Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances before adopting cloud services. Moreover, the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches. In this study, the model based on the root of trust for continuous integration and continuous deployment is proposed, instead of only relying on a single sign-on authentication method that typically uses only id and password. The underlying study opted hardware security module by utilizing the Trusted Platform Module (TPM), which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers. The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.     

Emotion Recognition from Occluded Facial Images Using Deep EnsembleModel

Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications.     

CS‐EWMA Chart for Monitoring Process Dispersion

Control charts are the most extensively used technique to detect the presence of special cause variations in processes. They can be classified into memory and memoryless control charts. Cumulative sum and exponentially weighted moving average control charts are memory‐type control charts as their control structures are developed in such a way that the past information is not ignored as it is done in the case of memoryless control charts, like the Shewhart‐type control charts. The present study is based on the proposal of a new memory‐type control chart for process dispersion. This chart is named as CS‐EWMA chart as its plotting statistic is based on a cumulative sum of the exponentially weighted moving averages. Comparisons with other memory charts used to monitor the process dispersion are done by means of the average run length. An illustration of the proposed technique is done by applying the CS‐EWMA chart on a simulated dataset. Copyright © 2012 John Wiley & Sons, Ltd.     

Annealing effect on structural, optical and electrical properties of pure and Mg doped tin oxide thin films

This study describes the effect of annealing at different temperatures (400–600 °C) on structural, optical and electrical behaviors of pure and Mg doped tin oxide thin films grown on the glass substrate by electron beam evaporation technique. The transformation of tetragonal to orthorhombic form due to annealing, introduced a change in the optical and electrical properties of pure and Mg doped tin oxide thin films. X-ray diffraction studies or analysis revealed the phase transformation and change in the crystalline size with increase in the annealing temperature. The morphology and roughness of the thin films were studied by Atomic force microscopy. Optical band gap increased with annealing temperature confirms the improvements of crystallinity. The quality of thin films transparency was investigated by UV/Vis-spectroscopy. Photoluminescence of pure and Mg doped tin oxide thin films shows two extra peaks one at 486 nm and other at 538 nm is due to the crystal defect created as a result of annealing temperature. These peaks became stronger and shifted to longer wavelength with increasing the annealing temperature. The complex plot (Nyquist plot) showed the data point laying on two semicircles and the resistance of grains and grain boundaries increases with the increase in annealing temperature for both pure and Mg doped tin oxide thin films.     

On the Performance of Phase I Dispersion Control Charts for Process Monitoring

Control charts are usually implemented in two phases: the retrospective phase (phase I) and the monitoring phase (phase II). The performance of any phase II control chart structure depends on the preciseness of the control limits obtained from the phase I analysis. In statistical process control, the performance of phase I dispersion charts has mainly been investigated for normal or contaminated normal distributions of the quality characteristic of interest. Little work has been carried out to investigate the performance of a wide range of possible phase I dispersion charts for processes following non‐normal distributions. The current study deals with the proper choice of a control chart for the evaluation of process dispersion in phase I. We have analyzed the performance of a wide range of dispersion control charts, including two distribution‐free structures. The performance of the control charts is evaluated in terms of probability to signal, under normal and non‐normal process setups. These results will be useful for quality control practitioners in their selection of a phase I control chart. Copyright © 2014 John Wiley & Sons, Ltd.     

A residual a posteriori error estimate for partition of unity finite elements for three-dimensional transient heat diffusion problems using multiple global enrichment functions

In this article, a study of residual based a posteriori error estimation is presented for the partition of unity finite element method (PUFEM) for three-dimensional (3D) transient heat diffusion problems. The proposed error estimate is independent of the heuristically selected enrichment functions and provides a useful and reliable upper bound for the discretization errors of the PUFEM solutions. Numerical results show that the presented error estimate efficiently captures the effect of h-refinement and q-refinement on the performance of PUFEM solutions. It also efficiently reflects the effect of ill-conditioning of the stiffness matrix that is typically experienced in the partition of unity based finite element methods. For a problem with a known exact solution, the error estimate is shown to capture the same solution trends as obtained by the classical L₂ norm error. For problems with no known analytical solutions, the proposed estimate is shown to be used as a reliable and efficient tool to predict the numerical errors in the PUFEM solutions of 3D transient heat diffusion problems.     

