THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.

Next Generation 5G Wireless Networks: A Comprehensive Survey

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A Break in the Clouds: Towards a Cloud Definition

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

In cloud service provisioning scenarios with a changing demand from consumers, it is appealing for cloud providers to leverage only a limited amount of the virtualized resources required to provide the service. However, it is not easy to determine how much resources are required to satisfy consumers expectations in terms of Quality of Service (QoS). Some existing frameworks provide mechanisms to adapt the required cloud resources in the service delivery, also called an elastic service, but only for consumers with the same QoS expectations. The problem arises when the service provider must deal with several consumers, each demanding a different QoS for the service. In such an scenario, cloud resources provisioning must deal with trade-offs between different QoS, while fulfilling these QoS, within the same service deployment. In this paper we propose an elasticity-aware governance platform for cloud service delivery that reacts to the dynamic service load introduced by consumers demand. Such a reaction consists of provisioning the required amount of cloud resources to satisfy the different QoS that is offered to the consumers by means of several service level agreements. The proposed platform aims to keep under control the QoS experienced by multiple service consumers while maintaining a controlled cost.

An Elasticity-Aware Governance Platform for Cloud Service Delivery

More widespread adoption requires swarms of robots to be more flexible for real-world applications. Multiple challenges remain in complex scenarios where a large amount of data needs to be processed in real-time and high degrees of situational awareness are required. The options in this direction are limited in existing robotic swarms, mostly homogeneous robots with limited operational and reconfiguration flexibility. We address this by bringing elastic computing techniques and dynamic resource management from the edge-cloud computing domain to the swarm robotics domain. This enables the dynamic provisioning of collective capabilities in the swarm for different applications. Therefore, we transform a swarm into a distributed sensing and computing platform capable of complex data processing tasks, which can then be offered as a service. In particular, we discuss how this can be applied to adaptive resource management in a heterogeneous swarm of drones, and how we are implementing the dynamic deployment of distributed data processing algorithms. With an elastic drone swarm built on reconfigurable hardware and containerized services, it will be possible to raise the self-awareness, degree of intelligence, and level of autonomy of heterogeneous swarms of robots. We describe novel directions for collaborative perception, and new ways of interacting with a robotic swarm.

End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms

Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research challenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs $h^2$ CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of $h^2$ CAS that describes a $h^2$ CAS program. We analyze and present program features for $h^2$ CAS in four main different contexts.

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Context-Aware Programming for Hybrid and Diversity-Aware Collective Adaptive Systems

Distributed services in a context-aware Web service system or in a service composition need to couple context information from different sources in order to be able to interpret conte xt information. Such interpretation is a prerequisite before services can adapt themselves to be context-aware. However, context coupling in contextaware Web service systems is a complex issue. It is related not only to how we represent and transfer context information among Web services, but also to how we support the context storage, and how we ensure security and privacy issues of context information. Context coupling techniques for Web service systems will be different from that of tightly coupled systems because, with Web service systems, context information is typically shared across the boundaries of organizations which host Web services. This chapter aims at presenting an overview of context coupling techniques, their related issues, and their implication for a context-aware Web service system. Our approach is to study existing techniques implemented in current systems, to present open context coupling issues in current and future context-aware Web service systems, and to suggest further research directions.

http://www.infosys.tuwien.ac.at/Staff/sd/papers/Buchbeitrag%20Linh%20revised%20paper.pdf

Context Coupling Techniques for Context-aware Web Service Systems - An Overview

In variant-rich workflow-based systems, a major concern for process variability is the context-aware configuration of the variants. This means that context information, not users, drives process configuration. To support context-aware process configuration in a dynamic environment, in which context information is available only at run time, smart workflows must be customized at run time. The LateVa (Late Variability for Context-Aware Smart Workflows) framework lets developers model and manage process variability by composing base models, fragments, and variability models and by deferring binding to run time. Base models and fragments are reusable, thereby reducing the modeling effort for developing variants. LateVa also includes an automated run-time-variability mechanism for context-aware smart workflows.

Hong-Linh Truong

Papers

An Elasticity-Aware Governance Platform for Cloud Service Delivery

End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms

Context-Aware Programming for Hybrid and Diversity-Aware Collective Adaptive Systems

Context Coupling Techniques for Context-aware Web Service Systems - An Overview

Run-Time Variability for Context-Aware Smart Workflows