We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

/pdf/no-reference-image-quality-assessment-in-the-spatial-domain-2mnlvf25ig.pdf

No-Reference Image Quality Assessment in the Spatial Domain

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.

/pdf/making-a-completely-blind-image-quality-analyzer-4rsald8tn3.pdf

Making a “Completely Blind” Image Quality Analyzer

Over the past decade, there has been an explosion of interest in network research across the physical and social sciences. For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field. We hope to contribute to a dialogue among researchers from across the physical and social sciences who share a common interest in understanding the antecedents and consequences of network phenomena.

/pdf/network-analysis-in-the-social-sciences-440fzedv4c.pdf

Network Analysis in the Social Sciences

I. the origin of species by means of natural selection

This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops. We present experimental measurement results for three AI-aaS applications deployed on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders; 2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway segment classification in smart transportation.

Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure.

Traditional telecommunications service providers are undergoing a transition to a shared infrastructure in which multiple services will be offered to customers. These services will be introduced, modified, and retired at a pace that tracks changing requirements and demands. In order to be cost-effective, these services will need to be delivered over a shared infrastructure that is managed to support delivery requirements at a given point in time. In this paper, we present an Autonomic Service Architecture (ASA) for the automated management of networking and computing resources. ASA ensures the delivery of services according to specific agreements between customers and service providers.

http://dpnm.postech.ac.kr/papers/IPOM/05/IPOM2005-paper.pdf

Towards an autonomic service architecture

A new technique using fuzzy logic reasoning to recover the information loss in a block-based image coding system is developed in this paper. The proposed method, suitable for recovering both isolated and contiguous block losses, provides a new approach for error concealment of block-based image and video coding systems such as the JPEG, H.261, MPEG and HDTV standards. Simulation results are presented to illustrate the effectiveness of the proposed method. >

http://ieeexplore.ieee.org/iel2/1047/7666/00318223.pdf

Image and video reconstruction using fuzzy logic

Flow control is essential in ATM networks to prevent service quality degradation during periods of network congestion. In particular, best effort services such as data file transfer have stringent cell loss requirements. Use of backpressure in conjunction with buffer reservation is a promising approach. Buffer reservation on per-VC basis require large buffers. Besides, fairness and high network utilization cannot be supported at low buffer sizes. In this paper, we propose a new scheme based on buffer reservation for groups of VCs rather than for individual VCs. A general framework for credit-based link-by-link flow control is developed by disassociating buffer reser vation from credit allocation. Optirnality conditions for a credit allocation mechanism are found and an adaptive credit allocation algorithm is designed. The buffer requirement of this scheme is close to one round trip time worth of cells per group. Simulation results indicate that fairness is maintained in a robust manner.

A minimal-buffer loss-free flow control protocol for ATM networks

This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops.

Alberto Leon-Garcia

Papers

Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure.

Towards an autonomic service architecture

Image and video reconstruction using fuzzy logic

A minimal-buffer loss-free flow control protocol for ATM networks

Artificial Intelligence as a Service (AI-aaS) on Software-Defined Infrastructure