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

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

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

The variability scale in large-scale Cyber-Physical Systems (CPSs) is high and complex due to the voluminousness, dynamicity and diversity of available computing resources (people, things and software services), domain-specific processes, domain-specific elements (stakeholders, assets and contracts), and their relationships. This requires us to go beyond current variability modeling and management techniques which neglect the complexity and the diversity of relevant stakeholders, data and assets, and thus cannot cope with intelligent business and analytics requirements in dynamic environments, such as smart city management. In this paper, we present a comprehensive analysis for understanding the multi-perspective variability in processes atop people, data and things in CPSs, particularly, for the sustainability governance of Smart Green Buildings (SGBs). We examine domain-specific processes and domain-specific elements and their relationships to derive a multiple-perspective variability management for SGBs. On the basis of this, we conceptualize a novel model for the multi-perspective process variability representation.

https://www.infosys.tuwien.ac.at/Staff/sd/papers/SOCA%202013%20H.-L.%20Truong%20Multi-perspective.pdf

Multi-perspective Process Variability: A Case for Smart Green Buildings (Short Paper)

While several techniques have been introduced for specifying and acquiring context and quality information associated with Web services, they consider such information representing the whole Web services. However, accessing to context and quality of data resources provided by Web services is crucial. This is particularly relevant for data-intensive Web services of which the context and quality of data resources will strongly impact on the service development and composition. In this paper we contribute an analysis of relationships among context, quality, and relevance, as well as their impact on the design and composition of Web services, in particular at the data resource level. Then, we propose several techniques to incorporate context and quality descriptions into REST APIs and RESTful services publishing. By implementing these features, RESTful Web services could allow the consumer to specify and query context and quality information associated with services and data resources, thus fostering the provision of high relevant data resources.

/pdf/context-quality-and-relevance-dependencies-and-impacts-on-3t0ami9vaj.pdf

Context, quality and relevance: dependencies and impacts on RESTful web services design

A service provider defines individual services with corresponding service licenses which consumers should follow. Often, service consumers are interested in selecting a service based on certain licensing clauses. For a set of requested licensing clauses by a consumer, there can be several licenses that differ in the set of offered license specifications. Thus, a license aware service selection process includes the discovery of a set of services that meets certain functional parameters and, in addition, the process evaluates these services in order to identify the ones that fulfill a set of license specifications as requested by a consumer. In this paper, we present a methodology and framework for service selection process, based on matching the offered licensing specifications by providers with the requested licensing specifications by consumers.

/pdf/lass-license-aware-service-selection-methodology-and-521025saml.pdf

LASS --- License Aware Service Selection: Methodology and Framework

While existing work concentrates on developing QoS models of business workflows and Web services, few tools have been developed to support the monitoring and performance analysis of scientific workflows in Grids. This paper describes a Grid service for performance monitoring and analysis of Grid scientific workflows. The service utilizes workflow graphs and various types of performance data including monitoring data of resources, execution status of activities, and performance measurement obtained from the dynamic instrumentation, to provide a rich set of monitoring and performance analysis features. We store workflows and their relevant information, devise techniques to compare constructs of different workflows, and support multi-workflow analysis.

Online performance monitoring and analysis of grid scientific workflows

We discuss an approach to collaborative data management based on uncertain databases. Note that, in a collaborative data management system, users may have contradicting opinions about the correct values of data items. In our approach, we propose to store all conflicting data versions in parallel and to resolve conflicts based on user ratings. We show that such a collaborative data management system can be nicely represented in an uncertain database using U-relations.

Hong-Linh Truong

Papers

Multi-perspective Process Variability: A Case for Smart Green Buildings (Short Paper)

Context, quality and relevance: dependencies and impacts on RESTful web services design

LASS --- License Aware Service Selection: Methodology and Framework

Online performance monitoring and analysis of grid scientific workflows

Uncertain Databases in Collaborative Data Management.