Perceptual Quality Assessment of Digital Images Using Deep Features

Perceptual quality assessment is a tough task especially in the absence of reference information. No-reference image quality assessment is more challenging than full-reference or reduced reference methods, as the system has to model the different image distortions in the form of a quality score. Most of the approaches are based on handcrafted features which are based on natural scene statistics and are specific to some distortion types. These approaches provide high correlation with human opinion score for datasets containing specific distortions, but they fail to generalize well in scenarios were multiple distortions or real-time distortions are present in images. Deep learning algorithms, on the other hand, demonstrated their abilities to learn expert features with better discriminatory power for various classification and regression tasks. It is a big challenge to use those deep learning methods for image quality assessment as the image datasets with human opinion score are very small and cannot be used effectively to train a deep learning algorithm. We experimented with activations of different deep layers of thirteen pre-trained models and checked for their suitability for the task of no-reference quality assessment. Fine-tuning of these models on quality assessment datasets provided even better performance. A Gaussian process regression model is trained on these activations to perform the quality assessment and it provided state-of-the-art performance. Cross-dataset validation demonstrated its performance further and also provided further prospects of research in this direction

Inference dynamics in transcriptional regulation

Computational systems biology is an emerging area of research that focuses on understanding the holistic view of complex biological systems with the help of statistical, mathematical and computational techniques. The regulation of gene expression in gene regulatory network is a fundamental task performed by all known forms of life. In this subsystem, modelling the behaviour of the components and their interactions can provide useful biological insights. Statistical approaches for understanding biological phenomena such as gene regulation are proving to be useful for understanding the biological processes that are otherwise not comprehensible due to multitude of information and experimental difficulties. A combination of both the experimental and computational biology can potentially lead to system level understanding of biological systems. This thesis focuses on the problem of inferring the dynamics of gene regulation from the observed output of gene expression. Understanding of the dynamics of regulatory proteins in regulating the gene expression is a fundamental task in elucidating the hidden regulatory mechanisms. For this task, an initial fixed structure of the network is obtained using experimental biology techniques. Given this network structure, the proposed inference algorithms make use of the expression data to predict the latent dynamics of transcription factor proteins. The thesis starts with an introductory chapter that familiarises the reader with the physical entities in biological systems; then we present the basic framework for inference in transcriptional regulation and highlight the main features of our approach. Then we introduce the methods and techniques that we use for inference in biological networks in chapter 2; it sets the foundation for the remaining chapters of the thesis. Chapter 3 describes four well-known methods for inference in transcriptional regulation with pros and cons of each method. Main contributions of the thesis are presented in the following three chapters. Chapter 4 describes a model for inference in transcriptional regulation using state space models. We extend this method to cope with the expression data obtained from multiple independent experiments where time dynamics are not present. We believe that the time has arrived to package methods like these into customised software packages tailored for biologists for analysing the expression data. So, we developed an open-sources, platform independent implementation of this method (TFInfer) that can process expression measurements with biological replicates to predict the activities of proteins and their influence on gene expression in gene regulatory network. The proteins in the regulatory network are known to interact with one another in regulating the expression of their downstream target genes. To take this into account, we propose a novel method to infer combinatorial effect of the proteins on gene expression using a variant of factorial hidden Markov model. We describe the inference mechanism in combinatorial factorial hidden model (cFHMM) using an efficient variational Bayesian expectation maximisation algorithm. We study the performance of the proposed model using simulated data analysis and identify its limitation in different noise conditions; then we use three real expression datasets to find the extent of combinatorial transcriptional regulation present in these datasets. This constitutes chapter 5 of the thesis. In chapter 6, we focus on problem of inferring the groups of proteins that are under the influence of same external signals and thus have similar effects on their downstream targets. Main objectives for this work are two fold: firstly, identifying the clusters of proteins with similar dynamics indicate their role is specific biological mechanisms and therefore potentially useful for novel biological insights; secondly, clustering naturally leads to better estimation of the transition rates of activity profiles of the regulatory proteins. The method we propose uses Dirichlet process mixtures to cluster the latent activity profiles of regulatory proteins that are modelled as latent Markov chain of a factorial hidden Markov model; we refer to this method as DPM-FHMM. We extensively test our methods using simulated and real datasets and show that our model shows better results for inference in transcriptional regulation compared to a standard factorial hidden Markov model. In the last chapter, we present conclusions about the work presented in this thesis and propose future directions for extending this work

Visual Speaker Identification Using Lip and Body Movements

Speaker identification has been studied in many fields such as image processing, audio processing, artificial intelligence and speech recognition. Two of these areas are integrated together in order to identify the speaker. This research will focus on two main approaches which are lip movements and body movements. We will work on the two approaches to achieve the speaker identification. The expected outcome of this study will be to identify the speaker in different scenarios, if there is a single speaker or if there is multiple speakers in the video or if the speaker’s lips are not in view

To Innovate Wireless Security Protocols’ Improvement: Inventive Principles, Guidelines and Potential Recommendations

The research documents importance of widespread wireless networks. With increase in wireless networks usage, security concerns in wireless networks are becoming more serious. This article sheds light on existing deficiencies in techniques and protocols of wireless networks security. Considering the deficient factors and to seek possible future improvements, the research presented potential usage of Theory of Inventive Problem Solving (TRIZ) in field of wireless network security. It derives set of guidelines from TRIZ knowledgebase to provide conceptual thinking support for solution hunters in field of wireless network security. The proposed conception of deriving innovative guidelines from TRIZ knowledgebase provides a novel way to implement TRIZ in network security field

An Efficient and Simplified Model for Forecasting using SRM

Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory), SRM (Structural Risk Minimization )Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression) for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model buildin

Semantic Based Cluster Content Discovery in Description First Clustering Algorithm

In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm) is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing); an IR (Information Retrieval) technique for induction of meaningful labels for clusters and VSM (Vector Space Model) for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase

PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression

Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without loss of reasonable image quality. Same become more prominent in the case of videos which contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of quality of images in such scenarios has become of particular interest. Subjective evaluation in most of the scenarios is infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the image quality which is unfeasible in scenarios such as broadcasting, acquisition or enhancement. Therefore, a no-reference Perceptual Image Quality Index (PIQI) is proposed in this paper to assess the quality of digital images which calculates luminance and gradient statistics along with mean subtracted contrast normalized products in multiple scales and color spaces. These extracted features are provided to a stacked ensemble of Gaussian Process Regression (GPR) to perform the perceptual quality evaluation. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods and competitive results are achieved. The comparison is made based on RMSE, Pearson and Spearman correlation coefficients between ground truth and predicted quality scores. The scores of 0.0552, 0.9802 and 0.9776 are achieved respectively for these metrics on CSIQ database. Two cross-dataset evaluation experiments are performed to check the generalization of PIQI

Inference dynamics in transcriptional regulation

Computational systems biology is an emerging area of research that focuses on understanding the holistic view of complex biological systems with the help of statistical, mathematical and computational techniques. The regulation of gene expression in gene regulatory network is a fundamental task performed by all known forms of life. In this subsystem, modelling the behaviour of the components and their interactions can provide useful biological insights. Statistical approaches for understanding biological phenomena such as gene regulation are proving to be useful for understanding the biological processes that are otherwise not comprehensible due to multitude of information and experimental difficulties. A combination of both the experimental and computational biology can potentially lead to system level understanding of biological systems. This thesis focuses on the problem of inferring the dynamics of gene regulation from the observed output of gene expression. Understanding of the dynamics of regulatory proteins in regulating the gene expression is a fundamental task in elucidating the hidden regulatory mechanisms. For this task, an initial fixed structure of the network is obtained using experimental biology techniques. Given this network structure, the proposed inference algorithms make use of the expression data to predict the latent dynamics of transcription factor proteins. The thesis starts with an introductory chapter that familiarises the reader with the physical entities in biological systems; then we present the basic framework for inference in transcriptional regulation and highlight the main features of our approach. Then we introduce the methods and techniques that we use for inference in biological networks in chapter 2; it sets the foundation for the remaining chapters of the thesis. Chapter 3 describes four well-known methods for inference in transcriptional regulation with pros and cons of each method. Main contributions of the thesis are presented in the following three chapters. Chapter 4 describes a model for inference in transcriptional regulation using state space models. We extend this method to cope with the expression data obtained from multiple independent experiments where time dynamics are not present. We believe that the time has arrived to package methods like these into customised software packages tailored for biologists for analysing the expression data. So, we developed an open-sources, platform independent implementation of this method (TFInfer) that can process expression measurements with biological replicates to predict the activities of proteins and their influence on gene expression in gene regulatory network. The proteins in the regulatory network are known to interact with one another in regulating the expression of their downstream target genes. To take this into account, we propose a novel method to infer combinatorial effect of the proteins on gene expression using a variant of factorial hidden Markov model. We describe the inference mechanism in combinatorial factorial hidden model (cFHMM) using an efficient variational Bayesian expectation maximisation algorithm. We study the performance of the proposed model using simulated data analysis and identify its limitation in different noise conditions; then we use three real expression datasets to find the extent of combinatorial transcriptional regulation present in these datasets. This constitutes chapter 5 of the thesis. In chapter 6, we focus on problem of inferring the groups of proteins that are under the influence of same external signals and thus have similar effects on their downstream targets. Main objectives for this work are two fold: firstly, identifying the clusters of proteins with similar dynamics indicate their role is specific biological mechanisms and therefore potentially useful for novel biological insights; secondly, clustering naturally leads to better estimation of the transition rates of activity profiles of the regulatory proteins. The method we propose uses Dirichlet process mixtures to cluster the latent activity profiles of regulatory proteins that are modelled as latent Markov chain of a factorial hidden Markov model; we refer to this method as DPM-FHMM. We extensively test our methods using simulated and real datasets and show that our model shows better results for inference in transcriptional regulation compared to a standard factorial hidden Markov model. In the last chapter, we present conclusions about the work presented in this thesis and propose future directions for extending this work.EThOS - Electronic Theses Online ServiceGBUnited Kingdo