Buckypapers of polyvinyl chloride/poly(styrene-co-maleic anhydride) blend intercalated graphene oxide-carbon nanotube nanobifiller: Physical property exploration

Novel buckypaper of polyvinyl chloride (PVC) and poly(styrene-co-maleic anhydride) (PSMA) intercalated with carbon nanotube (CNT) and graphene oxide-CNT (GO-CNT) nanobifiller was prepared using resin infiltration technique. Two series of buckypaper were prepared with varying CNT and GO-CNT contents. According to field emission scanning electron microscopy, PVC/SMA/CNT 0.1 showed porous morphology while PVC/PSMA/GO-CNT 0.1 revealed unique sort of island-nodule morphology. T_max of PVC/PSMA/GO-CNT 0.1 was 561°C while PVC/PSMA/CNT 0.1 depicted relatively lower value (552°C). T_g of PVC/PSMA/GO-CNT 0.1 was also increased to 294°C. Peak heat release rate of poly(methyl methacrylate-co-methacrylic acid)/polyamide 6/montmorillonite-modified GO was decreased from 322 to 209 kW/m² (65%) with 0.03–0.3 g GO-CNT loading.     

Bivariate Dispersion Control Charts for Monitoring Non‐Normal Processes

Multivariate control charts are well known to be more sensitive to the occurrence of variation in processes with two or more correlated quality variables than univariate charts. The use of separate univariate control charts to monitor multivariate process can be misleading as it ignores the correlation between the quality characteristics. The application of multivariate control charts allows for the simultaneous monitoring of the quality characteristics by forming a single chart. The charts operate on the assumption that process observations are normally distributed, but in practice this is not always the case. In this study, we examine and present multivariate dispersion control charts for detecting shifts in the covariance matrix of normal and non‐normal bivariate processes. These control charts, referred to as SMAX, QMAX, MDMAX and MADMAX, rely on dispersion estimates, such as the sample standard deviation (S), interquartile range (Q), average absolute deviation from median (MD) and median absolute deviation (MAD), respectively. We compare the performances of these charts to the existing multivariate generalized variance |S| and RMAX charts for bivariate processes using normal and non‐normal parent distributions. The average run length (ARL) measure is used for the evaluation and comparison of the charts. A real life and simulated datasets are used to demonstrate the application of the charts. Copyright © 2016 John Wiley & Sons, Ltd.     

Binary phase solid-state photopolymerization of acrylates: design,characterization and biomineralization of 3D scaffolds for tissue engineering

Porous polymer scaffolds designed by the cryogel method are attractive materials for a range of tissue engineering applications. However, the use of toxic crosslinker for retaining the pore structure limits their clinical applications. In this research, acrylates (HEA/PEGDA, HEMA/PEGDA and PEGDA) were used in the low-temperature solid-state photopolymerization to produce porous scaffolds with good structural retention. The morphology, pore diameter, mineral deposition and water absorption of the scaffold were characterized by SEM and water absorption test respectively. Elemental analysis and cytotoxicity of the biomineralized scaffold were revealed by using XRD and MTT assay test. The PEGDA-derived scaffold showed good water absorption ability and a higher degree of porosity with larger pore size compared to others. XRD patterns and IR results confirmed the formation of hydroxyapatite crystals from an alternative socking process. The overall cell proliferation was excellent, where PEGDA-derived scaffold had the highest and the most uniform cell growth, while HEMA/PEGDA scaffold showed the least. These results suggest that the cell proliferation and adhesion are directly proportional to the pore size, the shape and the porosity of scaffolds.