, flow rate and size of carbon support in a continuous, down-flow mode packed column. Further, to describe the sorption behavior, the experimental data were tested against different kinetic models. The analysis of the breakthrough curves allowed identification of the favorable operating parameters as: sorbate flow (8 L·h−1), initial urea concentration (60%) and glass bead support size (ϕ 1.5 cm). An equilibrium sorption of 802.8 mg·g−1 and up to 80% urea recovery was observed. Regeneration studies allowed for nearly 95% urea recovery with sorbent capacity decreasing by 5% over seven cycles of sorption/desorption