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

Developing and operating IoT cloud systems require novel features for deploying, controlling, monitoring and testing both IoT units and cloud services in an integrated environment spanning different infrastructures. In this paper, we demonstrate iCOMOT -- a novel toolset offering these features. Using iCOMOT we can perform various activities, such as dynamically reconfiguration of sensors, communication protocols, and cloud services in an elastic manner, suitable for testing and assuring quality of IoT cloud systems configurations. We will demonstrate our iCOMOT with a real-world predictive maintenance case study.

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iCOMOT -- A Toolset for Managing IoT Cloud Systems

Enabling and controlling elasticity of cloud computing applications is a challenging issue. Elasticity programming directives have been introduced to delegate elasticity control to infrastructures and to separate elasticity control from application logic. Since coordination models provide a general approach to manage interaction and elasticity control entails interactions among cloud infrastructure components, we present a coordination-based approach to elasticity control, supporting delegation and separation of concerns at design and run-time, paving the way towards coordination-aware elasticity.

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Coordination-Aware Elasticity

In the past decade, elastic computing has emerged as a popular solution approach for on-demand computing resources' provisioning Today, such dynamic resource provisioning is used not only in machine-based computing systems but also in mixed systems containing machine- and human-based computing elements Many efforts have been undertaken to achieve resource elasticity However, to provide a truly elastic computation, system designers also need to foresee trade-offs between cost, quality, and resources Unfortunately, the lack of modeling tools leads to difficulty in designing on-demand provisioning strategy in mixed systems In this paper we present a modeling tool named Elasticity Profile (EP) that specifies constructs for modeling the elastic behavior of mixed systems with respect to trade-offs between cost, quality, and resources We also present a conceptual framework where our EP can be deployed, utilized, and bound to runtime systems Furthermore, we demonstrate the suitability of EP model using several use cases

Modeling Elasticity Trade-Offs in Adaptive Mixed Systems

Recently, cloud computing technologies have been employed for large-scale machine-to-machine (M2M) systems, as they could potentially offer better solutions for managing monitoring data of IoTs (Internet of Things) and supporting rich sets of IoT analytics applications for different stakeholders. However, there exist complex relationships between monitored objects, monitoring data, analytics features, and stakeholders that require us to develop efficient ways to handle these complex relationships to support different business and data analytics processes in large-scale M2M systems. In this chapter, we analyze potential stakeholders and their complex relationships to data and analytics applications in M2M systems for sustainability governance. Based on that we present techniques for supporting M2M data and process integration, including linking and managing monitored objects, sustainability monitoring data and analytics applications, for different stakeholders who are interested in dealing with large-scale monitoring data in M2M environments. We present a cloud-based data analytics system for sustainability governance that includes a Platform-as-a-Service and an analytics framework. We also illustrate our prototype based on a real-world cloud system for facility monitoring and analytics.

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Sustainability Data and Analytics in Cloud-Based M2M Systems

The distribution of services spanning across organizational boundaries raises problems related to intellectual value that are less explored in current service oriented research. Being a way to manage the intellectual rights between service consumers and service providers, licenses are critical to be considered in services. A service license describes the terms and conditions for the use and access of the service in a machine interpretable way. The proliferation of services that offer similar functionalities requires the selection process to be enhanced together with consideration of licenses. In this paper, we analyze current service licensing practices and present a novel approach for service selection process based on matching the offered licensing specifications (primarily dealing with scope of rights and financial terms) by providers with the requested licensing specifications by consumers.

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Hong-Linh Truong

Papers

iCOMOT -- A Toolset for Managing IoT Cloud Systems

Coordination-Aware Elasticity

Modeling Elasticity Trade-Offs in Adaptive Mixed Systems

Sustainability Data and Analytics in Cloud-Based M2M Systems

License-Aware Service Selection