Dynamic selection of a video content adaptation strategy from a Pareto front

This article is available open access through the publisher’s website through the link below. Copyright @ 2008 The Authors.Genetic Algorithms may be used together with Pareto Optimality in the process of selection of a suitable video content adaptation strategy, the former to return best or fittest solutions that have evolved over many generations and the latter to evaluate and rank each generation's solutions against a set of objectives without the need to assign weights to each one. The outcome of this is a Pareto front of optimal strategies, all of which would satisfy the objectives. The distribution of optimal strategies on a Pareto front, however, suggests that there may be a ‘best-fit’ optimal strategy. This article refines the process of selection of an optimal strategy by taking into account this distribution alongside user preferences, video content characteristics and usage history. In order to make the refined process dynamic, it pursues its implementation using Self-Organising Neural Networks

STATISTICAL MACHINE LEARNING BASED MODELING FRAMEWORK FOR DESIGN SPACE EXPLORATION AND RUN-TIME CROSS-STACK ENERGY OPTIMIZATION FOR MANY-CORE PROCESSORS

The complexity of many-core processors continues to grow as a larger number of heterogeneous cores are integrated on a single chip. Such systems-on-chip contains computing structures ranging from complex out-of-order cores, simple in-order cores, digital signal processors (DSPs), graphic processing units (GPUs), application specific processors, hardware accelerators, I/O subsystems, network-on-chip interconnects, and large caches arranged in complex hierarchies. While the industry focus is on putting higher number of cores on a single chip, the key challenge is to optimally architect these many-core processors such that performance, energy and area constraints are satisfied. The traditional approach to processor design through extensive cycle accurate simulations are ill-suited for designing many-core processors due to the large microarchitecture design space that must be explored. Additionally it is hard to optimize such complex processors and the applications that run on them statically at design time such that performance and energy constraints are met under dynamically changing operating conditions. The dissertation establishes statistical machine learning based modeling framework that enables the efficient design and operation of many-core processors that meets performance, energy and area constraints. We apply the proposed framework to rapidly design the microarchitecture of a many-core processor for multimedia, computer graphics rendering, finance, and data mining applications derived from the Parsec benchmark. We further demonstrate the application of the framework in the joint run-time adaptation of both the application and microarchitecture such that energy availability constraints are met

Energy-efficient bandwidth allocation for multiuser scalable video streaming over WLAN

We consider the problem of packet scheduling for the transmission of multiple video streams over a wireless local area network (WLAN). A cross-layer optimization framework is proposed to minimize the wireless transceiver energy consumption while meeting the user required visual quality constraints. The framework relies on the IEEE 802.11 standard and on the embedded bitstream structure of the scalable video coding scheme. It integrates an application-level video quality metric as QoS constraint (instead of a communication layer quality metric) with energy consumption optimization through link layer scaling and sleeping. Both energy minimization and min-max energy optimization strategies are discussed. Simulation results demonstrate significant energy gains compared to the state-of-the-art approaches

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Innovative food recommendation systems: a machine learning approach

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecommendation systems employ users history data records to predict their preference, and have been widely used in diverse fields including biology, e-commerce, and healthcare. Traditional recommendation techniques include content-based, collaborative-based and hybrid methods but not all real-world problems can be best addressed by these classical recommendation techniques. Food recommendation is one such challenging problem where there is an urgent need to use novel recommendation systems in assisting people to select healthy, balanced and personalized food plans. In this thesis, we make several advances in food recommendation systems using innovative machine learning methods. First, a novel recommendation approach is proposed by transforming an original recommendation problem into a many-objective optimisation one that contains several different objectives resulting in more balanced recommendations. Second, a unified approach to designing sequence-based personalised food recommendation systems is investigated to accommodate dynamic user behaviours. Third, a new food recommendation approach is developed with a temporal dependent graph neural network and data augmentation techniques leading to more accurate and robust recommendations. The experimental results show that these proposed approaches have not only provided a more balanced and accurate way of recommending food than the traditional methods but also led to promising areas for future research

Multi-Quality Auto-Tuning by Contract Negotiation

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques